Reviewerly/exHarmony huggingface dataset:
Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection

In this paper, we propose a multi-source domain adaptation method for mitotic cell detection. Our method is two-step approach. The first step is extraction of candidate regions of mitosis, and the second step is classification of the candidate regions to mitosis or non-mitosis. In the second step, we train a deep neural network model that has two classification tasks, namely mitosis/non-mitosis classification and domain classification. The branch for the domain classification has Gradient Reversal Layer for the domain adaptation. Our method does not use all images in the source domain, but uses the selected images in the domain adaptation phase to reduce the storage size of the source domain data.
Application of Computer Technology in the Teaching of Chinese Language and Literature Internet Environment

With the progress and innovation of science and technology, information technology has become an important means of teaching reform and development. As a basic course in colleges and universities, the innovation of teaching mode and means has become an important channel for the cultivation of Chinese language and literature professionals. The application of information technology can make the teaching of Chinese language and literature more stereoscopic and vivid, not only improve the students' enthusiasm for learning, but also improve the teaching efficiency to a great extent and improve the teaching effect obviously. Therefore, this paper deeply analyzes the practical difficulties of information technology in the teaching of Chinese language and literature, and explores the concrete application methods of information technology in Chinese language and literature combined with the subject requirements of Chinese language and literature and the needs of talent training.
Robust Student's t-based trajectory tracking for inspection wall-climbing robot

To address the problem of measurement noise interference in the trajectory tracking process of a wall-climbing robot (WCR), a robust Student's t-based extended Kalman filter (STEKF) is proposed. The measurement noise of the extended Kalman filter (EKF) is modeled with a Student's t distribution instead of a Gaussian distribution. Three kinds of filters are selected for comparison. Simulation results show that STEKF algorithm is more robust with accuracy than existing state-of-the-art filters.
A flexible approach for transforming variability models

In software product lines, engineers use variability models to explicitly represent commonalities and variability. A plethora of variability modeling approaches have been proposed in the last 30 years, and there is no standard variability modeling approach the community agrees on. Well-known approaches such as feature modeling or decision modeling exist in many different variants, most of which have been shown to be useful for at least one specific use case. Due to this variety of approaches researchers and practitioners alike struggle to find, understand, and eventually pick the right approach for a specific context or (set of) system(s). Practitioners in industry often develop custom solutions to manage the variability of various artifacts, like requirements documents or design spreadsheets. In this paper, we report on our ongoing research towards developing a framework for (semi-)automatically transforming variability models. Our approach supports researchers and practitioners experimenting with and comparing different variability modeling approaches and switching from one modeling approach to another. We present the research questions guiding our research and discuss the current status of our work as well as future work.
Bug Classifying and Assigning System (BCAS): An Automated Framework to Classify and Assign Bugs

Classifying a bug and assigning it to skilled and proficient developer is a critical task of bug triaging process. Identifying the type of bug and its severity is among the most vital features of the reported bug report that is required to be fixed rapidly. Manually classifying and identifying the bugs based on the feature is time consuming and tedious process. To automate this task, we propose bug classifying and assigning system-BCAS, which aims to improve the accuracy and assigning process and will provide support to the person reporting the bug assigned to him. To gain the best accuracy, this approach also considers and compares three well-known classification algorithms namely support vector machine, logistic, and Naïve Bayes for classifying bug reports on the basis of severity and component. The proposed work is trained and tested using three open-source software bug dataset from standard Bugzilla repository. Among these classification algorithms, SVM provides best accuracy of 91.6, 80.5, and 81.6% for Eclipse, Firefox, and Mozilla Core projects.
Object Tracking Algorithm Based on Margin Feature Fusion

In the target tracking algorithm, extracting features from images is a vital link. By extracting the features of the tracking target and the surrounding area samples, the higher the similarity is, the greater the correlation of the image is, so as to distinguish the target from the background, and then determine the location of the tracked target. How to extract the effective feature information from the image directly affects the detection effect of the algorithm. Most target tracking algorithms adopt HOG feature and grayscale feature to extract target shape feature. When the target has local occlusion, illumination change, the target deformation, these characteristics of the target expression is not substantial, easy to appear tracker drift, thus reducing the accuracy of target tracking, and even lead to the occurrence of tracking failure. In order to solve this problem, this paper proposes a target tracking algorithm that integrates edge features, fuses edge features with richer textures and HOG features of the performance space, so as to improve the performance of the extracted features. In the case of target deformation, local occlusion and illumination change, the image features can also be extracted effectively, the model can be updated, and the target can be tracked. Experimental results show that the target tracking algorithm based on edge features is more stable than some traditional correlation filtering algorithms.
Public Perception of Factors that Influence Employee Motivation

motivation is the act or process of providing a motive that causes a person to take some action. Many researchers have analyzed the need for  Employee motivation in a company and gave many motivational  theories . Employees talent should be motivated within a company and provide them advancement for their better future. The aim of this study is to analyse the various perception on motivational factors and find a better factor that would benefit the employee personally and societally. Descriptive research is used to portray accurately the characteristics of an individual. Convenient sampling method is used in the study to collect the samples. 877 samples sizes are recorded. Independent variables are age, gender, educational qualification. Dependent variables are motivating factor, employees, health benefits, policies and incentives. Statistical tools used are Independent sample t test and chi square.
Hybrid Adaptive Path Planning for a USV under Complex Marine Environment

In this paper, a hybrid adaptive path planning (HAPP) scheme based on global path planning and local dynamic collision avoidance for unmanned surface vehicles (USVs) under complex marine environment is proposed. The salient features of the proposed scheme are: (1) In order to ensure that the USV can navigate safely, an improved obstacle swelling binary (IOSB) map is established. (2) In order to generate effective sparse waypoints in the complex marine environment, a novel global path planning algorithm, called IOSB adaptive Theta* (IOSB- AT*) is developed, and the waypoints are converted into a smooth path suitable for USVs after employing the B-spline fitting algorithm. (3) In order for the USV to cope with real marine environment containing dynamic obstacles, an improved dynamic window approach is proposed. Simulation and comparative studies demonstrate the safety, effectiveness and real-time performance of the proposed hybrid path planning scheme under complex marine environment.
Using Machine Learning for Risk Classification in Brazilian Federal Voluntary Transfers

Along with the re-democratization process in Brazil, states and municipalities have started to rely on voluntary transfers of resources through agreements with the Federal Government to execute their public policies. To improve timeliness in the recovery process of default resource usage, a classification tool is necessary to assign risk profiles of the success or failure of these transfers. In this paperwork, we propose the use of the eXtreme Gradient Boosting (XGBoost) algorithm using balanced and unbalanced data sets, using Tree-structured Parzen Bayesian Estimator (TPE) hyperparameter optimization techniques. The results achieved good success rates. Results for XGBoost using balanced data showed a recall of 89.3% and unbalanced data 87.8%. However, for unbalanced data, the AUC score was 98.1%, against 97.9% for balanced data.
Image Summarizer for the Visually Impaired Using Deep Learning

One of the leading problems for Humanity, particularly in this era where information and people are interconnected with text messages, are visual impairment, or have a learning disability. For this problem we aim to provide a solution that summarizes any input image into meaningful data i.e. we analyse the image data and identify the objects in it and thereby generating vocabulary and generate meaningful sentences accordingly. This can be achieved by the use of multilayer Convolutional Neural Network (CNN) to generate vocabulary describing the images, this includes living as well as non-living things. For this we used ResNet50. And a Long Short Term Memory to construct meaningful sentences using the generated keywords i.e. LSTM will use the information from CNN to help generate a description of the image and we call this, The Image Summarizer. We use the gTTS library to provide the audio output. The audio will be based on the user’s preferred language.
A Motion Comic Creation with Monochromatic Background Extraction from a Comic Image

We propose a method for creating a motion comic with monochromatic background extraction from a comic image. The targets of the background are white, monochromatic screen tone, and solid black, and are extracted with an image processing. The extracted background can be applied to an animation such as translation, expansion, and dissolving. The background extraction method consists of a saliency map method, speech balloon extraction, and posterization with a pixel value of an input image. In our experimental evaluation with four comics, an accuracy of correction extraction is 49.2 %. The animation was manually generated with the background extraction, and a possibility for an attractive content is confirmed.
Behavior anomaly detection based on big data analysis of Internet of Things

Abstract The technical requirements of behavior anomaly detection are higher and higher. Using the Internet of things technology combined with a variety of big data analysis algorithms, we can achieve accurate behavior anomaly detection by classifying behavior data sets to a large extent. In this paper, PLA - PRF (parallel random forest) algorithm is used to realize the behavior anomaly detection model of Internet of things integrating big data analysis. In behavior detection, the PRF algorithm and DFS algorithm are compared in the case of a different number of decision trees. The results show that, compared with DRF algorithm, PLA-PRF, SPARK MLRF(Spark Machine Learning Random Forests) and PRF algorithm perform better on the four datasets, with kappa values increased by about 3.13%, 2.56% and 1.98% respectively. In contrast, PLA-PRF algorithm has higher accuracy in the case of a small sample size. With the increase of sample size, the accuracy of behavior anomaly detection gradually decreases; because the algorithm is in subspace in the process of construction, some high pheromone features are abandoned, which makes the new spatial information of features insufficient, resulting in the decision tree training process does not learn the inherent laws of abandoned data. Compared with spark MLRF and DRF, PLA-PRF has a faster execution speed in large data sets, and with the increase of data volume, the advantage is more prominent. This is because PLA-PRF uses data reuse strategy "DRS" in the process of parallelization, which reduces the data communication overhead in a distributed environment and improves the parallelization efficiency of the algorithm.
Object Detection Techniques: A State-of-the-Art Survey and Challenges

Object detection is a field of tremendous research as it has applications in a wide range of areas. Various machine learning and deep learning methods have been developed to date with deep learning leading the edge in the field of computer vision and object detection. Of the methods used to date, deep learning methods have proved to be very efficient in producing the most optimized outcome. The You Only Look Once (YOLO) method is a deep learning method to detect objects with the advantage of scanning through the entire image only once as against the other methods that scan the image multiple times thus producing efficient results in minimal time. We discuss various state-of-the-art methods used for object detection along with the advantages and disadvantages of each of them.
Lane Keeping Algorithm for Autonomous Driving via Safe Reinforcement Learning

Autonomous driving can possibly facilitate the load and change the methods for transportation in our everyday life. Work is being done to create algorithms of decision making and motion control in Autonomous driving. Recently, reinforcement learning has been a predominant strategy applied for this purpose. But, problems of using reinforcement learning for autonomous driving is that the actions taken while exploration can be unsafe, and the convergence can be too slow. Therefore, before making an actual vehicle learn driving through reinforcement learning, there is an urgent need to solve the safety issue. The significance of this paper is that, it introduces Safe Reinforcement Learning (SRL) into the field of autonomous driving. Safe reinforcement learning is the method of adding constraints to ensure the safe exploration. This paper explores the Constrained Policy Optimization (CPO) algorithm. The principle is to introduce constraints in the cost function. CPO is based on the framework of the Actor-Critic algorithm where the space that is explored during the policy update process is enforced by setting tough constraints which reduces the size of policy update. A comparison is also made with typical reinforcement learning algorithms to prove its advantages in learning efficiency and safety.
Design and Implementation of Mobile Learning Platform for Undergraduate Teaching -Taking Probability Theory and Mathematical Statistics Course for Example

Taking the online teaching of probability theory and mathematical statistics course as example, this paper systematically analyzes the development background and process of the network teaching platform based on Android. Firstly, this paper introduces the software development environment and system function requirements, and provides the core technology and backbone architecture of the system. Then, it introduces the overall design architecture of the software, system function module design, database design, as well as the software interface function and implementation process. The main technical realization includes the development of Android system application software, the development and application of Ajax, OAuth authentication, SQLite application, etc. Finally, through the design and test of the interactive communication platform based on Android system, the implementation of the mobile learning platform is explained and tested in technical details. The test results show that the prototype design of the mobile learning platform achieves the expected goals.
Enabling Geospatial Context in an IoT Decentralised Reputation Management System using Ethereum Smart Contracts

Social Internet of Things (SIoT) is a concept that integrates the Internet of Things and human social networks. An SIoT system has to store and manage device reputation values, which are used by end devices to determine the trustworthiness of another one. This device trustworthiness can also be affected by its geographical location. In this work, we introduced an architecture that includes the geospatial context in the part concerned with reputation management. The proposed architecture is based on the cloud-fog-edge architecture and uses the fog layer as the management system. The devices in the fog layer form an Ethereum Blockchain network and store the Smart Contracts. These in turn allow the management functionalities to be carried out in a decentralised, transparent and secure way, which are the advantages of Blockchain. To enable the characteristics with a geospatial component, it is necessary to apply a geocoding technique. This work shows how geocoding techniques can be adapted to cover the main geospatial functionalities and compares two geocoding options (Geohash or S2). The results showed that it is possible to include the geospatial context in a decentralised reputation management system by using hierarchical geocoding techniques, and the experiments showed that both Geohash and S2 can offer a similar performance in the proposed architecture.
MonoMR: Synthesizing Pseudo-2.5D Mixed Reality Content from Monocular Videos

MonoMR is a system that synthesizes pseudo-2.5D content from monocular videos for mixed reality (MR) head-mounted displays (HMDs). Unlike conventional systems that require multiple cameras, the MonoMR system can be used by casual end-users to generate MR content from a single camera only. In order to synthesize the content, the system detects people in the video sequence via a deep neural network, and then the detected person’s pseudo-3D position is estimated by our proposed novel algorithm through a homography matrix. Finally, the person’s texture is extracted using a background subtraction algorithm and is placed on an estimated 3D position. The synthesized content can be played in MR HMD, and users can freely change their viewpoint and the content’s position. In order to evaluate the efficiency and interactive potential of MonoMR, we conducted performance evaluations and a user study with 12 participants. Moreover, we demonstrated the feasibility and usability of the MonoMR system to generate pseudo-2.5D content using three example application scenarios.
Transformation from System model to FACE data model based on metadata mapping

Aiming at the problems of long development cycle, poor reusability and portability in avionics software development, this paper proposes a development process based on MBSE(Model-based systems engineering) and FACE(future airborne capability environment) architecture. At the same time, the differences between SysML model and FACE data model lead to the incoherence of development process. Therefore, the mapping transformation method from SysML model to FACE data model is designed based on metadata model mapping Law.
Visibility Prediction Based on Landmark Detection in Foggy Weather

Visibility prediction has important application value in transportation, aviation, military, agriculture and other fields. Traditional visibility monitoring instruments have the disadvantages of complex operation, expensive price and low accuracy. At the same time, the existing visibility prediction methods based on video images also have the disadvantages of low accuracy. In this paper, a visibility prediction method in foggy weather based on deep learning is proposed. The proposed method uses target detection network YOLOv5 (YOU ONLY LOOK ONCE V5) to detect the ground landmarks in the video, then establishes a camera imaging model to calculate distance of the ground landmarks, and finally obtains the visibility value according to the distance of the farthest landmark that can be detected. In the proposed method, CIOU _Loss is selected as the loss function of YOLOv5 to improve the convergence speed and prediction accuracy. The experimental results show that thanks to yolov5's powerful and fast detection capabilities, after the training of 200 epoches, the proposed method can detect landmarks in foggy images with a 100% recall rate, which has the advantages of low cost and high accuracy.
A Cross-platform Interface for Automatic Speaker Identification and Verification

The task of automatically identifying and/or verifying the identity of a speaker from a recording of a speech sample, known as automatic speaker recognition, has been studied for many years and automatic speaker recognition technologies have improved recently and becoming inexpensive and reliable methods for identifying and verifying people. Although automatic speaker recognition research has now spanned over 50 years, there is not adequate research done with regards to low-resourced South African indigenous languages. In this paper, a multi-layer perceptron (MLP) classifier model is trained and deployed on a graphical user interface for real time identification and verification of Sepedi native speakers. Sepedi is a low-resourced language spoken by the majority of residents in the Limpopo province of South Africa. The data used to train the speaker recognition system is obtained from the NCHLT (National Centre for Human Language Technology) project. A total of 34 short-term acoustic features of speech are extracted with the use of py Audio Analysis library and Sklearn is used to train the MLP classifier model which performs well with an accuracy of 95%. The GUI is developed with QT Creator and PyQT4 and it has obtained a true acceptance rate (TAR) of 66.67% and a true rejection rate of (TRR) 13.33%.
Mathematical literacy as an important component of preparing a future teacher

The article analyzes the concept of mathematical literacy. The formation of mathematical literacy among members of modern society is a fundamental basis for the formation of functional literacy. Currently, there is no single approach to the interpretation of the concept of "mathematical literacy", the article provides several different approaches to the definition. Based on the analysis of the points of view of different scientists, the authors of the article highlight the main components of the concept of "mathematical literacy". These include: literacy of mathematical speech; possession of mathematical concepts; the ability to correctly formulate definitions and statements; the ability to apply mathematics to research. The paper provides a characteristic of the listed components of mathematical literacy. In addition, in this work, for the first time, an attempt was made to formulate the levels of mathematical literacy – speech, terminological, critical and operational. The article presents the results of testing students in pedagogic areas of training of two universities – Kostroma State University and Yaroslavl State Pedagogic University. The test was aimed at assessing the level of mathematical literacy of future teachers, for whom teaching mathematics will be a part of their professional activities. The test tested not only the knowledge of terms and the ability to give definitions, but also the ability to correctly apply mathematics to solve practical problems with subsequent interpretation, as well as the ability to see errors and inaccuracies in the texts of students' works, Internet sources and teaching aids. The authors identified criteria for assessing the test results and for each criterion; a quantitative indicator was determined equal to the number of correctly completed tasks. The low results of even third-year students show topicality of specific activities that could help improve the mathematical literacy of pedagogues.