Reviewerly/exHarmony huggingface dataset:
A Study on the Expansion of the Following Area in Target Following Using Ultrasonic Sensors with Separated Transceivers

This study proposes a method that considers the expansion of the following area in target following using ultrasonic sensors. The target following method using ultrasonic sensors calculates the distance between the target and the robot based on two distance data. However, the ultrasonic sensors are faced in a state of inability to follow when targets make rapid movements due to the limitations of the measurement range. To improve this problem, the existing research method rotates the ultrasonic sensors for researching the following target. However, since the existing method rotates two sensors with the same angle, the searching process is rather cumbersome because they search the target while increasing the rotation angle if there is no target within the range of rotation angles. In this paper, therefore, the rotation angle of the ultrasonic sensors located on the outside is set larger than that of the ultrasonic sensors located on the inside, centering on the rotation radius of the following robot for expanding the following area of the same rotation angle. In addition, the search for different angular ranges in distance detection sensors has been designed with a single link structure to miniaturize the search module.
Generalization in Reinforcement Learning with Selective Noise Injection and Information

The ability for policies to generalize to new environments is key to the broadapplication of RL agents. A promising approach to prevent an agent’s policyfrom overfitting to a limited set of training environments is to apply regularizationtechniques originally developed for supervised learning. However, there are starkdifferences between supervised learning and RL. We discuss those differencesand propose modifications to existing regularization techniques in order to betteradapt them to RL. In particular, we focus on regularization techniques relying onthe injection of noise into the learned function, a family that includes some ofthe most widely used approaches such as Dropout and Batch Normalization. Toadapt them to RL, we proposeSelective Noise Injection(SNI), which maintainsthe regularizing effect the injected noise has, while mitigating the adverse effectsit has on the gradient quality. Furthermore, we demonstrate that the InformationBottleneck (IB) is a particularly well suited regularization technique for RL asit is effective in the low-data regime encountered early on in training RL agents.Combining the IB with SNI, we significantly outperform current state of the artresults, including on the recently proposed generalization benchmarkCoinrun.
Algorithms for Automatic Identification and Analysis of Sri Lankan Anopheles Mosquito Species

Microscopic digital image processing algorithms are presented here to automatically detect primary morphological features of Sri Lankan anopheline mosquitoes, as an essential step towards the development of automated identification and analysis of various species of anopheline mosquitoes. Mosquitoes that belong to genus Anopheles spread the causative pathogen of malaria. Perfect and speedy species identification is crucial in any surveillance and control strategies. Currently, morphological taxonomic keys are used to identify various species. Two or more primary morphological characteristics, such as a number of dark spots of wings and pale bands of legs, are used in each step of the hierarchical key. To achieve the automatic detection of the primary morphological features, image processing algorithms performed at three levels. At the pre-processing level, methods work with raw, possibly noisy pixel values, with noise reduction and smoothing. In the mid-level, algorithms are utilized pre-processing results for further means with background removing and spots/bands segmentation. At the final level, techniques try to extract the semantics of spots/bands and counting the spots/bands from the information provided. Thirty samples of anopheline mosquitoes' wings and legs microscopic images were analysed with satisfactory results.
Ultrasound Breast Lesion Detection using Extracted Attention Maps from a Weakly Supervised Convolutional Neural Network

In order to detect lesions on medical images, deep learning models commonly require information about the size of the lesion, either through a bounding box or through the pixel-/voxel-wise annotation of the lesion, which is in turn extremely expensive to produce in most cases. In this paper, we aim at demonstrating that by having a single central point per lesion as ground truth for 3D ultrasounds, accurate deep learning models for lesion detection can be trained, leading to precise visualizations using Grad-CAM. From a set of breast ultrasound volumes, healthy and diseased patches were used to train a deep convolutional neural network. On the one hand, each diseased patch contained in its central area a lesion's center annotated by experts. On the other hand, healthy patches were extracted from random regions of ultrasounds taken from healthy patients. An AUC of 0.92 and an accuracy of 0.87 was achieved on test patches.
Research on Robotic Measurement Path Planning for Complex Surface Workpieces

Abstract Combined with measurement efficiency, measurement accuracy and no interference in the measurement process, this paper proposes a path planning method for a measuring robot for complex surfaces such as blisk. The position planning of robot measurement path is realized based on the surface chord height difference, and the attitude planning of robot measurement path is realized based on the single measured point measurement attitude space(SMPMAS). Finally, the effectiveness of the proposed method is verified by experiments.
Mathematical Framework of Compactly Supported Orthogonal Wavelets

After the wavelet methodWavelet methods for processingOrthogonal wavelets theMathematical framework jumping signal was proposed in the 1970s, its powerful advantages attract the attention of mathematicians, and the mathematical frameworkMathematical framework of a wavelet has been gradually established and various wavelets with different anticipatable properties are constructed since 1980s.
Detection of Singularity in the Cell Nucleus of Synovial Sarcoma Using Wavelet Leaders

Pathological examination is important for an accurate diagnosis of Synovial Sarcoma (SS). It is the most common cancer of the soft tissues of the limb in adolescents and adults. In this work, SS was used to determine the discriminant singularity characteristics using Wavelet Leaders (WL). The most popular technique for measuring the discriminant singularity characteristics of an image signal is the Lipschitz Exponent (LE). The singularity measurement was based on LE function by taking a slope of logarithmic scale versus logarithmic Wavelet Transform Modulus Maxima (WTMM). Here, the presence of the singularity was measured using WTMM and WL by summing each color component of an image signal. The performance characteristics of the statistical discrimination are evaluated and compared with the non-parametric hypothesis using the Wilcoxon rank-sum test. The most important difference between WTMM and WL was analyzed using the Receiver Operating Characteristics (ROC) curve. From the experimental analysis that the WL method provides excellent discriminant singularities or discontinuities performance characteristics such as area, standard error, z-statistics, and p-values. Finally, the results of experiments have proven that a WL can express practical, precise, robust, and satisfactory performance in practice.
Text Mining Analysis on Users’ Reviews for News Aggregator Toutiao

Abstract This paper intent to improve consumer communication by text mining analysis with users’ reviews. The news aggregator we focus on is: Toutiao, known as “today’s headlines” in Chinese. It is the top news aggregator application run by Bytedance company in China. It utilizes AI algorithms to provide numerous news feed for its users. As new technologies are shaping the business strategy studies as well as online communication analysis, it requires innovative and effective analyses of unconventional data, such as the 12,290 online reviews on Toutiao we collected from Apple’s App Store. Through the LDA topic modelling and sentiment analysis, our research has identified three major negative complains the consumers have regarding Toutiao application, namely: too many unsolicited advertisements, contents (vulgar content, time consuming video, privacy and copyrights infringement issue) and incompatibility with the latest Apple digital devices.
Evaluating Model Fit for a Time Series Solar Irradiance and DC Power Data to Forecast Accuracy Measures

Solar-powered photovoltaic system provides an unpolluted energy solution to current global warming. The solar input parameters and weather data are important to configure the desired power output. Installation at site, system degradation, and lifetime reliability analysis are crucial for long-term power optimization. Statistical analysis of solar data is crucial to forecast the behavioral trend of photovoltaic system. Mean absolute percentage error predicts accuracy for forecasting the trend in statistics which is widely used to due to scale independency and interpretability. Different models are considered to fit the actual curve and to reduce the mean absolute percentage error, mean square deviation, and mean absolute deviation.
Modified Data Storage and Replication Mechanism with Frequent Use-Case Based Indexing

The real time applications demands high speed and reliable data access from the remote database. An effective logical data management strategy that handles simultaneous connections with better performance negotiation is inevitable. This work considers an e-health care application that proposes MongoDB based modified indexing and performance tuning methods. To cope with certain high frequency use case and its performance mandates, a flexible and efficient logical data management may be preferred. By analysing the data dependency, data decomposition concerns and the performance requirements of the specific use case of the medical application, a logical schema may be customized on an ala-carte basis. This work focused on the flexible logical data modeling schemes and its performance factors of the NoSql DB. The efficiency of unstructured data base management in storing and retrieving the e-health care data was analysed with a web based tool. To enable faster data retrieval and query processing over the distributed nodes, a Spark based storage engine was built on top of the MongoDB based data storage management. With Spark tool, the database has been made distributed as master–slave structures with suitable data replication mechanisms. In such distributed database the fail-over also implemented with the suitable replication mechanism. This work considered MongoDB based flexible schema modeling and Spark based distributed computation with multiple chunks of data. The flexible data modeling scheme with MongoDB with the on-demand Spark based computation framework was proposed. To facilitate the eventual consistency, scalability aspects of the e-health care applications, use case based indexing was proposed. With the effective data management, faster query processing the horizontal scalability has been increased. The overall efficiency and scalability of the proposed logical data management approach was analysed. Through the simulation studies, the proposed approach has been claimed to boost the performance of the bigdata based application to a considerable extent.
Education in the city of Ufa: digital environment as a condition for improving the quality of ethnocultural training of teachers

This article discusses search of adequate tools for training of psychological and pedagogical staff of preschool education institutions, aiming at improvement of living quality in city, under conditions of electronic education for working in ethnocultural environment of preschool children. The choice of humanitarian approach assuming studying of personality as a subject of cultural self-development in education is theoretically substantiated. The contents of projects on training professional staff of the City of Ufa (Bashkiria) for preschool education institutions, as well as their advanced training in the frames of implementation of innovation technologies and practices have been determined. Some activities on training of teachers for working in ethnocultural kindergartens have been described. Experience of development of online courses aimed at formation of working activities related with ICT competences of participants in educational relations has been disclosed. The following projects have been presented: Network Electronic Kindergarten promoting efficient training of teachers for working in ethnocultural educational and digital environment of Bashkiria; Centers of competence development: Preschool Education Teacher, Electronic Education Teacher, as well as workshops of department of higher education entity on World Skills of Preschool Education competence disclosing opportunities to improve life quality in city.
Effectiveness of using computer aided detection based on convolutional neural network for screening microcalcification On USG Mammae

Abstract Breast cancer is a very common cancer in women, affects millions of women each year, and is also one of the leading causes of cancer deaths among women. Early-stage cancer detection can reduce breast cancer mortality significantly in the long term. Breast ultrasound is a cost-effective and widely available screening tool. Breast ultrasound can be used for women who are at high risk of developing breast cancer but cannot undergo MRI examinations or for pregnant women who should not be exposed to X-rays, and also for women who have dense breast tissue. Although ultrasound is considered to be used for breast screening, its use is highly dependent on the doctor’s experience in reading the images. Therefore, to diagnose breast cancer using ultrasound images automatically can use computer assistance, namely Computer-Aided Detection (CADe). This study discusses a deep learning-based CADe system, namely the convolutional neural network (CNN), CNN is desired to evaluate several ultrasound images to make a microcalcification diagnosis. Microcalcifications are fine white spots, similar to grains of salt. They are not usually cancer, but certain patterns can be an early sign of cancer. Detecting microcalcification using CNN can be used as a routine breast screening, which can help doctors find signs of breast cancer earlier than is currently possible. The best MSE(Mean Squared error) loss value obtained is achieved at 0.24 with an accuracy of 0.76.
Density-Based Vehicle Counting with Unsupervised Scale Selection

A significant hurdle within any counting task is the variance in a scale of the objects to be counted. While size changes of some extent can be induced by perspective distortion, more severe scale differences can easily occur, e.g. in case of images taken by a drone from different elevations above the ground. The aim of our work is to overcome this issue by leveraging only lightweight dot annotations and a minimum level of training supervision. We propose a modification to the Stacked Hourglass network which enables the model to process multiple input scales and to automatically select the most suitable candidate using a quality score. We alter the training procedure to enable learning of the quality scores while avoiding their direct supervision, and thus without requiring any additional annotation effort. We evaluate our method on three standard datasets: PUCPR+, TRANCOS and CARPK. The obtained results are on par with current state-of-the-art methods while being more robust towards significant variations in input scale.
Review of gesture recognition technique using cloud-assisted wearable devices for real-time healthcare

With the development of technology, the use of robotic assemblies is increasing in different sectors including healthcare. In accurate surgeries, early and effective diagnosis, robots are becoming crucial for healthcare workers. However, one of the major challenges in the healthcare sector is the interaction complexity that restricts the use of robotic assemblies. The complexity in interaction with these assemblies does not allow their effective use and this is a major challenge in the smart healthcare system. The expertise requirements to operate such a complex system in the medical domain create additional challenges. Also, the collection of real-time healthcare data with accuracy is challenging with the available techniques. Therefore, in order to overcome these challenges, a system is developed in this research that uses cloud-assisted wearable devices to recognise gestures and help healthcare system in real time. This reduces the interaction complexity with the robotic assemblies in the healthcare system by providing effective control over these assemblies and automation through recognised gestures. Classification of the developed system is given in the research as components `SGR' and the system is developed based on the review of current techniques, and further evaluated, validated, and verified. This will be of great help in the medical domain reducing the interaction challenges and helping in real-time monitoring and diagnosis in the medical field.
Science Zone: An Augmented Reality based Mobile Application for Science

In recent years, technology has rapidly developed, and it has provided many technological advancements for the field of education with an attempt to improve and overcome its limitations. Augmented Reality is among these latest technologies which support to improve learning environment around the world. It can bring education to a new level which can help students in many significant ways. In Sri Lanka, augmented reality is rarely been used for the purpose of educational enhancements. Therefore, it was decided to develop an augmented reality embedded mobile application for the G.C.E Ordinary Level Students in order to make it easy for them to learn Science with more enthusiasm and interest. This research has been used marker-based approach to transmit images or objects in the text book into the real-world scenes in order to create a more productive learning environment for the students. The first version of the application covers four main areas in the Science curriculum, such as; Preparation of Acids, Human Anatomy, Organization of Plants and Biosphere Cycles. Feedback for the application was taken from randomly selected ten science teachers and twenty grade eleven students and accordingly the application was further developed. Their feedback proves that the application would satisfy the common requirements of students, and it would be an immense support in scoring good results for science.
До питання врахування досвіду США з використання інтернету для запобігання злочинності

Research on the impact of the Internet on the mind and legal culture of society and on current possibilities of using it for crime prevention. The United States experience in creating Internet services for citizens containing data on crime maps is included.
ACO for Workforce Planning

The text of this chapter is based on [, ]. Planing the workforce is difficult and important decision making industrial problem. It includes several level of complexity: selection and assignment. First the employers need to be selected from large set of available workers. The second is selected employers to be assigned to the jobs to be performed. The aim is to fulfil the work requirements with minimal assignment cost. The human resource management is one of the important parts of the production organization.
Generating Temporal Relation Candidates Based on Open-domain Relation in Clinical Notes

From the point of view in natural language understanding, capturing the contextual information contained in a document is heavily influenced by identifying the relations between entities. Also, in the medical field, there are attempts not only to grasp the information of relations in the clinical notes but also to find out temporal relations among the medical events or observations. In this paper, we propose a method for generating the candidates which become temporal relations based on the open-domain information for extracting relations from medical documents. Open-domain relations can be extracted without specifying any type of relations in the given document. If a relation instance has either a time expression or time-related event, it can also be used to create candidates that have the potential to become temporal relations. We expect that the proposed method helps to better understand the contextual information about temporal relations in medical documents.
Generative Grammar Based on Arithmetic Operations for Realtime Composition

Mathematical sequences in $$\mathbb {N}_0$$ are regarded as time series. By repeatedly applying arithmetic operations to each of their elements, the sequences are metamorphised and finally transformed into sounds by an interpretation algorithm. The efficiency of this method as a composition method is demonstrated by explicit examples. In principle, this method also offers laypersons the possibility of composing. In this context it will be discussed how well and under what kind of conditions the compositional results can be predicted and thus can be deliberately planned by the user. On the way to assessing this, Edmund Husserl’s concept of “fulfillment chains” provides a good starting point. Finally, the computer-based board game MODULO is presented. Based on the here introduced generative grammar, MODULO converts the respective game situation directly into sound events. In MODULO, the players behave consistent to the gaming-rules and do not care about the evolving musical structure. In this respect, MODULO represents an alternative draft to a reasonable and common use of the symbols of the grammar in which the user anticipates the musical result.
Finding and Use of Source Code Changes for Aspect-Oriented Software

In aspect-oriented software, code contained in special constructs called pieces of advice is used to define cross-cutting functionalities. This code is separated from more purpose-specific code (base code) and applies to it at specified program execution points called join-points. Such mechanism of composing a program imposes new requirements for finding changes between two versions of aspect-oriented software. This particularly refers to finding changes in advice applications at join-points as these applications are rather implict due to the absence of syntactical dependence of the base code to pieces of advice. In our previous work, we proposed and evaluated a novel approach for finding changes in advice applications. In this paper, we overview that approach, provide more details about how it works and additional results of manual verification analysis. We also compare the state-of-the-art approaches for finding and use of changes between two aspect-oriented software versions, including our previous work. Finally, we introduce the implementation of the graphical user interface for presenenting the changes found with our approach. We discuss its perspective usage and other potential applications of our approach.