Reviewerly/exHarmony huggingface dataset:
Grey-based multiple instance learning with multiple bag-representative

Multiple instance learning is a modification in supervised learning that handles the classification of collection instances, which called bags. Each bag contains a number of instances whose features are extracted. In multiple instance learning, the standard assumption is that a positive bag contains at least one positive instance, whereas a negative bag is only comprised of negative instances. The complexity of multiple instance learning relies heavily on the number of instances in the training datasets. Since we are usually confronted with a large instance space, it is important to design efficient instance selection techniques to speed up the training process, without compromising the performance. Firstly, a multiple instance learning model of support vector machine based on grey relational analysis is proposed in this paper. The data size can be reduced, and the importance of instances in the bag can be preliminarily judged. Secondly, this paper introduces an algorithm with the bag-representative selector that trains the support vector machine based on bag-level information. Finally, this paper shows how to generalize the algorithm for binary multiple instance learning to multiple class tasks. The experimental study evaluates and compares the performance of our method against 8 state-of-the-art multiple instance methods over 10 datasets, and then demonstrates that the proposed approach is competitive with the state-of-art multiple instance learning methods.
Analysis and Classification of Digital Educational Resources Used in the Work of a Proactive Teacher

In modern society, information technologies and associated processes are becoming increasingly influential and relevant, among them: collecting, storing, processing, providing, disseminating information, as well as its actualization ways. Information technology development trends have influenced education as well. The relevance of the research topic is based on the need to identify and study digital educational resources, as well as the importance of their introduction into educational institutions in order to improve the quality of education. Today, there is an obvious need for the development of effective methods of using the tools provided by information technologies, which will improve the students' ability of educational material procession producing a qualitative effect on their everyday life. The article deals with the problem of teaching children of the generation Z through the use of digital educational resources at school. Traditional teaching methods become obsolete and fail to give the expected results; therefore, the introduction of modern developments is required. The field of education needs new ways to monitor, test and systematize students' knowledge. The authors of the article have analyzed the current digital educational resources, and formulated their classification. The classification includes two large groups of digital educational resources: online services and mobile applications. Each of the groups, in turn, is divided into subgroups. Online services include those for testing students' knowledge and those with supporting material for teachers. Mobile applications include creative applications and selfmonitoring and student performance monitoring apps. The purpose of the study is to analyze the current digital educational resources and to offer their classification. The authors have developed and proposed a classification of modern digital educational resources. The features of each element of this classification have been identified. Finally, conclusions are made about the need to improve the educational process for schoolchildren in order to ensure higher and better results of educational activities. The authors note that the existing problem of educating children of the generation Z needs to be addressed with the help of digital educational resources, including the development of tools, techniques, practices and methods to increase the attention and interest of pupils to learning.
Enhanced Type 2 Triangular Intuitionistic Fuzzy C Means Clustering Algorithm for Breast Cancer Histopathology Images

The theoretical study, focused on contrast specific adaptive histogram equalization (CLAHE) for breast cancer histopathology image enhancement, projected an original form of type 2 triangular intuitionistic fuzzy membership function (TIFM). Digital pathology is one of the most developed fields in medical diagnosis. The detection of cancer nuclei is a very tough process where histopathology images contain hematoxylin and eosin stains. Image processing enhancement algorithms were applied to improve the image quality and diagnostic result of whether cancer nuclei are present or not. CLAHE method was developed based on predefined clip limit value, it is used to decrease the noise and gets a better quality of the histopathology image. The proposed research work based on clips limit value was calculated automatically by using type2 intuitionistic triangular fuzzy membership function. This proposed method has given a better result compared with existing methods of TFMCLAHE and CLAHE. The image quality metric techniques are such as psnr, mse, rmse and entropy. After enhancement, cancer nuclei were segmented through the proposed clustering algorithm name as triangular based spatial type 2 intuitionistic fuzzy c means. This proposed method was measured by ssim and it worked well compared to the existing algorithms such as Fuzzy c means and spatial Intuitionistic fuzzy c means clustering methods.
Service Robot Security from Unauthorized Access by Connection Control

In connection with mass introduction of robots in various spheres of activity, and also absence of due attention to such factor as safety, the probability of unauthorized access to their blocks of management raises. Typical decisions on safety maintenance by introduction of information security systems are not suitable for the robotized platforms in view of their capacities limitation. Thus, there is a problem of providing protection of the control unit of robotic platforms from various threats. The results of the analysis of possible approaches to the formation of a list of threats and vulnerabilities of the robotic platform are shown. The list of threats corresponding to current Russian legal system are given. Algorithms for secure interaction with a robotic platform have been developed. The results of testing countermeasures on an initially unprotected research robotic platform using the NI MyRio 1900 controller are shown. Research results can be useful both for scientific tasks in protecting against theft and distortion of research results, and for production tasks related to the security of service robots.
Education Synergism Environmental Based Learning: Nurul Islam Tamidung Batang Context

Abstract Environment-based education builds student ability and behavior with the surrounding environment, the environment occupies an important position, the implication is the availaibility of a conducive environment both indoor and outdoor. This must be supported with environment health that is an essential part of the learners to learn and improve their cognitive, motoric and affective capabilities. This is a must to be created in an educational institution. Educational institutions always want to give different nuances that are accompanied by the quality of the institution itself. In essence, creating an educational synergism based on an environmentally-based learning perspective should be supported by environmental health for the sake of creating synergistic learning conditions.
Evaluating the Performance of Navigation Prediction Model Based on Varied Session Length

Web navigation prediction plays a vital role in web, due to its broad research applications. It can be used for personalization, improvise website design, and business intelligence. Main aim of these applications is to enhance user’s satisfaction levels who are visiting the website. Web navigation prediction model tries to predict the future set of the webpage from their historical navigations. The past navigations are collected in the web server log file. Navigations form the sessions of varied length which are used for building the navigation model. Selecting very long sessions or very small sessions degrades the model performance. Thus, selecting an optimal session length is mandate as it would impact the model performance positively. This paper presents pre-investigation measures like page loss, branching factor and session length. We investigate the performance of prediction model based on two different ranges of session length. First range that has been considered is three to seven (3 to 7) and second range is two to ten (2 to 10). The Model has been evaluated on three real datasets. The experimental results show that selecting session of length ranging from 2 to 10 gives better learning hence intensifies accuracy of navigation prediction model. The model accuracy of Set B showed improvement from 0.27 to 8.73% in MSWEB, 0.62 to 2.8% in BMS and 10.81 to 14.23% in Wikispeedia dataset.
An Attention-Based Deep learning Situation Information Recommendation Model

Massive battlefield data, fast combat rhythm and complex combat system pose great challenges to commanders’ limited cognitive ability. While increasing available information and enhancing situational awareness, it will also lead to information overload, which will seriously interfere with commanders’ extraction and effective utilization of useful information. This paper puts forward a deep-learning situation information recommendation model based on attention mechanism in view of overload of situation information. It introduces a double-layer attention mechanism, and uses a multi-layer neural network to learn the attention weights at item-level and component-level respectively in order to explore the potential relationship between commander and situation information, build a commander preference prediction model and realize personalized recommendation.
Implementation of ECLAT Algorithm Technology: Determining Books Borrowing Pattern in University library

Abstract Library is one place to get lots of information. Every transaction information recorded in a fairly large database, the large data if it can’t be used it will make the problem for the librarian. This research aims to determine the books that are often borrowed when borrowing, using the rules of the mining association using the ECLAT algorithm. Equivalence Class Transformation Algorithm (ECLAT) performs frequent itemset search from the bottom. This algorithm will only scan the data once, the scanning process will not be repeated to get frequent k-itemset. The database scan process in the ECLAT algorithm is not repeated because on the seacrh itemset not pay attention the sequence from the item. The result of this study indicate the performance of the ECLAT algorithm is good by requiring 15 ms in the execution process. 102 transaction data the same minimum support is 1%, resulting in 21 frequent itemset.
A Proposed Technique for Enhancing the Mining Process in Blockchain Architecture

Blockchain is a new technology that support peer-to-peer communication without the intervention of any third party. Blockchain is usually a chain of blocks shared among all the participating nodes. Each block contains a set of transactions that represent the communication between any participating nodes. Mining is the mechanism employed in blockchain to secure the data. This paper investigates the current blockchain mining mechanism, its limitations and proposes clustering miners and transaction allocation technique (CMTA) to solve these limitations.
The Research on Facial Segmentation Algorithm for Facial Diagnosis in Traditional Chinese Medicine

A method for accurate segmentation of human face based on region growing and ellipse fitting is presented on facial diagnosis in Traditional Chinese Medicine (TCM). Firstly, the region growing method is used to locate the face skin region, and the possible area is segmented. Then the non-face area is removed by a morphological processing step, and further the edge and the initial outline of the face is obtained. Finally, the exact contour of the face is extracted by using the direct least square ellipse fitting method according to the acquired edge and initial contour. The experimental results have shown that the algorithm can overcome the interference problems between chin contour and neck region, and meet the requirements of face contour extraction for an image processing system of TCM facial diagnosis, which lays a foundation for further segmentation and detection of facial features.
Social Media facilitate Compliance Communication in Higher Education Institutions

Managing compliance is never an easy task for the institutions to handle. Over the past decade, regardless of any increasingly expanding range of regulatory regulations that govern the higher ed, the institutional organizations must manage countless compliance obligations without losing focus on the institution's vision, mission and culture. Any implications of non-compliance warrant major media attention and significant negative consequences. Given that the success rate of institutional's compliance is highly depending on their reliability communication system. Recent benchmark studies evidenced that higher ed is contemplating the communication system failures between the compliance stakeholders that prevents them from having an effective compliance framework in place. Hence, this paper presents a systematic literature review (SLR) of 26 relevant published studies related to topics in compliance communication dated from 2010 to 2019 to review the problems. By addressing three specific research questions, this SLR shows the following evidence gathered of these studies: (1) an overview of compliance communication challenges in HEIs and aspects which need to be improved, (2) current compliance communication in practice, and (3) the fact on how current social media tools used to facilitate compliance communications. The findings suggest that compliance communication in HEIs still has severe limitations in terms of exercising effective and proactive ongoing oversight on communicating compliance-related issues as an important part of compliance initiatives. However, through correctly identifying and understanding the compliance communication requirements (future work), it is possible to develop an effective social media-based compliance communication model for HEIs.
Practical Authentication Scheme Preserving Privacy Protection for the Internet of Things

Based on the authentication scheme of Wu et al. (published in the Journal of Ambient Intelligence and Humanized Computing, Volume 8, Issue 1, pp 101–116, 2017), we propose a robust privacy protection authentication scheme to prevent malicious attacks in general wireless sensor networks. In addition, we implement the proposed scheme in Rasperry Pi to simulate a home monitoring system and validate the feasibility of the proposed method.
Double-Directional Bernoulli Track - Before- Detect Filter with Particle Flow

Track-before-detect (TBD) aims to detect and track a weak target by integrating measurements over time and position. The key prerequisite to the integration is the accurate representation of the target measurement. However, the TBD methods implemented by the particle filter rely too much on the proposal density, and thus the representation of the measurement is not accurate enough. Therefore, this paper proposes a novel TBD method, which incorporates the newly developed particle flow filter into the Bernoulli TBD filter to improve the measurement representation. Moreover, the poor initial state estimation is also refined by a double-directional filtering solution. The effectiveness of this method is demonstrated on the simulation results of a fluctuating weak target tracking in K-distributed clutter.
A Method of Initial Orbit Determination for Space Group Targets

Generally the initial orbit determination algorithm handles a single space target and little attention has been paid to that for group targets. Since the association problem in an extended dense space group is usually difficult, we propose an initial orbit determination method to directly estimate the mean orbital elements of the group targets. Firstly, an improved density clustering algorithm is used to accurately segment the dense group targets from the space background Then the mean orbital elements of the space group targets are estimated by fitting the trajectory of the group with a perturbed ellipse. The J2 perturbation is considered in the geometric orbit determination. The ill-condition problem of the short-arc geometric determination is solved by improving the merit function.
‘Italianness’ in English-language novels: intratextual translation as a representational tool

A contemporary writer choosing to set a narrative in a different country might find themselves desirous to represent multilingualism (or ‘heterolingualism’, the term preferred by Rainier Grutman) on the page. Bringing a different language into the main language of the text, however, can be a complicated process. Using terminology introduced by Meir Sternberg in 1981, this article looks at three examples of contemporary British novels set partly in Italy: Virginia Baily’s Early One Morning, Adam Foulds’s In the Wolf’s Mouth and Penelope Fitzgerald’s Innocence. Particular attention will be paid to the different ways in which the narratives integrate Italian language into their English text, and to the different purposes served by intratextual translation strategies, before pointing out some of the limits of intratextual translation as a representational tool.
First person perspective video activity recognition

The initial development of two First-Person Perspective Video Activity Recognition Systems is discussed. The first system, the First Person Fall Detection or UFall, can be used to recognize when a person wearing or holding the mobile vision system has fallen. The problem of fall detection is tackled from the unique first-person perspective. The second system, the directed CrossWalk System (UCross), involves detection of the user movement across a crosswalk and is intended for use in helping a low vision person navigate. In both cases, the user is wearing or holding the camera device for purposes of monitoring or inspection of the environment. This first-person perspective yields unusual fall data and this is captured and used for the creation of a fall detection system. For both systems Machine Learning is employed using video input to trained Long-Term Short-Term (LSTM) Networks. These first-perspective video activity recognition systems use the Tensorflow framework [1] and is deployed using mobile phones for proof of concept. These applications could be useful for low vision people and in the case of fall detection for senior citizens, police, construction and other inspection-oriented jobs to help users who have fallen. The success and challenges faced with this unique first-person perspective data are presented along with future avenues of work.
Automotive Crash Detection Using Multi-sensor Data Fusion

The focus of this paper is the early detection of the frontal crash in automobiles, for the purpose of effective Airbag deployment decision, using information from multiple sensors deployed on-board, specifically accelerometer sensors located on the vehicle engine and on the driver’s seat-belt. Measured acceleration signal data streams from the sensors are fused, based on principles of Multi-Sensor Data Fusion (MSDF), for faster detection of a crash. Adaptive Kalman filtering is employed for simultaneous estimation of individual sensor data and their signal level fusion. The proposed crash detection system is simulated in MATLAB and validated using US-NHSTA (National Highway Safety Traffic Administration) automotive crash datasets. For comparative evaluation, crash detection algorithms for individual sensor data are also simulated and tested on the same datasets. The MSDF based system resulted in faster crash detection when compared to single sensor systems.
Content Linking Via Information Compression and Similarity

Content Linking(CL) is a new task in natural language processing(NLP) recently. In this paper, CL is regarded as a binary classification task using neural networks. The input is the feature of the sentence pair, while the output is their association. Word embedding is the most popular text feature representations. However, its dimensionality would be large, and simply stitching the word embedding features of two words couldn't contain the relationship between them. We propose a new text feature named Word2vec_V_I by Principal Component Analysis (PCA) and similarity calculation. Furthermore, a content linking method based on CNN and Word2vec_V_I is designed and implemented. Experiments on CL-SciSumm 2018 dataset verify the effectiveness of this method.
ENTERPRISE IMAGE LEVEL CONTROL

The article is dedicated to issues of the control of organization image level.The main purpose is to the improvement of the process as to the control of organization image level applying the method of system analysis and process approach.It is proposed to consider the control of organization image level as a management process by which the organization ensures the achievement of goals for the formation, improvement and maintenance of organization image at the achieved level by taking into account and assessing the compliance of the planned values of the indicators with the actual elements of the marketing mix and taking appropriate actions to eliminate identified inconsistencies.The suggested process includes seven stages, they are: measurement of the actual level of the external (or internal) organization image; identification of the planned level of the external (or internal) organization image; comparison of the actual level with the planned one; analysis of the components of the external (or internal) organization image: determination of those that are subject to adjustment; identification of measures contributing to the achievement of planned level of the external (internal) organization image; theoretical calculation of the organization image level and its components after the implementation of identified measures; comparison of the theoretical level of the external (internal) organization image with the planned one; implementation of the selected measures.This process allows foreseen the influence of each marketing mix elements separately as well as the influence of implemented measures on the generalized organization image level (external and internal).It is based on the use of the chain substitution method and allows predicting the image level in the planning period based on the results of previous periods.Knowledge of the sequence and content of the image level control stages will allow a company to timely identify inconsistencies between its planned and actual indicators, adjust the process of the image formation, improve and maintain the image at the achieved level, as well as implement effective measures to achieve the planned results.
The Specifics of Work With Gifted Children in Special Piano Lessons at Children’s Art School

The concept of “musical talent” is considered, as well as the main musical and performance abilities and skills of students-pianists. The features of teachers’ modern teaching approach in piano lessons with gifted children are characterized. The distinctive features of the manifestation of the basic musical talent of gifted children in the piano lessons are analyzed. The importance of enlightening the teacher to the individual characteristics of a gifted child is emphasized.