Reviewerly/exHarmony huggingface dataset:
Composite likelihood for aggregate data from clustered multistate processes under intermittent observation

Markov processes offer a useful basis for modeling the progression of organisms through successive stages of their life cycle. When organisms are examined intermittently in developmental studies, likelihoods can be constructed based on the resulting panel data in terms of transition probability functions. In some settings however, organisms cannot be tracked individually due to a difficulty in identifying distinct individuals, and in such cases aggregate counts of the number of organisms in different stages of development are recorded at successive time points. We consider the setting in which such aggregate counts are available for each of a number of tanks in a developmental study. We develop methods which accommodate clustering of the transition rates within tanks using a marginal modeling approach followed by robust variance estimation, and through use of a random effects model. Composite likelihood is proposed as a basis of inference in both settings. An extension which incorporates mortality is also discussed. The proposed methods are shown to perform well in empirical studies and are applied in an illustrative example on the growth of the Arabidopsis thaliana plant.
Text Recognition from Document Images

The objective is the development of effective reading skills in machines. After reading the text and comprehending the meaning, it would know itself and according to the program, it would implement the instructions. The current investigation presents an algorithm and software which detects, recognizes text and character with specific protocol in a image and programs itself according to the text. Technological advances in image processing have acquainted us with character recognition and many such related technologies, which have proved to be a milestone. However, even years after the invention of these technologies we have not been able to achieve a technology by which machine can read, interpret and act according to the instructions and even update their database if required. Here’s an attempt to make this reality. Machine replication of human functions, like reading, is a long-awaited dream. However, over the last five decades, machine reading has transformed from a dream to reality. Text detection and character recognition known as Optical Character Recognition (OCR) has become one of the most successful applications of technology in the field of pattern recognition and artificial intelligence. Numerous commercial systems for OCR exist for a variety of applications
Automatic two-chamber segmentation in cardiac CTA using 3D fully convolutional neural networks

Cardiac chamber segmentation has proved to be essential in many clinical applications including cardiac functional analysis, myocardium analysis and electrophysiology studies for ablation planning. Traditional rule-based or modelbased approaches have been widely developed and employed, however these methods can be time consuming to run and sometimes fail when certain rules are not met. Recent advances in deep learning provide a new approach in solving these segmentation problems. In this work we employ a TensorFlow implementation of the 3D U-Net trained with 413 cardiac CTA volumes to segment the left ventricle (LV) and the left atrium (LA). The network is tested on 162 unseen volumes. For LV the Dice similarity coefficient (DSC) reaches 90.2&plusmn;2.6% and for LA 87.6&plusmn;7.5%. The number of training and testing samples far exceeds the common use of datasets seen in literature thanks to the existing rule-based algorithm in Vitrea®’s Cardiac Functional CT protocol which was used to provide the segmentation labels. The labels are manually filtered, and only accurate labels are kept for training and testing. For the datasets with inaccurate labels, the trained network has proved to perform better in generating more accurate boundaries around the aortic valve, mitral valve and the apex of LV. The TensorFlow implementation allows for faster training which takes 3-4 hours and inferencing which takes less than 6 seconds to simultaneously segment 12 CT volumes. This significantly reduces the pre-processing time required for cardiac functional CT studies which usually consist of 10-20 cardiac phases and take minutes to segment with traditional methods.
Fast quantum randomness generation from vacuum fluctuation induced phase diffusion between pulses of laser diode

In this paper, the prototype of quantum random number generator (QRNG) which is shown by extracting ran- domness from vacuum fluctuation of the electromagnetic field inside the laser cavity. A distributed feedback (DFB) laser diode (LD) is directly modulated to produce short optical pulses. The laser is operated far above and far below the threshold resulting in field of pulse which has a true random phase. The efficient conversion of vacuum fluctuations to random sequence of power of optical pulses is made by homemade Michelson interferom- eter. A proposed device has proven to have far more superior interferometric visibility coupled with high power making it also a cost-effective PIN diode detector as compared to QRNGs based on phase fluctuation modu- lated by constant current. As a result a proposed device is highly integrated using only commercially available components. Due to appropriate realization of our device, we have a good quality of raw random bits that have passed all tests without postprocessing. So the postprocessing doesn't significantly decrease the speed of output random bits. In fact, we have successfully demonstrated a prototype of high speed QRNG at a generation rate of over 1 Gbps.
The plant anatomy practicum uses a smartphone-assisted digital microscope in improving student compound intelligence

The background of this research is that the activity of students at the time of plant anatomy practice is not conducive and the learning process given is not oriented to explore students' multiple intelligence ability, so that the visual, logical-mathematical, physical-kinaesthetic and naturalistic ability of students is still low. This is evidenced through reports of their lab results and their learning activities are still low, but in generally plant anatomy is windows to explore science or science curiosity is not limited. The purpose of this study is to increase student learning activities, improve students 'multiple intelligence, and to find out students' responses to the application of digital microscope assisted smartphone in practicum of plant anatomy. Student learning activity is assessed by using observation sheet, the ability of multiple intelligences is measured by using test in the form of description, and student responses were assessed using a response questionnaire. The result of the research shows that the increase of students 'learning activity has very good criteria. The use of digital assisted microscope can improve students' intelligence ability, and the student response to the use of digital microscope with the aid of smartphones in the plant anatomy practice has very good criteria.
Exploiting Figures of Speech in Cultural Heritage Reasoning

Cultural Heritage are almost always commented and analyzed by using references to figures of speech. In particular, metaphors and allegories are very frequent in ancient and historical documents, painting and sculptures. It is frequent to have some hints about the assets and their authors by comparing their contents with elements in the domain of the figures of speech to which the asset refer. In order to enable reasoning by figures of speech, we propose here a methodology able to link concepts in the domain of cultural assets, with concepts in the domain of the figure of speech. We show here how reasoning in all these domains help in discover some elements related to the cultural heritage that humans may neglect at first glance.
SELF-IMAGE OF THE FIRST-YEAR STUDENTS OF TECHNICAL UNIVERSITY

This article describes the current issues of studying self-image of students of elite technical university. The first-year students have been involved in the study. The cognitive and emotional components of self-image were selected as the subject of the study. Diagnostic research complex included interview methods, questioning, observation, analysis of documents and the author’s projective drawing technique «I am a student». According to the results of the empirical research, 1) such five types of images, as elitism, cognition, integration, difficulties, forcedness have been highlighted; 2) the interpretation of self-image of first-year students by the type of drawing has been carried out.
Developing the Analysis Tool of Cyber-Attacks by Using CTI and Attributes of Organization

Cyber Threat Intelligence (CTI) is a source of useful information for organizations to take countermeasures against cyber-attacks. The process of using CTI is not automatic and requires human interventions, because we have to (1) check the CTI data to avoid obstructing business before executing countermeasures and (2) identify the similarity among CTI data to make the countermeasures effective. However, human tasks in using CTI are difficult, because CTI is inherently not human friendly and a large amount of CTI data is provided. Hence, we have to spend a lot of time before taking countermeasures. To solve this problem, we developed an analysis tool which can pick up and visualize a useful subset of CTI information as a graph, together with attributes of the organization, to help human judgment. By using graph structure, the relevancy to the organization and the similarity among CTI data are revealed at a glance. Moreover, the tool enables the reconciliation of CTI data, i.e. adding new relationships between them, to store the result of the analysis for later use. This helps us to take sophisticated countermeasures.
Inapplicability, Redundancy and Uncertainty of UML

Three problems i.e. inapplicability, redundancy, uncertainty of UML are discussed. In order to solve these problems, some ideas are given, including using business variables instead of classes, using system discrete events instead of use case diagrams, using requirement function instead of statechart diagram. New life cycle model is studied. New core ideas are proposed, including that requirement can not be refined step by step, and white box testing and black box testing are no longer differentiated but white box branches and black box branches are distinguished. Finally, the next step work is given.
Climatized module as the basic unit of high-tech medicine. Problems of development and application

In this article a study of climatized module application as a product for clean rooms complex formation in different technological schemes was made. The problems of clean rooms modular formation as well as its all advantages and disadvantages are considered. The clean rooms classification was analyzed. The scheme of climatized module classification has been derived. The problems of modern rules and regulations in sphere of clean rooms organization in Russian Federation was revealed and assumptions for improving of this situation were made. The following issues are considered: ensuring a high level of clean rooms equipping and execution; development of modular construction in medicine, pharmaceutical production, production of microelectronics and other fields on the climatized module basis; the possibility of upgrading obsolete technological solutions; creation of a structural classification of the climatized module for both medical technology and for various areas of possible application. The conclusions on the research were made.
A Method based on Sentence Embeddings for the Sub-Topics Detection

Weibo topics are characterized by diversity and complexity, and often contain different sub-topics even for the same topic. Therefore, how to classify sub-topics effectively and accurately is of great significance. Due to the strong similarity between sub-topics belonging to the same topic, most existing methods cannot be directly applied to the task of sub-topic discovery. In this paper, a new technique based on sentence embeddings for detecting sub-topics of weibo is proposed. The information of blogs is represented by sentence embeddings containing semantic information and the results are verified by clustering.
Understanding the scientific nature of competence in higher education as problem to improve its quality

The article shows the problems of improving the quality of Russian education related to the understanding of the essence of competencies. In spite of the constant «updates» of the Federal State Educational Standards for Higher Education (FES of HE), there is no obvious qualitative change in the solution of this problem. And first of all, it refers to what is not clear: what and how students should be taught, although the standards define that students should have the necessary competencies to perform a professional activity. However, despite numerous discussions, a clear understanding of the scientific essence of the concept of «competence», as a rule, is not given anywhere. And this today, in our opinion, is the main problem and obstacle in the qualitative improvement of the educational process in domestic universities.
Towards Classification of Loop Idioms Automatically Extracted from Legacy Systems

Legacy systems are important in business but difficult to maintain. One of the causes of the difficulties is a large number of code clones in the systems; Those clones implement similar functionalities using common loop idioms in a company. Since the loop idioms have been developed to implement popular functionalities, most of them are likely to be translated into simple SQL statements in a new, modernized version of a system. To investigate the feasibility of the approach, we propose a method to automatically extract cloned loop idioms embedded in COBOL program files. We manually classified the extracted idioms and labeled them according to their functionalities. We evaluated the accuracy of our classification result with three experts.
Using Go in teaching the theory of computation

With increased public interest in the ancient game of Go since 2016, it is an espe- cially good time to use it in teaching. The game is an excellent source of exercises in the theory of computation. We give some exercises developed during our research on Go which were then used when teaching this subject at Monash University. These are based on One-Dimensional Go (1D-Go) which uses a path graph as its board. They are about determining whether or not a position is legal and counting the number of legal positions. Curriculum elements that may be illustrated and practised using 1D-Go include: regular expressions, linear recurrences, proof by induction, nite automata, regular grammars, context-free grammars and languages, pushdown automata, and Turing machines.
Information Gathering Planning with Hermite Spline Motion Primitives for Aerial Vehicles with Limited Time of Flight

This paper focuses on motion planning for information gathering by Unmanned Aerial Vehicle (UAV) solved as Orienteering Problem (OP). The considered OP stands to find a path over subset of given target locations, each with associated reward, such that the collected reward is maximized within a limited time of flight. To fully utilize the motion range of the UAV, Hermite splines motion primitives are used to generate smooth trajectories. The minimal time of flight estimate for a given Hermite spline is calculated using known motion model of the UAV with limited maximum velocity and acceleration. The proposed Orienteering Problem with Hermite splines is introduced as Hermite Orienteering Problem (HOP) and its solution is based on Random Variable Neighborhood Search algorithm (RVNS). The proposed RVNS for HOP combines random combinatorial state space exploration and local continuous optimization for maximizing the collected reward. This approach was compared with state of the art solutions to the OP motivated by UAV applications and showed to be superior as the resulting trajectories reached better final rewards in all testing cases. The proposed method has been also successfully verified on a real UAV in information gathering task.
INVESTIGATION INTO MOTIVATION AND JOB SATISFACTION AMONG TEACHERS IN THE COLLEGE EDUCATION SYSTEM

This paper aims to investigate the important factors affecting job satisfaction and motivation of teachers in college. The statistical population of this study includes 67 teachers at Zhejiang University of Science and Technology who have answered the questionnaire. The results of the study show the group of people whose age is above 46 show greater levels of job satisfaction and factors such as work experience have an impact on job satisfaction. Furthermore, we figure out the most essential factor is an increase in the teachers and professors salary. The study also showed that teachers are satisfied with work conditions and have identified that they need to raise their teaching level by further studies. According to the further analysis, a strict evaluation system does not give a suitable result in improving motivation.
Can the Digital Revolutionize Academic Scholarship

This is a book review of Going Public: A Guide for Social Scientists by Arlene Stein and Jessie Daniels (2017).
Joint Identification Method Research of Access System Base on RFID and 3D Face Recognition

In this paper, the main contents of radio frequency identification, 3D(three-dimensional) face recognition, associative recognition algorithm based on radio frequency and 3D face recognition. The associative recognition method combined with radio frequency identification and 3D face recognition proposed in the paper, It can combine the advantages of stable radio frequency identification and high recognition rate and the advantages of 3D face recognition identity security to achieve the purpose of security, high recognition rate and strong operability of the entrance guard system. Therefore, it has broad application prospects in the access control system.
Application of Distance Measurement NLP Methods for Address and Location Matching in Logistics

Paper is based on research based on linguistic terms denoting an address used for delivery services in logistic. Distance measurement NLP methods are widely usable in text mining and can be used to find the similarity among sentence or document. As part of logistics process, being able to determine correct address using machine learning we need to tackle issue of two addresses comparison (street name, city name etc.) is crucial for efficient service. This paper explains comparison techniques based on similarity score that can be calculated using distance measurement. As part of process, several distance measurements were compared while conclusion include results and recommendation on usage in address and location matching in logistics (post services).
Inverting video slide shows

Video slide shows constitute a very frequent video type. Typically, in order to produce such a video, a series of still images are used and processed. We present a method to solve the video slide show inversion problem where we are given a video slide show and we wish to extract the sequence of still images that have been used to produce this video. Our approach relies on a fast and efficient key-frame extraction method that partitions the video into content homogeneous segments and extracts a representative key-frame for each video segment. An important characteristic of this method is that the number of key-frames is determined automatically. Next, the set of key-frames is further processed to solve various problems such as mixing of images, fade-in/fade-out and zooming effects that produce keyframes not belonging to the original image sequence, thus they must be detected and discarded. We provide illustrative examples from the application of the method on several video slide shows with different characteristics.
Enrich Exiting UML Class Model Using Ontology

Ontologies are becoming more and more important in our software development cycle as it provides critical semantic foundation and we know class modeling is widely adopted all over the world and it help in coding phase to the developers. We use both of these techniques to make or create ease in software development cycle. We use class models for our initiative after that we extract the data from that class models by using metadata and then match that extracted data with the original one or basic data. After complete and comprehensive comparison between data we identify the difference between user requirements and system model which is developed by our software and then validate either the system required some changes or not. This technique has significant advantages over the software life cycle as most importantly it’s a time and money saving technique you don’t need to make all new class models if any changes required either by user or system up gradation you can modify, update and enrich already existing classes by using this technique.
Breast density follow-up decision support system using deep convolutional models

Breast cancer risk assessment relies on accurate classification of breast density, which is a key component of the ACR breast cancer screening recommendations for clinical decisions. The 5<sup>th</sup> edition of the BIRADS standard divides breast density into four categories, ranging from almost entirely fatty to extremely dense. High breast density (classes C and D) reduces the sensitivity of mammography, since the dense fibroglandular tissue can hide lesions, masses and other findings. Therefore, although the benefit of supplementary imaging in such cases has not been conclusively demonstrated, the ACR guidelines suggest additional screening for patients with high breast density. This creates an important treatment decision boundary between class B (scattered areas of fibroglandular density) and class C (heterogeneously dense). Unfortunately, the slightly abstract, qualitative nature of the class descriptions leads to significant inter- and intra-rater variation in breast density assessment. This is exacerbated by updates to the BIRADS standard that can cause recent breast density assessments to be incompatible with prior assessments for the same patient. Additionally, images from similar patients can vary significantly when taken with different devices or at sites with different acquisition protocols. To address these issues, we present a new deep learning algorithm combining three models that achieves accurate and objective breast density classification. The first model performs the normal four-class breast density classification, the second model performs a two-class low (A or B) vs. high (C or D) classification, and the third patch-based model focuses on improving the accuracy of the B and C categories. We present initial results from 9989 studies from a three-site dataset with BIRADS 4<sup>th</sup> and 5<sup>th</sup> edition ground truth.