Reviewerly/exHarmony huggingface dataset:
Face Verification Using Single Sample in Adolescence

Recognition of facial aging using face images has enormous applications in forensic and security control. In this study, an attempted to verify the face images of adolescence and adults by using a single reference sample have been made. To study this problem, we have designed our model by considering with-patch based and without-patch based face images. Both local and pre-trained deep features have been extracted during feature extraction. Further, the well-known subspace techniques such as Principal Component Analysis (PCA) and Fisher Linear Discriminant (FLD) have been adopted for dimensionality reduction. To measure the model’s goodness, we have created our dataset consisting of two images of 64 persons, each with an age gap of 10 years between adolescents (15 years old) and adults (25 years old). The comparative analysis between with-patch and without-patch based images, and PCA and FLD have been studied effectively. The pre-trained networks give the highest matching characteristics at 96% using top projection vectors. The experimental results reveal that the deep learning model outperforms the state-of-the-art method for face verification. Therefore, the obtained results are promising and encouraging for face biometric applications.
Emergency Landing Field Identification Based on a Hierarchical Ensemble Transfer Learning Model

The full loss of thrust of an aircraft requires fast and reliable decisions of the pilot. If no published landing field is within reach, an emergency landing field must be selected. The choice of a suitable emergency landing field denotes a crucial task to avoid unnecessary damage of the aircraft, risk for the civil population as well as the crew and all passengers on board. Especially in case of instrument meteorological conditions it is indispensable to use a database of suitable emergency landing fields. Thus, based on public available digital orthographic photos and digital surface models, we created various datasets with different sample sizes to facilitate training and testing of neural networks. Each dataset consists of a set of data layers. The best compositions of these data layers as well as the best performing transfer learning models are selected. The hyperparameters of the chosen models for each sample size are optimized with Bayesian and Bandit optimization. The models outputs were investigated with respect to the input data by the utilization of layer-wise relevance propagation. With optimized models we created an ensemble model to improve the segmentation performance. Finally, an area around the airport of Arnsberg in North Rhine-Westphalia was segmented into 26.252 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> as landable and 221.329 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> as not suitable for an emergency landing. In sum 54,997 emergency landing fields are identified and stored in a database. The verification of the final approach's obstacle clearance is left unconsidered.
Risk-Based A*: Simulation Analysis of a Novel Task Assignment and Path Planning Method

This paper addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in material handling applications. We introduce risk-based A*, a novel TAPP method, that aims to reduce conflict and travel distance for AMRs considering system uncertainties such as travel speed, turning speed, and loading/unloading time. An environment simulator predicts the distribution of future locations for each AMR and constructs a probability map for future AMR locations. A revised A* algorithm generates low-risk paths based on the probability map. A discrete event simulation experiment shows our model significantly reduces the number of conflicts among robots in stochastic systems.
Research on Question Answering System of Character Relationship Based on Literary Works

In view of the huge chapter system and complicated character relationships of existing literary works, users obtain accurate character relationships difficulty, the paper proposes a research on the character relationship question answering system based on literary works and conducts an example analysis. Natural language processing technologies such as part-of-speech tagging and entity recognition are used to study the automatic extraction method of character relationships in literary works. The paper realizes the function of quickly giving answers to related relationships based on the names of characters entered by the users. Typical cases verify the effectiveness of the question answering system.
A Novel Approach to Determining the Quality of News Headlines

Headlines play a pivotal role in engaging and attracting news readers since headlines are the most visible parts of the news articles, especially in online media. Due to this importance, news agencies are putting much effort into producing high-quality news headlines. However, there is no concise definition of headline quality. We consider headlines as high quality if they are attractive to readers, and highly related to the article contents. While almost all the previous studies considered headline quality prediction as either clickbait detection (which is a binary text classification problem), or popularity prediction (which is a regression problem), our model employs four quality indicators to incorporate these two factors. In this paper, we first discuss the previous works on the news headline quality detection. We then propose a machine learning-based model to predict the quality of a headline based on four quality indicators before the publication of the news article. The proposed model is an extended version of our previously proposed model in a way that it considers sentiment features of headlines as well. We conduct experiments on a news dataset and compare our method with the state-of-the-art NLP models.
On Compatibility/Incompatibility of Two Discrete Probability Distributions in the Presence of Incomplete Specification

Conditional specification of distributions is a developing area with several applications. In the finite discrete case, a variety of compatible conditions can be derived. In this paper, we revisit a rank–based criterion for identifying compatible distributions corresponding to complete conditional specification, including the case with zeros under the finite discrete set up. Based on this, we primarily focus on the compatibility of two conditionals (under the finite discrete set-up) in which incomplete specification on either or both the conditional matrices are present. Compatibility in the general case are also briefly discussed. The proposed methods are finally illustrated with several examples.
Mobile App Authentication Systems: Usability Issues for Cyrillic Alphabet Users (Pilot Study)

According to World Advertising Research Center 72% of internet users worldwide will solely use smartphones to access the web by 2025 [1]. Nevertheless, developers continue to design authentication systems within the desktop computer paradigm. The paper aims to assess the usability of authentication systems interfaces in smartphones used by Cyrillic alphabet users. The authors study authentication practices, among which password authentication is the most common. Then, the authors identify the features of creating passwords by Cyrillic alphabet users and discuss the usability issues of existing authentication tools, proposing experiment design for further study.
Predicting the Prognosis of Patients in the Coronary Care Unit via Machine Learning Using XGBoost

Background: Early prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the XGBoost algorithm to prognosticate CCU patients, and compared XGBoost with traditional classification models. Methods: CCU patients’ data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days–1 year, 1 year–5 years, and ≥5 years. Variables were displayed and compared between groups using the STATA software. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the curve (AUC) of the receiver operating characteristic curves. We also constructed sub-models of each model based on the different categories of death time and compared the differences by decision curve analysis. The optimal model was further analysed using a clinical impact curve. Findings: A total of 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model performed better in accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841 and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different category of patients. Interpretation: For CCU physicians, ML technique by XGBoost is a potential predictive tool for patients with fast changing condition, and it may provide them with-assistance of prognosis improvements. Funding: National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Medical and Health Science Program of Zhejiang Province.Declaration of Interest: All authors declare that they have no competing interest.
Object detection by using Chebyschev filter : Reducing error in object detection using traditional way

In the preceding papers we have very distinguishably described object detection methods right from its roots. The mathematical models such as optical flow by LucasHorn form the backbone of modern object detecion. The next models such as the motion models also play a important part in object detection, these were Picard and Bergen et.al. In this paper we are on the very distance of object detection by using a prominent filter which is the Chebyschevfilter.Previous study done by us play a major role in establishing this work.
The optimal wavelet estimation for biased density

In this paper, the optimal estimation at a given point is considered for biased density in theory. A lower bound is first provided for all possible estimators over the local Hölder space. Using wavelet method, a linear wavelet estimator is constructed and turned out to be optimal by investigating its upper bound of pointwise convergence rate. To get the adaptivity, a nonlinear wavelet estimator is obtained and proved to be near-optimal within a logarithmic factor.
FILO OF ALEXANDRIA: DISCUSSION TOWARD THE LOGOS IN THE PHILOSOPHY OF EDUCATION

The article considers the influence of Greek philosophy on the ideas of the formation of the Hellenistic philosopher Philo of Alexandria. The object of study was the philosophical work of Philo. This study aimed to discover the ambiguity of the term logos as a central concept that defines in Philo's philosophy the relationship between God (the ideal creative teacher) and the world. In the works of Philo, the Logos appears as the highest, sub-divine, infinite power of the mind, which has no signs, but at the same time is identified with God. The transcendental nature of the Logos, embodied in the image of God's mind, in its paradoxical nature closely corresponds to holiness and higher wisdom. The research methodology is based on an interpretation that allows us to define the allegory and, therefore, the real meaning of Philo's philosophy, the central part of which is the philosophical reflection of the Holy Scriptures as the main source of education and the concept of Scripture, undergoing specific and simultaneously incomparable modifications. To identify these meanings, methods of systematization and hermeneutics are used. The result of the study is expressed in identifying various forms of embodiment and educational activity and the Logos.
Optimization of elderly nutrition needs using PSO algorithm: A case study at POSBINDU PTM Sejahtera

Abstract POSBINDU PTM Sejahtera is a health post that aims to increase awareness of the elderly in preventing non-communicable diseases. According to Departemen Kesehatan, this disease can be caused by food consumption. The food consumed must include vegetables and fruit to enhance the concept of active aging. However, these recommendations are not comparable with the POSBINDU PTM screening data. Screening data mentioned that only 3.5% of 73 elderly people consume vegetables and fruit three times a day. Factors that inhibit the elderly in consuming healthy food are the officers only giving food abstinence advice and expensive food staples. The problem of this optimization model can be solved by the artificial intelligence algorithm, Particle Swarm Optimization (PSO). The results of this study, PSO can provide varied food recommendations at a minimal price (optimization model). The calorie and carbohydrate content gets a value of &lt;10% of the nutritional needs of the elderly, while the protein and fat content produce a greater difference of &gt; 10%. The average price of foodstuffs produced by the PSO algorithm is Rp.50,965.
A Roadmap for Implementing the General Data Protection Regulation in Higher Education Institutions Using Kotter’s Organizational Change Model

The compliance with the General Data Protection Regulation (GDPR) is mandatory since May 25, 2018 for any organization dealing with personal data of European citizens. For the success of the GDPR implementation to be a reality, it is important to guarantee the existence with an adequate level of performance, of a set of Critical Success Factors (CSF). This article is based on a work in progress, but with final results already available, that determined a set of 16 CSFs related to the implementation of the GDPR in Higher Education Institutions (HEIs). One of these 16 CSFs highlights the importance of correctly managing change, being one of the most used models in this context including in HEIs, the 8-step organizational change model by Kotter [1]. The Kotter’s organizational change model is proposed in this study as a roadmap, to systematically carry out the change management related to the implementation of GDPR HEIs.
The use of spherical learning XR-arenas based on an abstract symbolic environment in physics teaching

Abstract Augmented/Virtual Reality (AR/VR) teaching technologies are increasingly popular in physics classes. The AR/VR (united by the term Extended Reality or XR) makes the physics course more close and understandable for young people. In addition, an abstract symbolic environment inside XR spherical arenas is easy applicable for physics teaching. The paper describes elements of an AR/VR sphere, abstract and symbolic objects of a learning arena. It deals with some essential findings from the analysis of experimental XR-teaching, controversial and debatable issues. Methodology of the study involved practical using of XR-devices and XR-applications in order to find adequate teaching elements of XR-arenas and the survey of students’ opinions. Nowadays, XR hardware and software are becoming much more prevalent among students. Physics teachers can easily respond to this request due to the specifics of their professional activities. Both teachers and students are interested in the easy use of XR demonstrations, unusual techniques, and pretty staggering opportunities of virtuality.
Using the Hammer only on Nails: A Hybrid Method for Representation-Based Evidence Retrieval for Question Answering

Evidence retrieval is a key component of explainable question answering (QA). We argue that, despite recent progress, transformer network-based approaches such as universal sentence encoder (USE-QA) do not always outperform traditional information retrieval (IR) methods such as BM25 for evidence retrieval for QA. We introduce a lexical probing task that validates this observation: we demonstrate that neural IR methods have the capacity to capture lexical differences between questions and answers, but miss obvious lexical overlap signal. Learning from this probing analysis, we introduce a hybrid approach for representation-based evidence retrieval that combines the advantages of both IR directions. Our approach uses a routing classifier that learns when to direct incoming questions to BM25 vs. USE-QA for evidence retrieval using very simple statistics, which can be efficiently extracted from the top candidate evidence sentences produced by a BM25 model. We demonstrate that this hybrid evidence retrieval generally performs better than either individual retrieval strategy on three QA datasets: OpenBookQA, ReQA SQuAD, and ReQA NQ. Furthermore, we show that the proposed routing strategy is considerably faster than neural methods, with a runtime that is up to 5 times faster than USE-QA.
A Multilevel Inference Mechanism for User Attributes over Social Networks

In a real social network, each user has attributes for self-description called user attributes which are semantically hierarchical. With these attributes, we can implement personalized services such as user classification and targeted recommendations. Most traditional approaches mainly focus on the flat inference problem without considering the semantic hierarchy of user attributes which will cause serious inconsistency in multilevel tasks. To address these issues, in this paper, we propose a cross-level model called IWM. It is based on the theory of maximum entropy which collects attribute information by mining the global graph structure. Meanwhile, we propose a correction method based on the predefined hierarchy to realize the mutual correction between different layers of attributes. Finally, we conduct extensive verification experiments on the DBLP data set and it has been proved that compared with other algorithms, our method has a superior effect.
Design and Implementation of User-Centered Adaptive Search Engine

With the ever-growing variety of information, the retrieval demands of different users are so multifarious that the traditional search engine cannot afford such heterogeneous retrieval results of huge magnitudes. Harnessing the advancements in a user-centered adaptive search engine will aid in groundbreaking retrieval results achieved efficiently for high-quality content. Previous work in this field have made using the excessive server load to achieve good retrieval results but with the limited extended ability and ignoring on demand generated content. To address this gap, we propose a novel model of adaptive search engine and describe how this model is realized in a distributed cluster environment. Using an improved current algorithm of topic-oriented web crawler with User Interface based Information Extraction Technique was able to produce a renewed set of user-centered retrieval results with higher efficiency than all existing methods. The proposed method was found to exceed by 1.5 times and two times for crawler and indexer, respectively than all prevailing methods with improved and highly precise results in extracting semantic information from Deep web.
A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries

Automatic Speech Recognition (ASR) technology has the potential to improve the learning experience of students in the classroom.This article addresses some of the key theoretical areas identified in the pursuit of implementing a speech recognition system, capable of lesson summary generation in the educational setting.The article discusses: some of the applications of ASR technology in education; prominent feature extraction and speech enhancement techniques typically applied to digital speech; and established neural network-based machine learning models capable of keyword spotting or continuous speech recognition.Following the theoretical investigation, a model is proposed for the implementation of an automatic speech recognition system in a noisy educational environment to facilitate automated, speech-driven lesson summary generation.A prototype system was developed and improved based on this model, ultimately proving itself capable of generating a lesson summary intended to bolster students' secondary contact with lesson content.This topic-oriented lesson summary provides students with a lesson transcript, but also helps them to monitor educator-defined keyword terms, their prevalence and order as communicated in the lesson, and their associations with educatordefined sections of course content.The prototype was developed using the Python programming language with a modular approach so that its implemented Continuous Speech Recognition system and noise management technique could be chosen at run-time.The prototype contrasts the performance of CMUSphinx and Google Speech Recognition for ASR, both accessed via a cloud-based programming library, and compared the change in accuracy when applying noise injection, noise cancellation or noise reduction to the educator's speech.Proof of concept was established using the Google Speech Recognition System, which prevailed over CMUSphinx and enabled the prototype to achieve 100,00% accuracy in keyword identification and association on noise-free speech, contrasted with a 96,93% accuracy in keyword identification and association on noise-polluted speech using a noise-cancellation technique.
A Comprehensive Investigation About Video Synopsis Methodology and Research Challenges

With enormous growth in video surveillance technology, the challenges in terms of data retrieval, monitoring, and browsing have been increased. A smarter solution for this is a video synopsis technique that represents prolonged video in a compact form based on the object trajectories rather than the key frame approach. It converts long video footage into shorter video form while preserving all the activities of the original video. The object trajectories are shifted in time domain as well as in spatial domain to offer the maximum compactness while maintaining the sequence of original source video. This paper gives the brief about the different approaches, evaluation parameters, and datasets used to assess the quality of synopsis video. The main objective is to investigate the query-based video synopsis useful for data retrieval through activity clustering steps in the synopsis framework that will also help to solve societal problems.
DEVELOPMENT OF ARTISTIC ABILITIES OF CHILDREN OF SENIOR PRESCHOOL AGE IN THE PROCESS OF DRAWING

Objective: to identify the component features of artistic abilities of children in older preschool children. Methods: the complex of diagnostic procedures included a set of tasks developed by T. S. Komarova, N. P. Sakulina and focused on the assessment of each of the structural components of the artistic abilities of older preschool children (the ability to create an image (perception, manual skill), the ability to create an artistic image by design) allowed us to analyze the features of artistic abilities and outline the prospect of its further development, in terms of testing pedagogical conditions. Results: of the study revealed that most children of older preschool age are assigned to the low level of artistic abilities: there is a significant distortion of shape, proportion and color; the layout on the plot sheet is often broken, the compositional intent is absent, the image is static, the proportions of objects are not violated, color harmony is not expressed, 1-2 colors are used. They have difficulties in creating and implementing an artistic idea and its independent implementation, selecting means of artistic expression, a low degree of originality of the idea, and dissatisfaction with the. Scientific novelty: the dynamics and conditions for the development of the artistic abilities of modern preschool children are identified and characterized. Practical significance: methodological approaches to the development of visual abilities of older preschoolers in a preschool educational institution are presented.