Reviewerly/exHarmony huggingface dataset:
Medicare Fraud Detection Using WTBagging Algorithm

Medicare fraud detection is essential for the stable development of the health insurance industry. In this study, we use the Bagging algorithm to build a Medicare fraud detection model. The Gradient Boost Tree, XGBoost, CatBoost, and Random Forest models, are proven effective in past studies, and are used as the base models to construct the Medicare fraud detection model. We proposed the Bagging algorithm based on the weighted threshold method named WTBagging and made ten model combinations using Bagging and WTBagging algorithms. The data are cleaned and sampled to construct three datasets with different class distributions. The 5-fold cross-validation process was applied to the model training and repeated ten times, and the F1 value was the performance metric to evaluate the model combination. The results show that the model combinations of the WTBagging achieved the highest F1 values under all datasets.
<i>Retracted:</i> Professional training of future engineers‐teachers: The societal needs of modern educational technologies introduction

Abstract In modern times, new means and techniques for optimising the educational process are required because one of the purposes of professional training is to increase the effectiveness of the educational process. Good‐quality professional training requires the introduction and implementation of modern educational technologies. The authors studied this, using the example of future engineers‐teachers' training, and implementation of modern educational technologies into this process was the purpose of the study. Such methods as analysis, synthesis, empirical method, concretisation, generalisation, survey and questionnaire were used. The aspects of readiness for the introduction of modern educational technologies were studied. The first condition is the readiness of university teachers to organise the process of training future engineers‐teachers with the use of modern educational technologies and their active introduction into the educational process. Another aspect is the development of individual approaches to each student with the use of modern educational technologies. Also, a desire for professional self‐improvement and self‐education should be developed in students which will contribute to successful training. The results of the study allowed to conclude that the level of students' readiness to the introduction of modern educational technologies does not fully meet societal needs.
CMA-ES and advanced adaptation mechanisms

tutorial CMA-ES and advanced adaptation mechanisms Authors: Youhei Akimoto University of Tsukuba, Japan University of Tsukuba, JapanView Profile , Nikolaus Hansen Inria & Ecole Polytechnique, France Inria & Ecole Polytechnique, FranceView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 636–663https://doi.org/10.1145/3449726.3462748Published:08 July 2021Publication History 0citation70DownloadsMetricsTotal Citations0Total Downloads70Last 12 Months25Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improving primary school teachers’ competence in teaching multi-literacy through RADEC-based training programs

The purpose of this research is to improve the competence of teachers in teaching multi-literacy through a training programme based on the Read–Answer–Discuss–Explain–Create (RADEC) learning model. This descriptive qualitative study was conducted in a private primary school in Bandung, West Java, Indonesia. The participants included were a teacher and 29 students (17 male and 12 female students, with an average age of 11 years). Documentations, observations and interviews were used as data collection. The data were analysed quantitatively and through the Rasch model. The results show that mentoring during the implementation of the RADEC model can increase teachers’ knowledge of multi-literacy learning and teachers’ skills in planning and implementing the RADEC model. It can be concluded that the RADEC learning model contributes to a positive change in student learning, promotes 21st-century skills and includes multi-literacy skills. Thus, trainers can use the RADEC learning model to enhance teachers’ ability in teaching multi-literacy. &#x0D; &#x0D; Keywords: Multi-literacy, RADEC model, teachers’ competence
Landcoin: A Practical Protocol for Transfer-of-Asset

Blockchains ensure integrity, transparency, and immutability of transactions they process. It also guarantees the eventual inclusion of all the transactions submitted to the blockchain and records them in its ledger. Bitcoin and Litecoin are examples of time-tested, reliable public blockchains that handle only one type of transaction – send/receive money (i.e., transfer-of-value) from one user to another. Whereas, general-purpose blockchains like Ethereum provide means and methods to encapsulate transfer of not only value but anything fungible that can be digitally represented with the help of programs aka smart contracts. However, with a high-level Turing-complete programming language to write smart contracts, Ethereum encountered vulnerabilities in its contracts thus forgoing the claim of immutability. On the contrary, script-based blockchains like Bitcoin and Litecoin have withstood the test of time and hence are perceived reliable – a very important aspect while managing assets like land. In this paper, we present a transfer-of-asset system for land management that borrows from Litecoin protocol its script, underlying consensus, and block structure. Our resultant system is a permissioned blockchain, where only a set of pre-approved miners can append land records to the blockchain. We introduce sidechains that are roped in a mainchain. The mainchain stores land records, which can be queried by citizens; whereas, sidechains hold intricate details about intermediate validations performed by regulators, registrars, and notaries. The process of land management used in this paper is a typical process in the states of India. Our system can be used to manage any asset class that is finite in nature. Our approach provides transparency of transactions at a higher level and privacy to individual transactions.
A Solution for Goal-oriented Policy Refinement in NFV Management and Orchestration Systems

In this thesis, we propose an automated policy refinement procedure for NFV-MANO systems based on planning and description logic reasoning. It is implemented in ATOM, a framework that enables NFV-MANO to record high-level goals and performs a fully automated policy refinement that derives enforceable rules to govern NFV-MANO behavioral choices. Besides, ATOM provides a semantic verification system to find inconsistencies among policies. Experiments have shown that our solution scales well considering realistic scenarios with different sizes.
Uncertainty Based Hybrid-intelligence Multi-branch Wargaming

Uncertainty of war challenges command decision making, especially in the pre-war preparation stage. In newly proposed operational concepts, uncertainty is taken as a means to limit the opponent's decision-making. How to help commanders analyze and utilize uncertainty has become a technical challenge that must be overcome to win the future combats. In this paper, a method called hybrid-intelligence multi-branch wargaming was proposed. Through combination of human intelligence in form of knowledge, and machine intelligence trained by reinforcement learning, the method realized new functions of COA evaluation and optimization analysis. Its feasibility and effectiveness has been verified through prototype development and experiments. It provides a short-term feasible way for the application of artificial intelligence technology in the field of command decision-making.
Natural Landscape-Friendly Animation Display Technique Using Shape-Changing Artificial Grass System

This paper introduces a display technique to show gradation animation using a shape-changing artificial grass system. Recently, liquid crystal displays (LCDs) and projectors have been used in a natural public facility such as a park. However, these displays sometimes spoil natural scenery because they look like artificial materials. Our proposed method is useful to develop a natural landscape-friendly display. We develop a grass pixel system to display the gradation. The grass pixel consists of yellow and green grass, and the length of the green grass moves vertically. The grass pixel uses a pin-based shape-changing system using a stepper motor to control the grass length dynamically. We evaluated the gradation of the grass pixel through an image processing of HSV (Hue, Saturation, Value of Brightness) values. In the measurement, the pixel system moved the grass length from minimum to maximum, and we took images of the running grass pixel from several angles. As a result, we observed that the HSV values changed linearly with the movement of the length of the green grass. We also developed a 3 × 3 pixels grass display, which can show several gradation animations as demonstrations.
A Grey-box Model for Estimating Nonlinear SNR in Optical Networks Based on Physics-guided Neural Networks

Based on physics-guided neural network, we design the features and loss function for estimating nonlinear SNR. With 1603 examples from simulations, a shallow neural network can have a higher accuracy and a better physical consistency.
Design of Shift-, Scale- and Rotation Invariant Diffractive Optical Networks

We investigate the sensitivity of diffractive optical networks to random object translation, scaling and rotation operations, and present a deep learning-based training strategy to design shift-, scale- and rotation invariant diffractive networks.
ISSUES OF EMOTIONAL INSTABILITY IN THE CONTEXT OF STUDENTS’ EMOTIONAL HEALTH RESEARCH

The aim: The aim of the research was to investigate the issue of emotional health in the context of mental health of students, in particular, the emotional instability as a students&amp;apos; personal quality with regard to cognitive and empathic components. Materials and methods: In a comparative analysis of the study, 104 3rd-year students of the Faculty of Management and Media Communications of the Ukrainian Academy of Printing, were divided into two groups on the basis of gender (group №1 – females; group №2 – males). In the study, undertaken in 2019-2020, two questionnaires, namely “Problems of Emotional Health” and “Assessment of Empathic Compassion”, drawn up by a team of authors, were used. The comparative analysis was carried out with the use of the Mann–Whitney U test. Results: The study of emotional instability and competence in the context of the student&amp;apos;s mental health allows us to conclude that the cognitive component is the basis that promotes the development of social, regulatory, and empathic components, and the latter is the main motivator of a person’s activity. Conclusions: The research showed that high school students do not always have enough opportunities for independent formation and emotional competence development, and have problems with emotional instability. These problems may be solved by the introduction of the educational component, in particular: introducing relevant special courses into the educational process, conducting psychological training aimed at the development of emotional professional competence in the context of the formation of personality’s mental health.
Spatial Locality Based Identifier Name Recommendation

Identifier names are used to represent concepts in the source code. Concise and consistent identifier names are crucial to program comprehension. Identifier names reduce the effort to understand the software, support software maintenance and improve source code quality. Despite these benefits, many software systems are known to have meaningless and inconsistent identifier names. One of the reasons that lead to inconsistent identifier names is lack of knowledge of identifier names already used to represent concepts in the software. To address this problem, this study proposes a new approach to automatically suggest part of identifier name. The approach aims to use spatial locality to identify and suggest next terms given identifier name prefix. Spatial locality, in this context, refers to the use of terms in close proximity of documents related to the software system. The performance of our proposed approach is evaluated using six open source software systems. The evaluation result shows that the spatial locality based approach suggests part of identifier names correctly with an average precision of 83.2% and average mean reciprocal rank (MRR) of 25.5%. Of the top four correct suggestions, more than half are ranked in the first and second place.
A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

In the past few years, with the continuous breakthrough of technology in various fields, artificial intelligence has been considered as a revolutionary technology. One of the most important and useful applications of artificial intelligence is face detection. The outbreak of COVID-19 has promoted the development of the noncontact identity authentication system. Face detection is also one of the key techniques in this kind of authentication system. However, the current real-time face detection is computationally expensive which hinders the application of face recognition. To address this issue, we propose a face verification framework based on adaptive cascade network and triplet loss. The framework is simple in network architecture and has light-weighted parameters. The training network is made of three stages with an adaptive cascade network and utilizes a novel image pyramid based on scales with different sizes. We train the face verification model and complete the verification within 0.15 second for processing one image which shows the computation efficiency of our proposed framework. In addition, the experimental results also show the competitive accuracy of our proposed framework which is around 98.6%. Using dynamic semihard triplet strategy for training, our network achieves a classification accuracy of 99.2% on the dataset of Labeled Faces in the Wild.
Performance Comparison of Fixed and Tracking Type Solar Plants

Solar energy, which is essential for all on earth, is clean and plentiful and can be transformed into electrical energy using photovoltaic (PV) systems. The generation of energy using different types of PV solar panel mountings viz. fixed, tracking, and adjustable, depends on a variety of factors such as sun intensity, relative humidity, cloud cover, and heat buildup. This paper reviews the various parameters which influence the performance of solar power plants. Further, the performance comparison of fixed and tracking PV systems shows that in comparison to the classical fixed-position PV systems, the tracking type of PV systems capture much more solar energy and thus produce substantially higher output power. Furthermore, consideration is also given to design variables which should be taken into account during the initial stage of engineering of a plant to achieve better performances and outcomes from the generation of a solar power plant.
Optimization For 3D Reconstruction Of Coronary Artery Tree By Two-stage Levenberg-Marquardt Algorithm

Three dimensional(3D) information acquisition and optimization of coronary artery tree is a key technology for the evaluation and diagnosis of cardiovascular disease. Based on the X-ray coronary angiography images in two directions, 3D reconstruction method establishes a projection model using the theory of perspective projection. In this study, on the basis of two-stage Levenberg-Marquardt(LM) algorithm, a novel method is employed for refinement of the 3D structure of vessel skeletons, considering deviation of angiography angle and distance. The first stage LM algorithm(LM1) optimizes the epipolar line matching error and the second stage LM algorithm(LM2) optimizes the back projection error. The experimental results show that this algorithm manages to obtain reconstruction model more accurately within a shorter time, verifying the efficacy of the proposed method.
Blockchain and Cloud Technology: Leading the ICT Innovations

Information and communication sector (ICT) is persistently transforming the manner of computing and provisioning of the Information Technology (IT) services and applications. Subsequently, the complexity, niceties and intricacies of the prevalent internet services and applications are also growing as well as changing. Eventually, cloud computing has emerged as one of the leading ICT innovations that has gained momentous importance owing to its benefits. Currently, blockchain technology is earning utmost prominence in the IT industry. The efforts are being initiated to merge the capabilities of both the cloud computing and blockchain technologies and yield utmost benefits from a collaborative work on these technologies. This paper attempts to lists the backend details for the technologies and discusses the benefits of them.
Catching cognitive biases in an erroneous decision making process

This paper proposes a logic-based model to study erroneous decision making by humans in accident reports. Our model is based on minimal belief revisions and forward chaining. It computes possible mental states that could explain the operator’s behavior. From this sequence, we extract logical patterns that correspond to possible cognitive biases responsible for the erroneous decision making. We apply this model on the crash of Air France’s 447 flight in 2009.
Improving Efficient Semantic Segmentation Networks by Enhancing Multi-scale Feature Representation via Resolution Path Based Knowledge Distillation and Pixel Shuffle

Multi-resolution paths and multi-scale feature representation are key elements of semantic segmentation networks. We develop two techniques for efficient networks based on the recent FasterSeg network architecture. One is to use a state-of-the-art high resolution network (e.g. HRNet) as a teacher to distill a light weight student network. Due to dissimilar structures in the teacher and student networks, distillation is not effective to be carried out directly in a standard way. To solve this problem, we introduce a tutor network with an added high resolution path to help distill a student network which improves FasterSeg student while maintaining its parameter/FLOPs counts. The other finding is to replace standard bilinear interpolation in the upscaling module of FasterSeg student net by a depth-wise separable convolution and a Pixel Shuffle module which leads to 1.9% (1.4%) mIoU improvements on low (high) input image sizes without increasing model size. A combination of these techniques will be pursued in future works.
Flower Pollination Algorithm Based on Beetle Antennae Search Method

In this paper, a new flower pollination algorithm based on beetle antennae search method (TBFPA) is proposed to deal with the slow convergence speed problem of traditional flower pollination algorithm. Specifically, after the new individuals are generated by the way of flower pollination algorithm, the beetle antennae search method is used to search the nearby solution space of the new individuals, thus increasing the probability to reach better solutions and speeding up the convergence speed of the algorithm. In addition, a new uniform distribution sampling method is used to generate the initial population so as to spread the initial solutions all over the solution space. In order to verify the effectiveness of TBFPA, different standard functions are tested between TBFPA and FPA algorithms and the results proved that TBFPA has faster convergence speed and stronger global searchability.
Path Planning of Bio-inspired Swarm of AUVs using Distributed Path Consensus algorithm

This paper uses the multi agent system (MAS) as the bioinformatics-inspired technique for guiding a team of autonomous underwater vehicles (AUVs) toward desired destination.This problem mimics the team behaviour towards achieving a common target.Here, each AUV estimates the position of the neighbour AUVs while moving towards the destination.An individual AUV is designated as an agent connected by a communication network and assumes full communication.The proposed multi-AUV system constitutes of a leader AUV and three follower AUVs.A distributed path consensus (DPC) is proposed that determine the distance constraint to ensure the neighbouring agent AUVs must maintain a predefined distance between each other while moving towards the respective destinations.Due to the proposed distance constraint, AUVs stays at a safe distance from each other and also from the static obstacles while maintaining underwater communication using interactive switching topology.The performance of the optimized path is obtained using MATLAB simulation.From the obtained results, it is observed that the proposed control algorithm provides effective co-operative motion control of multiple AUVs along the desired paths and avoid obstacles successively.The proposed method solves coordination problem among multiple AUVs and increase the coverage of underwater missions like oceanographic surveys.
COVID-19: The Future of African Development Systems

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An Archival Repository of Archival Repositories: integrating metadata in Archives Portal Europe

Since 2009, in parallel with the creation of national or local research portals, countries around Europe have started to design a system to integrate these online allowing to access Europe’s archival heritage through one single entry point. In 2012, after three years of research, Archives Portal Europe (APE) opened up to all institutions holding archival material about Europe. Today, the portal enables new types of digital archival research, freed from geographical limitations and based on cross-country comparison and multilingualism. At the same time, establishing a common basis for the central services of the portal to allow for the searching, processing, and displaying of the archival material of thousands of institutions comes with important technical (as well as political) challenges. This paper presents challenges and solutions adopted by Archives Portal Europe as a multi-level international aggregator.