Reviewerly/exHarmony huggingface dataset:
Online strategies for coverage of uncertain terrains using an autonomous mobile robot

Coverage path planning (CPP) algorithms customize an autonomous robot’s trajectory for various applications. In surveillance and exploration of unknown environments, random CPP can be very effective in searching and finding the objects of interest. Robots with random-like search algorithms need to be unpredictable in their motion and simultaneously scan an uncertain environment, avoiding intruders and obstacles in their path. Inducing chaos into the robot’s controller system makes its navigation unpredictable, accounts for better scanning coverage, and avoids any hurdles (obstacles and intruders) without the need for a map of the environment. The unpredictability, however, will come at the cost of increased coverage time. Due to the associated challenges, previous studies have ignored the coverage time and focused instead on the coverage rate only. This paper establishes novel methods that address the coverage time challenge of chaotic path planners. Initially, four chaotic state-of-the-art dynamic systems were selected for their fastest coverage time in an unknown environment of size 50 m × 50 m and greater. These systems were then coupled together and manipulated using 3 chaos control techniques and 2 obstacle avoidance techniques to achieve a fast coverage of the environment and simultaneously avoid obstacles. Considering the average coverage time for the manipulated systems, the outcome has been a technique that can cover 90% of an area, 53.4% less than the state-of-the-art systems.
MODELING OF INTERDISCIPLINARY PEDAGOGICAL INTERACTION IN THE UNIVERSITY

It is advisable to introduce the ideas of an interdisciplinary approach into the practice of teaching university disciplines on the basis of pedagogical interaction of teachers of various disciplines, which should take place at the "intersection" of three areas: the content-methodological areas of each of the disciplines and the pedagogical area of knowledge. The process of modeling interdisciplinary pedagogical interaction includes setting the modeling goal, choosing theoretical foundations, studying the structure of organization and coordination of methodological work at the university, conversations with representatives of the university leadership, analyzing university regulations and questioning representatives of the teaching staff. The article provides the author's definition of the concept of "interdisciplinary pedagogical interaction", a developed structural model of interdisciplinary pedagogical interaction and a description of its structural elements. The conceptual-target block contains conceptual foundations, goals, objectives and principles of interdisciplinary pedagogical interaction; the diagnostic and prognostic block is responsible for the methods used to ascertain the level of development of interdisciplinary interaction in a university; the content block takes into account the objects of interdisciplinary interaction and the classification of interdisciplinary relations; the organizational-activity block covers subjects, forms, and builds an algorithm for the procedure of interdisciplinary pedagogical interaction; the resultative block describes the results of interdisciplinary pedagogical interaction.
Medical Image Segmentation using 3D Convolutional Neural Networks: A Review.

Computer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed.
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

The Backprop algorithm for learning in neural networks utilizes two mechanisms: first, stochastic gradient descent and second, initialization with small random weights, where the latter is essential to the effectiveness of the former. We show that in continual learning setups, Backprop performs well initially, but over time its performance degrades. Stochastic gradient descent alone is insufficient to learn continually; the initial randomness enables only initial learning but not continual learning. To the best of our knowledge, ours is the first result showing this degradation in Backprop's ability to learn. To address this issue, we propose an algorithm that continually injects random features alongside gradient descent using a new generate-and-test process. We call this the Continual Backprop algorithm. We show that, unlike Backprop, Continual Backprop is able to continually adapt in both supervised and reinforcement learning problems. We expect that as continual learning becomes more common in future applications, a method like Continual Backprop will be essential where the advantages of random initialization are present throughout learning.
Effective connections of $a_\mu$, Higgs physics, and the collider frontier

We consider scalar extensions of the SM and their effective field theoretic generalisations to illustrate the phenomenological connection between precision measurements of the anomalous magnetic moment of the muon $a_\mu$, precision Higgs measurements, and direct collider sensitivity. To this end, we consider charged BSM scalar sectors of the Zee-Babu type for which we develop a consistent, and complete dimensions-5 and -6 effective field theory extension. This enables us to track generic new physics effects that interact with the SM predominantly via radiative interactions. While the operator space is high dimensional, the intersection of exotics searches at the Large Hadron Collider, Higgs signal strength and anomalous muon magnetic measurements is manageably small. We find that consistency of LHC Higgs observations and $a_\mu$ requires a significant deformation of the new states' electroweak properties. Evidence in searches for doubly charged scalars as currently pursued by the LHC experiments can be used to further tension the BSMEFT parameter space and resolve blind directions in the EFT-extended Zee-Babu scenario.
Can coronavirus linger on Amazon packages

People are wondering if their packages could be carrying coronavirus after an Amazon warehouse employee tested positive for the illness.
Implementing a new data acquisition system for the advanced integrated atmospheric observation system KITcube

&amp;lt;p&amp;gt;KITcube (Kalthoff et al, 2013) is a mobile advanced integrated observation system for the measurement of meteorological processes within a volume of 10x10x10 km&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt;. A large variety of different instruments from in-situ sensors to scanning remote sensing devices are deployed during campaigns. The simultaneous operation and real time instrument control needed for maximum instrument synergy requires a real-time data management designed to cover the various user needs: Save data acquisition, fast loading, compressed storage, easy data access, monitoring and data exchange. Large volumes of data such as raw and semi-processed data of various data types, from simple ASCII time series to high frequency multi-dimensional binary data provide abundant information, but makes the integration and efficient management of such data volumes to a challenge.&amp;lt;br&amp;gt;Our data processing architecture is based on open source technologies and involves the following five sections: 1) Transferring: Data and metadata collected during a campaign are stored on a file server. 2) Populating the database: A relational database is used for time series data and a hybrid database model for very large, complex, unstructured data. 3) Quality control: Automated checks for data acceptance and data consistency. 4) Monitoring: Data visualization in a web-application. 5) Data exchange: Allows the exchange of observation data and metadata in specified data formats with external users.&amp;lt;br&amp;gt;The implemented data architecture and workflow is illustrated in this presentation using data from the MOSES project (http://moses.eskp.de/home).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;References:&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;KITcube - A mobile observation platform for convection studies deployed during HyMeX&amp;amp;#160;&amp;lt;/strong&amp;gt;.&amp;lt;br&amp;gt;Kalthoff, N.; Adler, B.; Wieser, A.; Kohler, M.; Tr&amp;amp;#228;umner, K.; Handwerker, J.; Corsmeier, U.; Khodayar, S.; Lambert, D.; Kopmann, A.; Kunka, N.; Dick, G.; Ramatschi, M.; Wickert, J.; Kottmeier, C.&amp;lt;br&amp;gt;2013. Meteorologische Zeitschrift, 22 (6), 633&amp;amp;#8211;647. doi:10.1127/0941-2948/2013/0542&amp;amp;#160;&amp;lt;/p&amp;gt;
Sistem Pakar Dalam Mengidentifikasi Kenaikan Pangkat Pegawai Negeri Sipil Menggunakan Metode Backward Chaining

Promotion can be interpreted as an element in enforcing the career of Civil Servants (PNS). The promotion to the rank of Civil Servants (PNS) is in the form of an award for work achievements that have been achieved and service to the country after fulfilling certain conditions. At this time there are still many Civil Servants (PNS) who do not understand employee governance such as this promotion and there are still many who do not know what are the completeness of promotions and do not know whether a Civil Servant (PNS) can be promoted. or not. This study aims to make Civil Servants (PNS) know whether it is proven or not to be able to be promoted based on certain conditions that must be met for promotion. The data processed in this study were directly directed by experts. The data is sourced from the staffing of the Regional Office of the Ministry of Religion of West Sumatra Province. The promotion data is processed and developed using an expert system built using PHP programming and MySQL database. In the Expert System in identifying promotions for Civil Servants using the Backward Chaining method, the appropriate and suitable results between expert data and tracking results are very good, so that promotions can be identified properly. It is hoped that the application that has been built in this research can be useful for Civil Servants (PNS) in identifying promotions, and to provide information about promotions.
From and individual to a collaborative culture inthe educational institution: professional development and management

This article has been written in the frame of the research project “Design and evaluation of strategies for professional growth of teachers in order to achieve a collaborative culture in schools” (Fondecyt Project No 1000331). It presents a theoretic analysis of organizational culture in school centers from the point of view of teachers’ education and management. The authors are centered in establishing three fields of analysis: tendencies in teachers education, conceptualization of “organizational culture”, and presentation of a methodology to promote change towards the development of a collaborative culture. The analysis pretends to set the basis for administrators and educational professionals to built together collaborative cultures in educational centers.
DESIGN AND BUILD LEARNING MEDIA FOR PNEUMATIC AND HYDRAULIC ANIMATION BASED ON ANDROID

Pneumatic and hydraulic learning media are ussualy used sofware that intergated on a computer device. Android based learning media can be use as an alternative media that can fullfill the limitation of computer based media. This study aims to produce and to determine the feasibility of android based learning animation media in pneumatic and hydraulic subjects, especially pneumatic components. Analysis, Design, Development, Implementation, Evaluation (ADDIE) is used as a research method in producing learning media. The respondend are students of the Department of Mechanical Engineering Education who have passed and are currently contracting pneumatic and hydraulic courses. Judgment of instructional media is carried out by material experts and media experts. The results shows that the android based learning media are feasible to be use in the learning activities with the judgment score from media and subject experts are 89,5% and 94%, respectively. Moreover, student respondends were also give good score with the presentase 84,8%.
Penerapan Algoritma Fuzzy Tahani Untuk Rekomendasi Penerima Beasiswa Peningkatan Prestasi Akademik

Relational database systems that exist until now are only able to handle data that is definite (crisp), deterministic and precise. In fact, in real conditions, vague data is often needed for the decision-making process. For decision making involving fuzzy variables based on crisp data in the database, you can use a query on the database system with the concept of fuzzification on the data. In every educational institution, especially universities, there are several types of scholarships given to students. To get a scholarship, students must meet all the requirements that have been set. This study discusses the application of the Fuzzy Tahani algorithm for the recommendation of Academic Achievement Improvement (PPA) scholarship recipients at Bumigora University, Mataram. Data for PPA scholarship recipients was used in 2014 with details of the number of registrants 64 people and recipients (quota) of 15 people. Every year the number of applicants for this scholarship is increasing, while the processing and selection process is still done semi-manually so that the expected results are less than optimal, especially in terms of transparency and distribution. There are several variables that must be calculated by PPA scholarship recipients, namely the value of the Grade Point Average (GPA), Parents' Income, Number of Dependent Parents and Number of Diplomas. From the results of trials conducted in this study, it can be seen that the system's accuracy level reaches a value of 73.3%. This value is obtained by comparing the results of the semi-manual selection of PPA scholarship recipients with the results of the PPA scholarship selection using a system that uses the Fuzzy Tahani Algorithm.
ANOMALYMAXQ:Anomaly-Structured Maximization to Query in Attributed Network

The detection of anomaly subgraphs naturally appears in various real-life tasks, yet label noise seriously interferes with the result. As a motivation for our work, we focus on inaccurate supervision and use prior knowledge to reduce effects of noise, like query graphs. Anomalies in attributed networks exhibit structured-properties, e.g., anomaly in money laundering with "ring structure" property. It is the main challenge to fast and approximate query anomaly in attributed networks. We propose a novel search method: 1) decomposing a query graph into stars; 2) sorting attributed vertices; and 3) assembling anomaly stars under the root vertex sequence into near query. We present ANOMALYMAXQ and perform on 68,411 company network (Tianyancha dataset),7.72m patent networks (Company patents) and so on. Extensive experiments show that our method has high robustness and fast response time. When running the patent dataset,the average running time to query the graph once is about 252 seconds.
A de Finetti Style Representation Theorem for Arbitrary Finite Sequences of Random Variables

We prove that an arbitrary finite sequence of random variables can be represented as a signed mixture of simpler sequences, which we call i.i.d-rooted sequences. An i.i.d-rooted sequence is obtained as a permutation of an i.i.d. sequence, where the permutation is allowed to be random and data-dependent (for example, sorting the i.i.d. random variables into increasing order). Examples demonstrate that not all random sequences are i.i.d.-rooted, so the representation theorem indeed reduces a general finite sequence to a mix of special ones. We also provide a characterization of i.i.d-rooted sequences, which allows the efficient testing whether a given sequence has the property. To present the material in a self-contained way, we briefly review the background and history of this type of decomposition.
LibGuides: Oxford English Dictionary: Resources for social media

A selection of videos and images that can be downloaded to use on your own social media channels Images and videos for you to use on social media
The Transformation and Remodeling of Literary Education Concept Under Mobile Internet Era

Literature education occupies an important position in the strategic training of college talents, and the concept of literature education determines the true effect of literature education. Under the guidance of advanced literary education concepts, literary education can not only improve students’ literacy in basic aspects such as characters, words, sentences, and texts. Moreover, it enables students to absorb rich spiritual food and cultivate their own sentiments in literary works, and constantly improve their own literary taste and literary literacy, thereby enhancing the core competitiveness of college talents.
How accurate is our memory?

Our memories often feel like records of the past. But the dominant view among scientists is that our personal memories are highly prone to error and not to be trusted. How accurate is memory, really? No need to be pessimistic – our study suggests that memory, while not a perfect record, is much more accurate than scientists think. - submission by Nicholas B. Diamond
XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking

Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a ML method that combines multiple hypotheses to form a single hypothesis used for prediction. A bagging algorithm combines multiple classifiers modeled on different sub-samples of the same data set to build one large classifier. Banks, and their retail banking activities, are nowadays using the power of ML algorithms, including decision trees and random forests, to optimize their processes. However, banks have to comply with regulators and governance and, hence, delivering effective ML solutions is a challenging task. It starts with the bank's validation and governance department, followed by the deployment of the solution in a production environment up to the external validation of the national financial regulator. Each proposed ML model has to be validated and clear rules for every algorithm-based decision must be justified. In this context, we propose XtracTree, an algorithm capable of efficiently converting an ML bagging classifier, such as a random forest, into simple "if-then" rules satisfying the requirements of model validation. We use a public loan data set from Kaggle to illustrate the usefulness of our approach. Our experiments demonstrate that using XtracTree, one can convert an ML model into a rule-based algorithm, leading to easier model validation by national financial regulators and the bank's validation department. The proposed approach allowed our banking institution to reduce up to 50% the time of delivery of our AI solutions to the end-user.
Real-time Indian Sign Language (ISL) Recognition

This paper presents a system which can recognise hand poses & gestures from the Indian Sign Language (ISL) in real-time using grid-based features. This system attempts to bridge the communication gap between the hearing and speech impaired and the rest of the society. The existing solutions either provide relatively low accuracy or do not work in real-time. This system provides good results on both the parameters. It can identify 33 hand poses and some gestures from the ISL. Sign Language is captured from a smartphone camera and its frames are transmitted to a remote server for processing. The use of any external hardware (such as gloves or the Microsoft Kinect sensor) is avoided, making it user-friendly. Techniques such as Face detection, Object stabilisation and Skin Colour Segmentation are used for hand detection and tracking. The image is further subjected to a Grid-based Feature Extraction technique which represents the hand's pose in the form of a Feature Vector. Hand poses are then classified using the k-Nearest Neighbours algorithm. On the other hand, for gesture classification, the motion and intermediate hand poses observation sequences are fed to Hidden Markov Model chains corresponding to the 12 pre-selected gestures defined in ISL. Using this methodology, the system is able to achieve an accuracy of 99.7% for static hand poses, and an accuracy of 97.23% for gesture recognition.
DEVELOPMENT AND IMPROVEMENT OF SPEED AND POWER QUALITIES OF APPLICANTS FOR HIGHER EDUCATION WHO SPECIALIZE IN MARTIAL ARTS

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Search Re-Platforming – The Evolution of a New Platform

The Search Re-Platforming initiative is modernizing search for LexisNexis. Our vision is to increase search performance and release frequency to continuously provide value to New LexisNexis customers while providing development teams with an easily configurable search platform meeting market and product needs. We are building a best-in-class search experience, delivering dramatically faster search performance, and enabling a direct path to production delivery.We are overcoming the parity and relevance challenges of search by reducing the subjectivity of human testing and increasing the level of automated testing for all comparison points. By utilizing the Paved Road to Production, we have implemented a sophisticated continuous integration and deployment strategy that enables us, our partners, and consumers to develop and continually release improvements to our customers.
An Ensemble Model for Financial Statement Fraud Detection

Fraudulent financial statements are deliberate furnishing and/or reporting incorrect statistics, and this has become a major economic and social concern as the global market is witnessing an upsurge in financial accounting fraud, costing businesses billions of dollars a year. Identifying companies that manipulate financial statements remains a challenge for auditors, as fraud strategies have become increasingly sophisticated over the years. We evaluate machine learning techniques for financial statement fraud detection, particularly a powerful ensemble technique, the XGBoost algorithm, that help to identify fraud on a set of sample companies drawn from the MENA region. The issue of the class imbalance in the dataset is addressed by applying the SMOTE algorithm. We found that XGBoost algorithm outperformed other algorithms in this study: Logistic Regression (LR), Decision Tree (DT), Vector Machine Support (SVM), Adaboost, and RandomForest. The XGBoost algorithm is then optimised to obtain the optimum performance.