Reviewerly/exHarmony huggingface dataset:
Making Knowledge Management Research more Scientific, Relevant, and Engaged: A Comparative Study of Academic ECKM Papers

The purpose is to analyse and compare all the academic papers in the proceedings of the European Conference on Knowledge Management (ECKM) in 2017 (Barcelona), 2018 (Padua), 2019 (Lisbon), and the digital conference in 2020 (Coventry). The methodology is to code and classify 440 papers and use five contemporary science frameworks to describe and analyse the papers. The theoretical implication of contemporary KM is a research field without common paradigms, domains, and perspectives without accumulating knowledge. The KM researchers do not understand the nature of knowledge management as a field where the research cannot be replicated, synthesized, or theorized. Knowledge management needs to move along from the empirical research paradigm to a clarified subjectivity and action-basedresearch. The criticism implying acceptable/unacceptable solutions and constructed adequate/inadequate solutions for corporations and societies have strengthened their place, offering new paradigms and perspectives. The way to do this is to let in controversial, greener, and sustainable studies, whatever objectivity or subjectivity the studies have. We need more actual problem focused and less knowledge and instrument focused studies. KM will have a higher responsibility for sustainability and greener corporations and the possibility of accumulating knowledge into replication and synthesizing for general knowledge. The rate of tested and replicated studies is for the four conferences zero. The tested part, but not replicated, is 80%. The rate of untheorized untheorizable concepts is zero, the rate of theorized but not synthesized studies is zero, while the number of synthesized, theorized, and conceptual studies is around 20%. To become a discipline or research domain KM needs to replicate both empirical and conceptual studies. The only way to accumulate knowledge is through replication giving paradigms for verification and falsification. To move ahead for better quality in the research, we must break free from the empirical and materialistic paradigms and move into the clarified subjectivity and action paradigm. Paradigmatic ecumenism will tend to a fiercer but idea-generating debate. This pluralistic approach will give more engaged practical research representing more sustainable societies and businesses. ECKM is on the road to include more pluralistic perspectives upon sustainability, value creation, gender issues, and the design of future knowledge work. There is a critical openness toward these issues making ECKM 2020 a more relevant conference than the ECKM conferences in 2017, 2018, and 2019. The 2020 conference more open up for reflections, dialogues, and criticism upon existing problems and knowledge asking about what is the adequate actual KM solutions.
Infrastructure Security

This chapter deals with the infrastructure security behind your Snowflake account. This is mainly about things that are good to know, or important decisions to make, as opposed to things that you should take action about on an ongoing basis. We believe that it’s important to also discuss the infrastructure security parts that you don’t have control over, so you can understand what’s happening under the hood, so you can answer questions that may arise during security assessments.
ACTIVE LEARNING OF STUDENTS – NEED OR NECESSARY IN ONLINE TEACHING

The subject of research is the concept of active learning by students. The aim is to demonstrate its value and the possibility of using it in a pandemic of COVID 19 to a digital learning environment, in online teaching. The values inherent in this concept, in the construction of own knowledge by students, were analyzed. This is confirmed by the results of empirical research as well as in the practice of education. The use of this concept by teachers in a digital learning environment requires increased effort, but its effects increase the quality of the desired learning outcomes by students. Therefore, in the conditions of a pandemic of COVID 19, these actions are not only need but necessary.
Bounds on isolated scattering number

The isolated scattering number is a parameter that measures the vulnerability of networks. This measure is bounded by formulas depending on the independence number. We present new bounds on the isolated scattering number that can be calculated in polynomial time. References Z. Chen, M. Dehmer, F. Emmert-Streib, and Y. Shi. Modern and interdisciplinary problems in network science: A translational research perspective. CRC Press, 2018. doi: 10.1201/9781351237307 P. Erdős and T. Gallai. On the minimal number of vertices representing the edges of a graph. Magyar Tud. Akad. Mat. Kutató Int. Közl. 6 (1961), pp. 181–203. url: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.7468 J. Harant and I. Schiermeyer. On the independence number of a graph in terms of order and size. Discrete Math. 232.1–3 (2001), pp. 131–138. doi: 10.1016/S0012-365X(00)00298-3 E. Korach, T. Nguyen, and B. Peis. Subgraph characterization of red/blue-split graph and Kőnig Egerváry graphs. Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithms. ACM, New York, 2006, pp. 842–850. doi: 10.1145/1109557.1109650 F. Li, Q. Ye, and Y. Sun. Proceedings of the 2016 Joint Conference of ANZIAM and Zhejiang Provincial Applied Mathematics Association, ANZPAMS-2016. Ed. by P. Broadbridge, M. Nelson, D. Wang, and A. J. Roberts. Vol. 58. ANZIAM J. 2017, E81–E97. doi: 10.21914/anziamj.v58i0.10993 F. Li, Q. Ye, and X. Zhang. Isolated scattering number of split graphs and graph products. ANZIAM J. 58.3-4 (2017), pp. 350–358. doi: 10.1017/S1446181117000062 E. R. Scheinerman and D. H. Ullman. Fractional graph theory. Dover Publications, 2011. url: https://www.ams.jhu.edu/ers/wp-content/uploads/2015/12/fgt.pdf S. Y. Wang, Y. X. Yang, S. W. Lin, J. Li, and Z. M. Hu. The isolated scattering number of graphs. Acta Math. Sinica (Chin. Ser.) 54.5 (2011), pp. 861–874. url: http://www.actamath.com/EN/abstract/abstract21097.shtml M. Xiao and H. Nagamochi. Exact algorithms for maximum independent set. Inform. and Comput. 255, Part 1 (2017), pp. 126–146. doi: 10.1016/j.ic.2017.06.001
Security of Healthcare Data Using Blockchains

Advances in the healthcare sector are entering into a new era in the form of Health 4.0. The integration of innovative technologies like Cyber-Physical Systems (CPS), Big Data, Cloud Computing, Machine Learning, and Blockchain with Healthcare services has led to improved performance and efficiency through data-based learning and interconnection of systems. On the other hand, it has also increased complexities and has brought its own share of vulnerabilities due to the heavy influx, sharing and storage of healthcare data. Its protection from cyberattacks along with privacy preservation through authenticated access is one of the significant challenges for the healthcare sector. For this purpose, the use of blockchain-based networks can lead to a considerable reduction in the vulnerabilities of the healthcare systems and secure their data. This chapter explores blockchain’s role in strengthening healthcare data security by answering the questions related to what data to use, when we need, why we need, who needs, and how state-of-the-art techniques use blockchains to secure healthcare data. As a case study, we also explore and analyze the state-of-the-art implementations for blockchain in healthcare data security for the Covid-19 pandemic. In order to provide a path for the direction of future research, we identify and discuss the technical limitations and regulatory challenges associated with blockchain-based healthcare data security implementation.
MFAUNet: Multiscale feature attentive U‐Net for cardiac MRI structural segmentation

The accurate and robust automatic segmentation of cardiac structures in magnetic resonance imaging (MRI) is significant in calculating cardiac clinical functional indices, and diagnosing heart diseases. Most U-Net based methods use pooling, transposed convolution, and skip connection operations to integrate the multiscale features for improved segmentation in cardiac MRI. However, this architecture lacks adequate semantic connection between the channel and spatial information, and robustness in segmenting objects with significant shape variations. In this paper, a new multiscale feature attentive U-Net for cardiac MRI structural segmentation method is proposed. An attention mechanism is adopted after concatenating the multi-level features to aggregate different scale features and determine on which features to focus. Cascade and parallel dilated convolution is also employed in the decoder blocks and skip connection is employed to enhance the ability of sensing receptive fields for multiscale context information. Furthermore, deep supervision approach with a loss function that combines the dice and cross-entropy losses to reduce overfitting and ensure better prediction is introduced. The proposed method was evaluated on three public cardiac datasets. The experimental results indicate that the method achieved competitive segmentation performance with the three datasets, which verifies the robustness and generalisability of the proposed network. In comparison with conventional U-Net methods, the model leverages attention mechanism and dilated convolution block, which increases the semantic connection between the channel and the spatial information, and improves the robustness of the right ventricle segmentation performance. From the view of the Dice scores and segmentation results, the multiscale feature attentive U-Net method is one of effective methods in segmenting cardiac MRI structures.
Azure Quantum

Microsoft offers a hugely popular cloud platform that listens to the name Azure or Microsoft Azure. Over the past decade, Azure has grown to one of the most popular cloud platforms and offers a wide range of IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service) offerings.
Impact of AWGN estimation on classification performance of AMC algorithms based on higher order cumulants

Automatic modulation classification (AMC) represents technique for modulation format recognition of a priori unknown signals, with wide usage in a numerous wireless systems and applications. In this paper, performance of AMC algorithms based on higher order cumulants was explored, since low computational complexity makes them be a very attractive from practical point of view. In order to additionally explore practical aspects of these algorithms, they were considered under conditions of Additive White Gaussian Noise (AWGN) with unknown noise variance, in channel with AWGN only, along with the multipath channel. Monte Carlo simulations were executed for performance tests, with AMC features expressed through probability of correct classification of modulation formats. In this way, impact of using estimated noise variance, instead of a priori known noise variance, on performances of AMC algorithms was presented and discussed, confirming their potential for practical application.
Inverted Topp-Leone Distribution: Contribution to a Family of J-Shaped Frequency Functions in Presence of Random Censoring

In this paper, Bayesian and non-Bayesian estimation of the inverted Topp-Leone distribution shape parameter are studied when the sample is complete and random censored. The maximum likelihood estimator (MLE) and Bayes estimator of the unknown parameter are proposed. The Bayes estimates (BEs) have been computed based on the squared error loss (SEL) function and using Markov Chain Monte Carlo (MCMC) techniques. The asymptotic, bootstrap (p,t), and highest posterior density intervals are computed. The Metropolis Hasting algorithm is proposed for Bayes estimates. Monte Carlo simulation is performed to compare the performances of the proposed methods and one real data set has been analyzed for illustrative purposes.
Moving Sum Data Segmentation for Stochastic Processes Based on Invariance

The segmentation of data into stationary stretches also known as multiple change point problem is important for many applications in time series analysis as well as signal processing.Based on strong invariance principles, we analyze data segmentation methodology using moving sum (MOSUM) statistics for a class of regime-switching multivariate processes where each switch results in a change in the drift.In particular, this framework includes the data segmentation of multivariate partial sum, integrated diffusion and renewal processes even if the distance between change points is sublinear.We study the asymptotic behavior of the corresponding change point estimators, show consistency and derive the corresponding localization rates which are minimax optimal in a variety of situations including an unbounded number of changes in Wiener processes with drift.Furthermore, we derive the limit distribution of the change point estimators for local changes -a result that can in principle be used to derive confidence intervals for the change points.
Building the Python App

For many people getting into programming, one of their first issues is figuring out what they can build. Programming is seldom learned by just reading. For this reason, this book includes a sample Python project. In this chapter, you’ll learn more about the project.
The Oxford Handbook of Spectral Music

Abstract This handbook is currently in development, with individual articles publishing online in advance of print publication. At this time, we cannot add information about unpublished articles in this handbook, however the table of contents will continue to grow as additional articles pass through the review process and are added to the site. Please note that the online publication date for this handbook is the date that the first article in the title was published online. For more information, please read the site FAQs.
Enhancement to test case prioritization through object-oriented software architectural design

Numerous prioritization techniques have been developed taking into account time and cost constraints frequently encountered in software system development at the testing phase. Theoretically, it is possible to establish random predictive models, however, to obtain more precise results, it is important to consider the real context characteristics and the system scope to be evaluated. The main objective of this proposal is to develop a deterministic test case prioritization model, based on the object-oriented software architectural design. In the analysis process, graph theory is used to abstract the system architecture weighted with software metrics. The obtained results show that this approach can establish predictive models with greater precision and profitability in a practical area.
Ten fundamental problems for artificial intelligence: mathematical and physical aspects

Ten fundamental to-be-solved problems for current artificial intelligence research and development are proposed and analyzed from the mathematical and physical points of view. These 10 problems include: (1) the statistical foundation problem for big data analysis, (2) the algorithm problem for basic mathematical computation in big data circumstances, (3) the understanding of structures and properties of various data spaces, (4) the mathematical theories on deep learning, (5) the investigation on the optimal transportation problem under irregular constraints, (6) the problem of how to simulate learning methodology and learn in infinite functional spaces, (7) breaking through the prior hypotheses of machine learning, (8) realizing machine learning automation from selection to creation, (9) integrating knowledge inference and data learning, (10) intelligent search and AI chips.
Preprocessing Analysis on Medical Image Retrieval Using One-to-one Matching of SURF Keypoints

The development of medical image technology is increasing very rapidly. Moreover, medical image growth is predicted to grow exponentially over the past decade. Much research on medical images has been done, one of which is medical image retrieval using colors, textures, and keypoints matching. Generally, the keypoints matching approach is more reliable against spatial rotation. However, the medical images mostly contain blur objects and noise, which affect the performance of keypoints detection. This study proposes a preprocessing analysis on medical image retrieval using one-one matching of SURF keypoints. The image retrieval performance with and without preprocessing is compared on the IRMA 2009 dataset. This analysis proves that the use of preprocessing can improve the precision by 17.78%, 17.59%, and 17.92% on top-5, top-10, and top-15, respectively.
Wi-SUN Device Authentication using Physical Layer Fingerprint

This paper aims to identify Wi-SUN devices using physical layer fingerprint. We first extract physical layer features based on the received Wi-SUN signals, especially focusing on device-specific clock skew and frequency deviation in FSK modulation. Then, these physical layer fingerprints are used to train a machine learning-based classifier and the resulting classifier finally identifies the authorized Wi-SUN devices. Preliminary experiments on Wi-SUN certified chips show that the authenticator with the proposed physical layer fingerprints can distinguish Wi-SUN devices with 100 % accuracy. Since no additional computational complexity for authentication is involved on the device side, our approach can be applied to any Wi-SUN based IoT devices with security requirements.
One-shot Radar-based Gesture Recognizer for Fast Prototyping

Millimeter-Wave radar is a promising sensor for contactless, privacy concerned user scenarios, e.g. interacting computers with gestures. But training a robust gesture recognizer requires a large dataset, which hinders fast prototyping. Here we propose One-Shot Gesture Recognizer, namely to use only one trajectory sample as template to recognize gesture. Due to high ambiguity of gesture itself, we extended protractor3d, giving a closed-form solution regardless of position, scale and direction.
Semi-Automated Methods for BIBFRAME Work Entity Description

This paper reports an investigation of machine learning methods for the semi-automated creation of a BIBFRAME Work entity description within the RDF linked data editor Sinopia (https://sinopia.io). The automated subject indexing software Annif was configured with the Library of Congress Subject Headings (LCSH) vocabulary from the Linked Data Service at https://id.loc.gov/. The training corpus was comprised of 9.3 million titles and LCSH linked data references from the IvyPlus POD project (https://pod.stanford.edu/) and from Share-VDE (https://wiki.share-vde.org). Semi-automated processes were explored to support and extend, not replace, professional expertise.
Analysis and Comparison of Natural Language Processing Algorithms as Applied to Bitcoin Conversations on Social Media

Institutional interest in bitcoin has grown significantly since its “bubble highs” in 2017; and given its lack of traditional fundamentals, investor sentiment is likely to drive price discovery. As social media has grown, Reddit has evolved to host significant conversations regarding bitcoin (‘r/bitcoin’) and general investing (‘r/investing’). We focus on these two channels using four distinct natural language processing algorithms to create an ensemble sentiment score and measure the relationship of the score to the change in bitcoin’s price.
Fuzzy logic obstacle avoidance by a NAO robot in unknown environment

In the current scenario, among all robots, humanoid robots are of greater importance due to their adaptability to human environment and human-like appearance. Obstacle avoidance is a crucial task for mobile robots and especially for humanoid robots. In this paper, an obstacle avoidance method based on fuzzy inference system is developed. The proposed system is tested on a real humanoid robot NAO V6. The results show that our method is more efficient than the default obstacle avoidance system of our humanoid robot and that our system takes into account several possible directions. Future developments will take into account these results with other systems in order to obtain an autonomous robot in terms of navigation.
COVID-19 and Primary and Secondary Education: The Impact of the Crisis and Public Policy Implications for Latin America and the Caribbean

More than 144 million students in Latin America and the Caribbean have missed nearly five months of school due to public health measures taken by governments in response to the COVID-19 pandemic. The health crisis has meant a triple shock for children and adolescents, with the prolonged closure of schools, confinement due to lockdown measures and the loss of economic security in households. This triple shock has both short- and long-term repercussions that put the development of an entire generation at risk. Although governments throughout the region have implemented distance learning strategies intended to maintain a degree of continuity in children’s and adolescents’ learning and well-being, these solutions have been unevenly implemented and may even further exacerbate the education gaps that existed in the region before the pandemic. Addressing this educational emergency requires governments to focus on guaranteeing children’s and adolescents’ learning and well-being, working on four priority areas: 1) planning for the urgent reopening of schools; 2) developing a strategy to ensure learning for all students, in the new context where not all instruction will be in person; 3) preserving school’s protective role and providing services that have been disrupted; and 4) ensuring the emotional well-being of the educational community (teachers, families and students). Implementing these measures promptly requires the protection of education budgets in the region, promoting cooperation between countries, and coordination between the education sector and other sectors. This crisis could be an opportunity to rethink the current education system and build one that closes existing inequalities and enables all children and adolescents in the region to reach their full potential. Achieving this will require a longterm vision for managing the current emergency, with investment in rebuilding an education system that ensures access to learning for all students, particularly the most vulnerable.
Managing Health Insurance Using Blockchain Technology

Blockchain technology has drawn considerable attention from various stakeholders. It is an obvious fact that blockchain finds implementation in nearly all aspects of the business. Many experts expect that this technology, like insurance, will threaten financial services. The number of blockchain insurance applications is growing every day and it is clear that every major player in the space will use them in some way within the next few years. The potential of blockchain to provide complete accountability, transparency and superior protection will help insurers save time and money, as well as enhance customer satisfaction. It provides huge opportunities for both FinTechs and smaller insurance firms ready to accept the transition. This chapter presents an overview of blockchain, the role that blockchain will play in the insurance sector and its benefits. The chapter then discusses building a blockchain-based insurance application using Ethereum/Hyperledger.