Reviewerly/exHarmony huggingface dataset:
From Theoretical to Practical Transfer Learning: The ADAPT Library

In traditional machine learning, the learner assumes that the training and testing datasets are drawn according to the same distribution. However, in most practical scenarios, the two datasets are drawn according to two different distributions, the source distribution and the target distribution. In this context, the use of classical machine learning algorithms often fails as models trained on the source data provide poor performances on the target data. To solve this problem, many transfer learning techniques have been developed following one of the three main strategies: parameter-based transfer, instance-based transfer and feature-based transfer. The choice of the appropriate strategy is mainly determined by the nature of the shift between the source and target distributions. For example, to deal with the problem of sampling bias, when part of the population is over- or under-represented in the training set, instance-based approaches are useful to adequately reweight the source data in the training phase. If the shift is caused by a change in data acquisition, such as sensor drift, feature-based methods help to correct the shift by learning a common feature representation for the source and target data. For a real application, it is really a challenge to choose in advance the best transfer learning strategy and one often needs to evaluate different models in practice. As the different transfer methods were introduced by various contributors, no common framework is today available for a rapid development. To tackle this issue, we propose a Python library for transfer learning: ADAPT (Awesome Domain Adaptation Python Toolbox), which allows practitioners to compare the results of many methods on their particular problem. ADAPT is an open-source library providing the implementation of several transfer learning methods. The library is suited for scikit-learn estimator objects (objects which implement fit and predict methods) and tensorflow models. It allows to evaluate very easily the benefits of transfer learning methods on real data. In this chapter, we propose to illustrate the different features of the ADAPT library on both synthetic and real datasets.
Algorithms for Probabilistic and Stochastic Subsequential Failure Transducers

This paper introduces a framework for building probabilistic models with subsequential failure transducers. We first show how various types of subsequential transducers commonly used in natural language processing are represented by conditional probabilistic subsequential transducers and probabilistic subsequential failure transducers. Afterwards, we introduce efficient algorithms for composition of conditional probabilistic subsequential transducers with probabilistic subsequential failure transducers and canonization (weight-pushing) of probabilistic subsequential failure transducers. Those algorithms are applicable to many tasks for representing probabilistic models. One such task is the construction of the [Formula: see text] weighted transducer used in speech recognition which we describe in detail. At the end, a comparison between the presented [Formula: see text] failure weighted transducer and the standard [Formula: see text] weighted transducer along with empirical results for HMM-based speech recognition decoding reveal the competitiveness of the presented constructions.
Towards a Fully Connected Many-User Entanglement Distribution Quantum Network Within Deployed Telecommunications Fibre-Optic Infrastructure

We present developments in entanglement distribution quantum networks towards a fully connected, scalable, many-user network, which is not limited to simple quantum key distribution protocols.
The Selective Regularization of a Linear Regression Model: A Recent Study

The construction of a linear regression model incorporating regularisation of the system matrix of normal equations is covered in this article. Only the matrix diagonal entries that correspond to the data with a high correlation are increased, as opposed to the conventional ridge regression, which adds positive parameters to all of a matrix's diagonal terms. This causes the matrix conditioning to decrease, which in turn causes the corresponding regression equation coefficients to decrease. Based on the triangular decomposition of the correlation matrix of the original dataset, selection of the entries to be increased. On a known dataset, the method's efficacy is evaluated using not only ridge regression but also the outcomes of applying the well-known algorithms LARS and Lasso.
Towards Formalization of Constructivist Seed AI

A general intelligent system is expected to solve wide range of problems and adapt across multiple varied environments. It has already been hypothesized that seed AI needs to be bootstrapped in a system which could evolve to handle multiple problem domains. Such a seed AI may consist of few core intelligent components and a seed program. However, there is no formal structural definition of seed AI in place. Thus, an abstract model of the seed AI is presented and its generality has been proved. The formal structure of such an algorithm has also been derived. It has been discussed how and in what setting the proposed model of seed AI can achieve different properties of an intelligent agent like adaptability, constructivism, learnability and recursive self-improvement. The proposed theoretical framework for seed AI can serve as the basis for construction of any artificial general intelligent system. A prototype of seed AI has been developed using universal search to demonstrate and present a guidance on physical implementation of an agent-based system. The agent has been experimented in a heterogeneous toy problem to illustrate its usability.
Social Media Insights About COVID-19 in Portugal: A Text Mining Approach

The rapid spread of COVID-19 around the world had a significant impact on daily life. As in other countries, measures were taken in Portugal to combat the exponential increase of cases, such as curfews and the use of masks. Thus, in parallel with the direct consequences on health and the healthcare sector, the pandemic also caused changes in human behavior from a sociological viewpoint.&#x0D; The objective of this dissertation is to attain a perception of the reality concerning COVID-19. For this purpose, real-time data was extracted from three sources, two of them being social media platforms – Twitter and Reddit – and the other one being Público, a Portuguese online newspaper. The adopted approach, based on topic modelling and sentiment analysis, was validated within the Portugal context, concerning data over a period of one year, but it can equally be employed in similar situations and other countries and provide decision-making support.&#x0D; After the data extracting, it was prepared for application of natural language processing (NLP) tools specific to the Portuguese language, which can represent a challenge due to the lexical richness. With the gathered information, a dashboard was built, with the purpose of gaining insights on the COVID-19 pandemic in Portugal. It was concluded that the topics discussed on social media reflect the events related to the pandemic. In a final stage, these dashboards were evaluated by public health experts, who highlighted the potential of the results obtained. The data and dashboards will be made available to the scientific community upon request.
A Parametric Test based Analysis of State Estimation Techniques under Data Uncertainties

This work examines the statistical analysis of conventional and evolutionary strategies used to solve state estimation problems. All energy management systems use state estimation to determine the operational condition of the system. Moreover, with the rise of the electrical market and the notion of a smart grid, the assessment of system parameters has received considerable attention. Hence an assessment of the efficiency and robustness of various state estimation techniques used to compute the system parameters is very much required. This paper primarily focuses on the parametric tests used to access and compare the robustness of various state estimators. Case studies are conducted on IEEE 6 bus and 14 bus systems. In addition, this paper also provides a statistical evaluation of the performance of evolutionary algorithms with varying upper and lower optimal solution constraints. Furthermore, the algorithms' robustness under conditions of missing and infringed data is also determined. The findings derived from these estimators are compared with the base values, and the percentage error in estimated values is computed and analysed.
Understanding ‘Distributed Ledger Technology’ and Its Potential as a Disruptive Technology for Climate Action

This chapter introduces the book and sets the scene in relation to which concepts are to be explored.
Realizing a Quantum-enabled Interconnect Between Microwave and Telecom Light

We present a quantum-enabled microwave-telecom interface with bidirectional conversion efficiencies up to 15% and added input noise quanta as low as 0.16. Moreover, we observe evidence for electro-optic laser cooling and vacuum amplification.
Development of the institute of mentoring within the framework of internal corporate training of personnel in a secondary school

The purpose of the study is to describe the conditions for the development of the institution of mentoring within the framework of internal corporate training of personnel in a secondary school. The scientific novelty consists in identifying the opportunities for internal corporate training for the development of the institution of mentoring, substantiating the focus of non-formal training of school personnel on ensuring the quality of education. The result of the study is a description of the conditions for the development of the institution of mentoring within the framework of internal corporate training at school: the organization of educational cooperation between teachers; voluntary participation of personnel in internal corporate training; ensuring the unity of the development of a general educational organization and the professional growth of its staff; continuous training of school staff according to the 60/20/20 principle.
VQE for the square-octagon-lattice Kitaev model [Slides]

hardware-native square-octagon qubit connectivity map, and examine the possibility of efficiently probing its rich phase diagram with VQE approaches. By benchmarking different choices of variational Ansatz states and classical optimizers, we illustrate the advantage of a mixed optimization approach using the Hamiltonian variational Ansatz (HVA) and the potential of probing the system's phase diagram using VQE. We further demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.
Noise reduction in quantum simulations of scalar QED via qudit encodings: Why physics has a qudit bias [Slides]

noise model for Google's experimental quantum computing platform and compare the results of noisy simulations with experiments for several quantum algorithms on Google's Quantum Computing Service.
Scheduling Strategy of Virtual Device Based on Wake-up and Semi-sleep Mode

In order to meet the request and response requirements of users and further improve the efficiency of the computer system, a new type of virtual device scheduling strategy is proposed by integrating the wake-up and semi-sleep mode. At the end of a semi-sleep period, if the number of user requests waiting in the buffer reaches the wake-up threshold, the virtual device will transition from the semi-sleep state to the wake-up state to serve user requests at a normal rate. Otherwise, the virtual device will restart a new semi-sleep period to continuously provide low-speed services for user. According to the principle of scheduling strategy, a multi-service desk queuing model with policies and multiple asynchronous work vacations is established. Using the quasi-birth-death process and the matrix geometric solution method, performance indicators such as the average sojourn time requested by users and the energy-saving rate of computer system are deduced. Comprehensive numerical analysis experiments and computer system simulation experiments are used to evaluate the computer system performance of the virtual device scheduling strategy.
CrossMark Applying on LENPUST

Lentera Pustaka: Jurnal Kajian Ilmu Perpustakaan, Informasi, dan Kearsipan is peer-reviewed, independently published, and open access journal devoted to advancing research, theory , and practice in the area of library and information management, information literacy, records, and information behaviour
Experimental Demonstration of Optical Stochastic Cooling [Slides]

future accelerators. First proposed nearly 30 years ago, optical stochastic cooling (OSC) replaces the conventional microwave elements of SC with optical-frequency analogues and is, in principle, compatible with any species of charged-particle beam. Here we describe a demonstration of OSC in a proof-of-principle experiment at the Fermi National Accelerator Laboratory’s Integrable Optics Test Accelerator. The experiment used 100-MeV electrons and a non-amplified configuration of OSC with a radiation wavelength of 950 nm, and achieved strong, simultaneous cooling of the beam in all degrees of freedom. This realization of SC at optical frequencies serves as a foundation for more advanced experiments with high-gain optical amplification, and advances opportunities for future operational OSC systems with potential benefit to a broad user community in the accelerator-based sciences.
Comparison of Bagging and Adaboost Methods on C4.5 Algorithm for Stroke Prediction

Stroke is a non-communicable disease and is very dangerous because of functional disorders of the brain caused by blockage of blood circulation. This disease is classified as a cerebrovascular disease because it requires treatment for 24 hours, if not treated quickly it can cause death. The purpose of this research is to overcome this problem is to create a machine learning-based prediction model for medical experts in dealing with diseases to help reduce the risk of death. The method applied for this research is to apply the C4.5 algorithm classification method as well as the bagging and Adaboost methods from Ensemble Learning. Stroke data is processed using 2 stages of data processing, namely the data cleaning stage and the data transformation stage. In this study, a comparison will be made between the C4.5 algorithm, the bagging method + the C4.5 algorithm and the Adaboost method + the C4.5 algorithm using the confusion matrix, k-fold cross validation and validation test based on the values of TP, TN, FP, FN, recall, precision, F1-Score and accuracy. The results of the classification test using the Confusion Matrix and k-fold cross validation for the C4.5 algorithm resulted in an accuracy of 92.87%. Then the accuracy of the C4.5 algorithm with the bagging method increased to 95.02% and when combined with the Adaboost method the accuracy value also increased to 94.63%. From these results, it can be said that a single classifier algorithm, namely the C4.5 algorithm with the bagging and Adaboost methods, has been proven to improve classification performance.
Best Alpha for Forecasting Stock using Brown's Weighted Exponential Moving Average

Stocks are a very profitable type of investment, one of the most profitable stock sectors is mining. PT. Aneka Tambang Tbk (ANTM) is the largest mining company in Indonesia engaged in metals and minerals. The problem that occurs in stock investment is the fluctuation of stock prices. Stock price fluctuations occur because of the process of buying and selling shares, rumors about the company, or certain policies made by the government that can affect the company. These rapid stock price fluctuations are very dangerous for investors because these fluctuations are not always directly proportional to the company's fundamental conditions. Therefore we need a system that can perform forecasting to monitor stock price fluctuations that occur. The author uses the Brown's Weighted Exponential Moving Average (B-WEMA) method for forecasting. The data used are 241 daily PT Aneka Tambang Tbk (ANTM) in the Covid-19 era, starting January 1, 2020 - December 31, 2020. The experiment was carried out into 3 scenarios, namely using 3 months, 6 months, and 12 months data. Alpha tested is 0.1 to 0.9 (1 digit behind the comma) while the number of moving averages is 3, 5, 10, 20, 50, 100, and 200 adjusted to the amount of data. From the experiment it was found that the best forecasting was obtained in scenario 2 with the best manual alpha value having a MAPE error value of 2.84% using an alpha of 0.9 and a moving average of 100.
Development of Android-Based E-Modules in Basic Computer and Networking Subjects for Class 10 Vocational High School Students

The use of android-based learning facilities as teaching materials for students is a solution to facilitate understanding of practicum material in online learning. This study aims to determine the development and feasibility of an Android-based E-module in computer and basic network subjects at SMK 1 Antarctica, Sidoarjo. This type of research is Research and Development (R&amp;D) using the Analysis, Design, Development, Implementation, Evaluation (ADDIE) development model. The feasibility test was carried out by media experts and material experts as well as the results of student responses, the results of the validity of this study obtained a percentage of 94% in the "Very Eligible" category for the media and a percentage of 92% with the "Very Eligible" category for the material, and a percentage of 81% with the category " Very Eligible” for the students' responses. Based on these results, it can be concluded that Android-based E-modules in basic computer and network subjects are very feasible to be used as alternative learning media.
First Experimental Demonstration of Optical Stochastic Cooling [Slides]

future accelerators. First proposed nearly 30 years ago, optical stochastic cooling (OSC) replaces the conventional microwave elements of SC with optical-frequency analogues and is, in principle, compatible with any species of charged-particle beam. Here we describe a demonstration of OSC in a proof-of-principle experiment at the Fermi National Accelerator Laboratory’s Integrable Optics Test Accelerator. The experiment used 100-MeV electrons and a non-amplified configuration of OSC with a radiation wavelength of 950 nm, and achieved strong, simultaneous cooling of the beam in all degrees of freedom. This realization of SC at optical frequencies serves as a foundation for more advanced experiments with high-gain optical amplification, and advances opportunities for future operational OSC systems with potential benefit to a broad user community in the accelerator-based sciences.
P- and S-wave 3D velocity models for the South American platform

ABSTRACT. With the installation and growth of the Brazilian Seismographic Network (RSBR) since 2011, it is now possible to detect and locate events with magnitudes lower than 3.5 mb for Brazil, and better study the local and regional seismicity. Nevertheless, most of these events are located using 1D velocity models that lead to larger uncertainties precluding any correlation with the known geological structures. The use of heterogeneous 3D velocity models better predicts the travel times and guarantees the accuracy of hypocenter location; however, there is not yet a calibrated 3D velocity model for the South American Platform (SAP). In this study, integrating multiple local and regional geological and geophysical knowledge and average velocities of the crust and mantle, we have elaborated 3D P- and S-wave velocity models for the SAP, including the Central Andes area (down to 900 km). We tested the model locating the well-known 1998 Andean Aiquile earthquake (Bolivia), 6.6 Mw, obtaining successful results when compared with its ground truth location. We also satisfactorily located two other recent important events of the SAP, using local and regional body wave arrivals recorded by the RSBR. Finally, S-wave arrivals of near stations helped to constrain the event depths to expected values.Keywords: heterogeneous velocity model, Brazilian seismicity; hypocenter location; NonLinLoc. P- and S-wave 3D velocity models for the South American platformRESUMO. Em decorrência da implantação da Rede Sismográfica Brasileira (RSBR) em 2011, tornou-se possível detectar e localizar eventos com magnitudes menores de 3,5 mb no Brasil e aprofundar o estudo da sismicidade local e regional. Porém, a maioria desses eventos são localizados utilizando-se modelos de velocidade 1D, resultando em incertezas que não permitem correlacionar os hipocentros com as estruturas geológicas conhecidas. Em contrapartida, o uso de modelos 3D de velocidade prediz melhor os tempos de percurso das ondas e garante precisão na localização de hipocentros; porém, ainda não haviam sido desenvolvidos modelos 3D calibrados para a plataforma Sul-Americana. No presente estudo, integrando-se informações geológica e geofísica, local e regional, e velocidades médias da crosta e manto foram elaborados modelos 3D de velocidade das ondas P e S para a Plataforma Sul-americana e para os Andes Centrais (até 900 km de profundidade). Para testar a confiabilidade dos modelos, foi localizado o evento andino de Aiquile 1998, 6,6 Mw (Bolívia) usando-se chegadas regionais da onda P, com resultados compatíveis com o melhor epicentro conhecido. Também foram localizados satisfatoriamente dois outros eventos importantes. Finalmente, o uso de leituras da onda S de estações próximas restringe a profundidade hipocentral para valores esperados em cada caso.Palavras-chave: modelo de velocidade heterogêneo; sismicidade brasileira; localização de hipocentros, NonLinLoc.
On transactional business logic design

The given work is devoted to the task of transactional business logic design. Additional layer responsible for transaction processing is introduced to make the architecture of the system more flexible and robust, testable and maintainable.
Hindi Language Sentiment Classification using Natural Language Processing Techniques

This paper has presented Hybrid Approach for determination of sentimental phrase or words from Hindi text automatically through use of Hindi sentiment’s lexicon and classifying them into polarity i.e. Positive, Negativeand Neutral.
Python as a Basic Calculator

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.