Reviewerly/exHarmony huggingface dataset:
Range Majorities and Minorities in Arrays

The problem of parameterized range majority asks us to preprocess a string of length n such that, given the endpoints of a range, one can quickly find all the distinct elements whose relative frequencies in that range are more than a threshold $$\tau$$ . This is a more tractable version of the classical problem of finding the range mode, which is unlikely to be solvable in polylogarithmic time and linear space. In this paper we give the first linear-space solution with optimal $$\mathcal {O}\!\left( {1 / \tau } \right)$$ query time, even when $$\tau$$ can be specified with the query. We then consider data structures whose space is bounded by the entropy of the distribution of the symbols in the sequence. For the case when the alphabet size $$\sigma$$ is polynomial on the computer word size, we retain the optimal time within optimally compressed space (i.e., with sublinear redundancy). Otherwise, either the compressed space is increased by an arbitrarily small constant factor or the time rises to any function in $$(1/\tau )\cdot \omega (1)$$ . We obtain the same results on the complementary problem of parameterized range minority.
Aplikasi Metode Quantum Dengan Model Pembelajaran Tipe Circ Pada Materi Pythagoras Terhadap Hasil Belajar Siswa

Mathematics is one of the subjects that are included in the national exam and is still considered difficult by students. Results in the field show that student learning outcomes on the last semester exam in mathematics are still below the minimum completeness criteria. it is necessary to apply certain models and methods, one of which is the Quantum Learning Method, the Quantum Method is a method, instructions, strategies, and the entire learning process that can sharpen understanding and memory, and make learning a fun and useful process. In this study, the Quantum Method is collaborated with one of the learning models known as the CIRC type of cooperative learning model. This study aims to determine the learning outcomes of class VIII students of MTsS Ulumul Qur'an using the quantum method with the CIRC cooperative learning model in the Pythagorean material. Data obtained through test instruments, observation and interviews. Based on the research results, it was found that in the first cycle, the percentage of students who got a score of ≥ 70 was 42.46%, and had not reached the KKM 80%. Then in the second cycle the percentage of students who scored ≥ 70 was 82.76%. So the results of the second cycle research can be said to be successful because it has reached the KKM 80%.
An Improved Ensemble-learning-based CBIR Algorithm

Traditional Content-based Image Retrieval (CBIR) algorithms are based on low-level features of images, which leads to a big margin for improvement in retrieval performance. To solve this problem, we propose a two-stage CBIR algorithm in the paper. Firstly, considering the strong ability of Convolutional Neural Network (CNN) in feature extraction, CNN-based models are established to extract high-level features for image retrieval. Secondly, Ensemble Learning (EL) framework is employed to form a new CBIR algorithm. Finally, experiments are implemented to compare the performance of the proposed algorithm with traditional algorithms. The results show that our algorithm has better image retrieval capability and stronger generalization ability.
Progressive Visualization of Epidemiological Models for COVID-19 Visual Analysis.

COVID-19 data analysis has become a prominent activity in the last year. The use of different models for predicting the spread of the disease, while providing very useful insights on the epidemics, are not inherently designed for interactive analysis, with time for a single computation ranging in the tens of seconds. In order to overcome this limitation, this paper proposes three techniques for progressive visualization of Susceptible-Infectious-Recovered (SIR) models data, that govern the trade-off between time and quality of the intermediate results. The techniques are quantitatively evaluated showing promising results.
PENGGUNAAN MEDIA SALINDIA INTERAKTIF UNTUK MENARIK MINAT BELAJAR SISWA SEKOLAH DASAR DALAM PEMBELAJARAN DARING

THE USE OF INTERACTIVE POWERPOINT TO INCREASING ELEMENTARY SCHOOL STUDENTS LEARNING INTEREST IN ONLINE LEARNING The purpose of this research is to describe the effectiveness of PowerPoint to engage primary students especially in the online learning setting. Method that have been using for this research was qualitative descriptive with the instruments are lesson plan, observation log, and teacher’s mentor feedback. The result show that the use of PowerPoint was effective to engage students in learning because there are many features that support teacher to teach. The features is use to add pictures, video, and particularly hyperlink features that help user to made nonlinear presentation, so the teacher can design an interactive quizzes. PowerPoint also support students to learn individually at home in this kind of online learning setting. It can be conclude that PowerPoint is effective to engage primary student in online learning. Therefore the researcher suggest that the PowerPoint can be validated by the expert so that it can use for teaching primary students in online learning around Indonesia.
Produção Científica Sobre Catalogação: Uma Análise na Literatura Indexada pela Base de Dados Brapci / Scientific Production About Cataloging: An Analysis in the Literature Indexed by the Brapci Database

A pesquisa objetiva analisar a producao cientifica sobre catalogacao nos ultimos dez anos, representada pelos artigos cientificos indexados na BRAPCI. Os procedimentos metodologicos tiveram carater exploratorio e descritivo, com abordagem quantitativa e qualitativa e com aplicacao do metodo bibliometrico. A coleta dos dados foi realizada em 17 de janeiro de 2020. Os resultados apontam que o periodico com maior numero de publicacoes foi o Perspectivas em Ciencia da Informacao e o ano de 2017 foi o de maior destaque, com 21% de trabalhos produzidos. 79% dos trabalhos foram elaborados em coautoria. Os autores mais proliferos foram Placida Santos (28), Zaira Zafalon (13), Ana Maria Pereira (12) e Fabiano Castro (11). 81% dos autores sao doutores e 77% atuam como docentes. As instituicoes que mais contribuiram com o tema foram a UNESP, a UFSC e a UNIRIO. Conclui-se que nos ultimos dez anos sao significativos tanto os numeros de producoes sobre catalogacao quanto de publicacoes sobre a tematica por doutores e docentes da area. Palavras-chave : Producao Cientifica. Comunicacao Cientifica. Periodico. Catalogacao. ABSTRACT The research aims to analyze the scientific production on cataloging in the last ten years, represented by the scientific articles indexed in BRAPCI. The methodological procedures were exploratory and descriptive, with a quantitative and qualitative approach using the bibliometric method. Data collection was carried out on January 17, 2020. The results indicate that the periodical with the largest number of publications was Perspectivas em Ciencia da Informacao and 2017 was the year with the greatest prominence, with 21% of works produced. 79% of the works were co-authored. The most prolific authors were Placida Santos (28), Zaira Zafalon (13), Ana Maria Pereira (12) and Fabiano Castro (11). 81% of the authors are doctors and 77% act as teachers. The institutions that contributed most on the topic were UNESP, UFSC and UNIRIO. It is concluded that in the last ten years both the number of productions on cataloging and publications on the subject by doctors and professors in the area are significant. Keywords : Scientific Production. Scientific Communication. Periodic. Cataloging.
DEEP LEARNING-BASED SPEECH-TO-IMAGE CONVERSION FOR SCIENCE COURSE

Motivation: According to the law of pupils' cognitive development, intuitive images are more suitable for pupils' classroom learning than abstract imaginations. In elementary science course, hands-on course topics are the best way to ensure the learning effect. However, in remote rural areas, they are faced with the problems of lack of experimental equipment and low scientific literacy of teachers. Therefore, we propose a speech-to-image conversion framework that converts speech descriptions into images that conform to speech semantics. It can provide students with an abstract to intuitive way to learn science course, stimulate students' interest in learning, and improve elementary science course's implementation effect in remote rural areas.Method: The framework of speech-to-image conversion includes a speech recognition module and an image generation module. The speech recognition module outputs text from speech, and then the image generation module generates images that are semantically consistent with corresponding speech descriptions based on the output text from speech recognition. In the speech recognition module, we firstly extract acoustic features from speech, and then we use the Transformer network-based speech recognition method to train the acoustic model. We train a deep convolutional generative adversarial network in the image generation module to convert text descriptions to images. Both the generator network and the discriminator network perform feed-forward inference conditioned on the text feature.Result: This task requires specific training material, consisting of speech and image pairs. Unfortunately, there is no such database with the right amount of data. Therefore, we used the thchs30 corpus and Oxford-102 database with text descriptions to provide the results of our proof of concept in the experimental part. The result shows that our framework can achieve 79.3% accuracy of the speech-to-image conversion. We experimented on 20 students to aim at teaching in elementary science course. For the teacher's guidance, students describe the features of the object by speech and generate the corresponding feature images. We can explore whether this way has improved students' interest in learning and classroom enthusiasm.Conclusion: The paper proposes a framework for converting speech to image for pupil's science course. The results show that this method can effectively stimulate students' interests in learning and easily accept and master the knowledge content. It can promote the better development of elementary science courses in remote rural areas.
Attribute-Based Data Management in Crypt Cloud

Data proprietors will store their data out in the open cloud alongside encryption and specific arrangement of credits to get to control on the cloud data. While transferring the data into open cloud, they will allot some ascribe set to their data. On the off chance that any approved cloud client needs to download their data, they ought to enter that specific credit set to perform further activities on data proprietor's data. A cloud client needs to enlist their subtleties under cloud association to get to the data proprietor's data. Clients need to present their subtleties as characteristics alongside their assignment. In light of the client subtleties, semi-trusted authority creates decoding keys to gain power on proprietor's data. A client can play out a great deal of activities over the cloud data. In the event that the client needs to peruse the cloud data, he should enter some read-related traits, and on the off chance that he needs to compose the data, he should enter compose-related qualities. Adversary every single activity client in an association would be checked with their one of a kind quality set. These qualities would be shared by the administrators to the approved clients in cloud association. These characteristics will be put away in the approach records in a cloud. On the off chance that any client releases their interesting decoding key to the any malignant client, data proprietors needs to follow by sending review solicitation to evaluator and reviewer will process the data proprietors demand and infers that who is the liable.
AI in an imaging center: Challenges and opportunities

There is significant and well-founded enthusiasm for the promise of AI to improve healthcare outcomes across diverse clinical applications in oncology However, careful analyses of the impact of AI tools in actual real world clinical settings are sparse in this relatively early phase of scientific discovery The rigor applied to deep learning model development (training, testing, external validation) must be matched with equal rigor in assessing patient outcomes after implementation of deep learning tools in actual clinical settings We can learn from our past endeavors in computer-assisted image interpretation Tools for computer aided detection were approved by the FDA in 1998 to help radiologists find breast cancers on mammograms However, after widespread clinical implementation (based on promising results in reader studies and reimbursement approvals), clinical trials to assess the impact of CAD on diagnostic accuracy of mammography revealed the tools did not improve human performance in findings cancers, and in many cases increased false positive readings by humans using CAD assistance Due to severely limited healthcare resources during pandemics, and to protect patients and healthcare workers, state governments and the COVID-19 Pandemic Breast Cancer Consortium urged providers to focus cancer screening efforts on those patients at higher These mandates were and continue to be necessary responses to support fair allocation of scarce resources to maximize benefits for all patients across the full spectrum of healthcare needs and acknowledge the shift in risk-benefit ratios for screening in average women After screening mammography programs were closed due to the COVID-19 pandemic, we implemented an AI image-only model strategy at MGH to invite women at higher to return to screening for breast cancer The AI model identified the top 60% of patients at risk, who were diagnosed with 90% of cancers, while the top 60% of at-risk patients based on traditional models harbored less than 50% of cancers Our AI image-only model performed best in identifying women with current breast cancer, followed by strategies which used five-year traditional models and included all women with a personal history of breast cancer in invitations to screen Lifetime models performed extremely poorly Operating at the traditional high risk threshold of >20% lifetime risk, these models identified fewer than 10% of patients with cancer in the total population screened AI-based models may support accurate and equitable risk-based screening It may be possible with early intervention to reduce the widening disparities and later stage breast cancer diagnoses we are experiencing with COVID and mitigate the of delayed diagnoses and increased mortality from breast cancer during and after pandemics Strategies for careful and rigorous assessment of clinical implementation are essential for AI to have the impact in health desired by all
Development, Test and Application of a framework for cloud serverless services

In the scene of services for the creation of web applications is focusing more and more towards a micro services oriented approach, moving away from structures called monolithic. The maximum representation of this is with the serverless paradigm, which since 2014 has seen an ever greater increase in its use and in its investments by the major cloud providers. Such a paradigm has found an implementation in the cloud model Functions as a Service, which uses as its main resources, plain simple functions. Serverless Framework has emerged as one the major framework that allow the usage of the homonym paradigm in a simple way, and introducing a level of abstraction regarding the underlying structure of the chosen cloud provider. Despite the functionalities introduced by Serverless, the developer has to take charge of various operations concerning indirectly the business logic of the application, with the main one being: to structure the code base, to define the various resources through the compilation of a configuration file, to define a unit testing structure, fundamental once the application complexity increases. Furthermore, based on the chosen cloud provider, the developer must find solutions to problems such as Cold start, and limitations in the creation of resources. The Restlessness framework was born with the goal of improving the user experience of Serverless, providing a standard project and testing structure, a Command Line Interface and a local Web Interface through which is possible to completely manage the project, and with the further goal of minimizing all operations that do not concern directly the application's business logic. The framework is provided as an Open Source package, and with the possibility of extending its functionalities, through the use of addons, some of which are already present, to address common patterns, such as database access or authentication. During the framework development has been possible to test it on real applications, thus allowing to find and correct critical issues, whose main ones were: Cold start handling, use of the non relational database mongodb, and limitations on the applications structure proposed at the beginning.
Research Guides: Science: Getting Started

Provides resources for research in science. If unsure where to start, or need help, please ask a librarian for help.
KaByahe: a mobile application focusing on the historical and cultural places within the city of manila

Abstract With the Philippines' rich historical and cultural heritage, tourism remains a vital sector and is one of the greatest contributors to its rising economy. Manila, its capital, has a vast list of historical and cultural places that mirrors the country’s rich historical and cultural heritage. The developers were inspired to develop a mobile application called KaByahe with the purpose to determine the different historical and cultural places within Manila and serve as its users' own guide in exploring more of its historical and cultural roots. The developers strongly believe that this project is important not only for the benefit of the tourists in exploring Manila, but also, for its local people, to aid them in knowing what has transpired beyond these places and promote appreciation and understanding of the city’s origins. The developers used Agile Development Methodology since the strategy is to develop using short development cycles to focus more on addressing potential issues and produce quality output per cycle. With this, the developers were able to work more efficiently and collaboratively. The developers were able to gather substantial information through various testing procedures conducted to address both functional and nonfunctional requirements. Based on the results of these, KaByahe was deemed to successfully meet the objectives set by the developers.
Pre-adjustment of positions of point-clouds for the ICP algorithm using IMU and parallel projection

In this paper, we propose a preprocessing for the ICP algorithm to avoid convergence failures. Using a depth camera with the IMU, the movement of it can be measured. We use it for the pre-adjustment of two point-clouds as a preprocessing of the ICP. Our method consists of the following steps: (1) Measurement of the camera orientation from IMU, (2) Adjustment of the measured point-clouds using the camera orientation, (3) Parallel projection of the points onto one plane and 2D alignment, and (4) Depth alignment and overlapping of the projected models as the initial positions for the ICP. We also show several experimental results to indicate the effectiveness of our proposed methods.
What Is Legal Innovation

Technological progress, along with the economic success it brings innovators, has transformed technological innovation into an object of admiration. The law supports and regulates technological and creative innovation in other fields, but is law itself an arena of innovation – of legal innovation? Do the concepts, doctrines, theories and techniques produced by the law encompass innovation? If so, does legal innovation share similar characteristics with other kinds of innovation? Can we learn something about the nature of legal innovation from the general field of innovation research? The legal discipline is more preoccupied with identifying innovation in other areas than in analyzing the characteristics of its own innovations. In this Essay I argue that legal innovation has similar characteristics to innovation in other areas, although it is no less impressive and influential. The phenomenon of legal innovation can and should attract more scientific attention. 2021 U. Ill. L. Rev. Online 63, https://illinoislawreview.org/online/what-is-legal-innovation/
The Model Behavioral Health Crisis Mobile Response Team Act

This is a model law for non-police behavioral health crisis response teams. It is modeled after CAHOOTs and can be customized to fit the needs of state or local governments.
Emergency response robotic platforms

Abstract This paper describes the development of an autonomous robotic platform capable of undertaking rescue missions on rough terrain and other unpredictable environments. The key difference of this platform is the lack of LIDAR system and an algorithm that relies primarily on ultrasonic and optical sensors. As a result, two platforms were created, a fully autonomous drone and a scout bot that operate effectively together.
Comparison of GaoFen-5 hyperspectral and airborne hyperspectral imagery: case study of Gossan identification in Subei area

As a new multi-sensor satellite, GaoFen-5 (GF-5) has gradually attracted more attention. Especially, the GF-5 hyperspectral sensor has shown good prospects in geological applications, such as mineral mapping, geological body identification, and mining environment analysis. Therefore, there is an urgent need to evaluate the effectiveness of GF-5 hyperspectral data relative to airborne hyperspectral images (HSI) in geological applications. In this paper, the characteristics and preprocessing steps of GF-5 HSI were introduced. The HyMap data in the Subei area was employed for comparative experiments to evaluate the application performance of GF-5 in gossan identification. The experimental results show that the diagnostic spectral characteristics of limonite can be observed through GF-5 data. The distribution trends of limonite in both hyperspectral data are consistent, and the concentration of the limonite area directly indicated the gossan information, indicating that GF-5 HSI has promising potential for mineral mapping and may have important significance in large-scale geological applications.
Research Guides: CYBR-500-OLA (Hawthorne): Citing Sources: APA Style

Library research guide for Rider University's M.S. in Cybersecurity program and Dr. Hawthorne's Beyond Code: Cybersecurity in Context online course