Reviewerly/exHarmony huggingface dataset:
Estrategias de enseñanza que fomentan la competencia matemática durante la COVID-19

espanolLas estrategias de ensenanza de la Matematica se conciben como un conjunto de acciones realizadas por el docente, con el objetivo consciente de que el estudiante aprenda de la manera mas eficaz; son acciones controladas por el docente y tienen un alto grado de complejidad, ademas incluyen los medios de ensenanza para su puesta en practica, el control y la evaluacion de los propositos. Por lo tanto, la investigacion tuvo como objetivo evaluar las estrategias de ensenanza utilizadas por el docente de Matematica que fomentan la competencia Matematica durante la COVID-19. La investigacion se enmarco en el paradigma interpretativo, fue de tipo descriptivo y su diseno fue el estudio de caso. La investigacion se realizo en cuatro fases: la primera fase “preparatoria”; en la segunda fase se realizo “el trabajo de campo” donde se aplico una entrevista en profundidad al docente. En la entrevista en profundidad se encontro que las estrategias de ensenanza que fomentan la competencia Matematica presentan deficiencias en algunas categorias. En la tercera fase “analitica” se redujo, se sintetizo y se transformo la informacion; y en la cuarta fase “informativa” se dio a conocer los resultados y se difundio la investigacion. Se concluye que las estrategias de ensenanza de la Matematica son determinantes en el fomento de las competencias en los estudiantes, por eso se debe enfatizar que los docentes incluyan en sus secuencias didacticas estrategias de ensenanza que enfaticen la solucion de problemas basados en competencias y no en procesos repetitivos y mecanicos. EnglishThe teaching strategies of Mathematics are conceived as a set of actions carried out by the teacher, with the conscious objective that the student learns in the most effective way; they are actions controlled by the teacher and have a high degree of complexity and include the teaching aids for their implementation, control and evaluation of the purposes. Therefore, the research aimed to “evaluate the teaching strategies used by the Mathematics teacher that promote Mathematics competence during COVID-19. The research was framed within the interpretive paradigm, it was descriptive and its design was a case study. The research was carried out in four phases: the first “preparatory” phase; the second phase was carried out field work where an in-depth interview was applied to the teacher. In the in-depth interview, it was found that teaching strategies that promote Mathematical competence present deficiencies in some categories. The third “analytical” phase where the information was reduced, synthesized and transformed; and the fourth “informative” phase where the results are made known and the research is disseminated. It is concluded, the teaching strategies of Mathematics are decisive in the promotion of competences in students, so it should be emphasized that teachers include in their didactic sequences teaching strategies that emphasize the solution of problems based on competencies and not in repetitive and mechanical processes.
Efficient Learning of Less Biased Models with Transfer Learning

Prediction bias in machine learning models, referring to undesirable model behaviors that discriminates inputs mentioning or produced by certain group, has drawn increasing attention from the research community given its societal impact. While a number of bias mitigation algorithms exist, it is often difficult and/or costly to apply them to a large number of downstream models due to the challenges on (sensitive) user data collection, expensive data annotation, and complications in algorithm implementation. In this paper, we present a new approach for creating less biased downstream models: transfer learning from a less biased upstream model. A model is trained with bias mitigation algorithms in the source domain and fine-tuned in the target domain without bias mitigation. By doing so, the framework allows to achieve less bias on downstream tasks in a more efficient, accessible manner. We conduct extensive experiments with the proposed framework under different levels of similarities between the source and target domain and the number of factors included for de-biasing. The results are positive, implying that less biased models can be obtained with our transfer learning framework.
A unified method for improving long-range accuracy of stereo and monocular depth estimation algorithms.

Environment perception for driving applications requires very accurate sensors especially when dealing with depth measurements. LiDAR is the most trustworthy sensor in this domain, but it suffers from disadvantages in terms of number of scene points and their temporal alignment. These issues are especially relevant when dealing with long-range measurements, where each 3D point is crucial. As an alternative, in this work we focus on camera-based depth perception for objects at large distance by using stereo reconstruction and monocular depth estimation. Towards improving the capabilities of camera-based, we initially introduce a taxonomy to categorize all types of camera-based depth perception methods with respect to their long-range capabilities. We then present a correction method that works for both stereo and monocular depth perception algorithms that output a depth in discrete setting (most suitable for realtime applications). We show that our method improves the precision for such algorithms for objects at large distances without affecting the near-range accuracy. The method requires only several additional operations, preserving the real-time capabilities of the underlying algorithms.
Library Research Guides: OpenAthens: Updating Direct Links

This guide provides information for Kaufman Library's transition to OpenAthens authentication of electronic resources with GALILEO. Information for athenizing links.
Development of Youtube Sentiment Analysis Application using K-Nearest Neighbors (Nokia Case Study)

Nokia is a company that uses Youtube as a platform to advertise and market its products until now. Nokia was a cellphone company that had fallen in 2013 due to the company's unwillingness to follow the android operating system trend at the time. Nokia continues to rise and launch new products that are increasingly sophisticated. In seeing and summarizing public opinion towards Nokia's products, this research will develop a website-based application namely Youtube Sentiment Analysis. Application is built using Python and JavaScript with the Flask framework. In building the sentiment analysis model, this reseach use K-Nearest Neighbors algorithm with value of k=5 and getting an accuracy value of 88.6%. Application that has been built can carry out processes ranging from collecting comments using Selenium, labeling comments automatically using VaderSentiment, preprocessing comments using NLTK, weighting words using TFIDF, classifying using K-Nearest Neighbors algorithm, evaluating algorithms using confusion matrix, to visualizing classification and evaluation results in the form of pie charts, bar charts and confusion matrix.
Local wisdom-based story calendar: Improving students narrative writing skills in elementary school

The research aims to: (1) develop a local wisdom-based story calendar to improve narrative writing skills and (2) reveal the effectiveness of the developed story calendar. This research was R & D applying Brog & Gall model (1983). The subjects were fourth-grade students of elementary schools in Semarang, Indonesia. The data collection was through observation, interviews, and questioners. The data analysis used the independent sample t-test at the significance level of 0.05. The results of the research show that: (1) the developed story calendar has passed the criteria of feasibility with very good criteria; (2) the local wisdom-based story calendar is effective to improve the narrative writing skills based on the t-test results with a significance level of < 0.05, that is 0.000. Through the pictures, local wisdom-based stories, and some literacy activities in the developed story calendar, elementary students can write narrative text more directed and easily.
A Study on Specialized High School Students’ English Learning Motivation and Class Environment

This study aims to investigate English learning motivation of students attending the specialized high school and to examine their English classroom environment. To achieve the goal of this investigation, the researcher conducted both quantitative and qualitative research. The quantitative research included a survey of 109 specialized high school students while qualitative research was also conducted through class observation and a teacher interview. The quantitative research showed that the most predominant motivation factor for learning English among specialized high school students is ''ought-to L2 self'' while the least prevailing motivation factor is intrinsic motivation. On the other hand, the dominant demotivating factor is learning difficulty due to the lack of basic English knowledge. In addition, it is noted that the English class desired by students is a practical conversation class centered on speaking activities. According to the qualitative research through class observation and interview, it is suggested that an integrated class of English and art is one of the feasible ways to increase students'' participation and to enhance students'' learning motivation. This study implies the need for continuous follow-up research of English classes for specialized high school students. (Daejeon University)
Testing and Code Quality

Whether the system you are working on is big or small, as mentioned in the beginning of this book, the codebase's integrity is very important. Following some basic rules, like proper naming conventions, neat code structure, and just careful coding, will have an effect on the codebase's integrity. Easy-to-read code is slightly harder to break than code that is badly structured. The easier it is to read and follow the code, the easier it becomes to make changes to it. As Harold Abelson wrote in Structure and Interpretation of Computer Programs, “Programs must be written for people to read, and only incidentally for machines to execute.” Proper naming conventions can also lead to code that will be less prone to introducing bugs than badly named variables, functions, and classes.
On-Demand Blind Packet Forwarding

Network Address Confidentiality (NAC) classifies all third parties and network nodes as adversaries and limits access to the network packet addresses in cleartext exclusively to the communicating endpoints. NAC implies certain anonymity properties, namely sender/recipient and relationship unlinkabilities. Blind Packet Forwarding (BPF) realizes NAC and its unlinkability properties by redesigning the packet forwarding and its associated network functions to blind ones transferring and processing packet addresses in end-to-end encrypted form. BPF defines two modes. In the semi-blind mode, NAC and its unlinkability properties apply only to communicating endpoints, while the fully blind mode provides these security properties for communicating endpoints as well as domains and local networks. However, the full blindness in a domain requires to set up and maintain masked routing tables within the entire domain, which is a costly process. This paper proposes multiple approaches for different cases to selectively set up masked routing table entries and to perform the full blindness on demand. Moreover, we present our prototype implementation and its evaluation by means of an adjusted OpenFlow version and multiple scenarios respectively.
Make the Most of Discovery And Qualification Visits

The Major Gifts ReportVolume 23, Issue 3 p. 8-8 Prospect Identification Make the Most of Discovery And Qualification Visits Thomas Schroeder, Thomas SchroederSearch for more papers by this author Thomas Schroeder, Thomas SchroederSearch for more papers by this author First published: 03 February 2021 https://doi.org/10.1002/mgr.31684Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume23, Issue3March 2021Pages 8-8 RelatedInformation
Automated Traffic Control System

Abstract In this paper, we propose an architecture for creating Intelligent Traffic Control System. The vehicle works in two modes mainly normal and emergency mode. Here Image processing technique is used to sense the traffic density of every junction and release the heavy road frequently. The system will detect vehicles, through image instead of using electronic sensors.
Method of differential privacy protection for web-based communities based on adding noise to the centroid of positions

The research of location privacy protection has a very important significance for the emergence and sustenance of web-based communities in the big data era. The method of differential privacy for position is a privacy model strictly for the measurement of privacy. However, under the privacy protection for multi-positions, it will bring much bigger errors by adopting the method of adding noise to protect the privacy of multi-positions. In this paper, the author puts forward a new method of differential privacy for multi-positions based on mechanism of adding noise to the centroid of positions and compares with the independent mechanism of adding noise. The experiment shows that the centroid mechanism of adding noise is better than the mechanism of independently adding noise.
Non-Markovian Predictive Coding For Planning In Latent Space

High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. In order to handle high-dimensional sensory inputs, existing MBRL approaches use representation learning to map high-dimensional observations into a lower-dimensional latent space that is more amenable to dynamics estimation and planning. Crucially, the task-relevance and predictability of the learned representations play critical roles in the success of planning in latent space. In this work, we present Non-Markovian Predictive Coding (NMPC), an information-theoretic approach for planning from high-dimensional observations with two key properties: 1) it formulates a mutual information objective that prioritizes the encoding of task-relevant components of the environment; and 2) it employs a recurrent neural network capable of modeling non-Markovian latent dynamics. To demonstrate NMPC’s ability to prioritize task-relevant information, we evaluate our new model on a challenging modification of standard DMControl tasks where the DMControl background is replaced with natural videos, containing complex but irrelevant information to the planning task. Our experiments show that NMPC is superior to existing methods in the challenging complex-background setting while remaining competitive with current state-of-the-art MBRL models in the standard setting.
Groundwater Level Forecasting Model in Tropical Peatland Using Artificial Neural Network

One of the key parameters in peat land management is water, which is expressed in the water level of peat land. The water level fluctuations of a peat land are closely related to the decomposition of the peat constituent material, its cover and hydrological conditions. When the water level drops, the peat decomposition increases and will release the carbon into the atmosphere. In addition, the condition of peat will be dry so that the area becomes prone to fire. The main purpose of the research is to develop a groundwater level forecasting model to monitor the dynamics of land water fluctuations in tropical peatland in order to comply with government regulation No. 57 of 2016 on the protection and management of peat ecosystems, especially the necessity to maintain water level at a rate of 40 cm. The method of research approach used is using ANN as one branch of soft computing. The location of research on peatland of PT Meskom Pulau Bengkalis, Riau province. Data that is used to build a model of groundwater level forecasting in tropical peatland sourced from Hobo Water Logger measuring device that record water table in 2014. The main results of the research proved that the implementation of a groundwater level forecasting model on the tropical peatlands in Bengkalis using the ANN method approach for the next day (t + 24) has a very strong classification tested using statistical parameters coefficient of correlation (R) and Mean Square Error (MSE) respectively 0.995929 and 0.0003026 so that the model can be applied on tropical peatlands.
PENGOPERASIAN DAN PERAWATAN POMPA PEMADAM KEBAKARAN DARURAT DI KAPAL KM. PERMATA PAPUADI PT. CITRA BAHARI SHIPYARD

Emergency fire pump is one of safety equipment on ship and the function to put out fire if happen a fire on ship, with sea water, and emergency fire pump have require owned to every ship cause the authority it’s very urgent. Emergency fire pump must routine to check done with scale. Ship extinguisher fire system is very urgent, usefull to tackle a danger fire caused by the fire was happen on ship.The system extinguisher fire of fire main system, supplying sea water in high temperature to ship. Sea water it’s one of equipment extinguisher fire on ship own supplying very big, sea water can be applicated with steam or spray customized with the conditions that occur and sea water is a cooling device which can block flammable material from replashing, slowing the spread of fire on ships and protect firefighters. The treatment is more emphasis on the observation of the object by making certain measurement and predict the damage that occurs on emergency fire pump and to determine the damage that occurred is done by inspection step using the instrument as in predictive maintenance..Keyword : Emergency fire pump, damage and maintenance
ODiSa: Offline Diversity Sampling for Efficient Goldset Generation

Labeled dataset generation is an expensive and time-consuming process, so it is important to make sure we make the most of our investment. Randomly sampling a subset to manually label is the most common method but does not provide the most informative data for training. Additionally, most existing methods for sample selection for dataset generation are online processes that require continuous feedback between a model and the annotator, often requiring technical expertise to set up, and often results in test set leakage or biasing the annotator. We propose Offline Diversity Sampling (ODiSa) as an offline unsupervised data selection method to increase the size and heterogeneity of the final labeled dataset. We expect this to lead to models that perform better on rare terms and are better able to generalize. We draw inspiration from experimental design, unsupervised pre-training, topic modeling, and abstractive summarization to improve the efficiency of dataset building. ODiSa consists of several interchangeable steps that allow adaptation to other types of applications and should be considered a strategy rather than a precise recipe. We demonstrate this strategy for a chest radiology annotation task with the following stages: SciBERT sentence embedding, UMAP, HDBSCAN, and a greedy-wise set cover algorithm. Using ODiSa we find ~4x improvement in sentence diversity, ~2.5x increase in the mean annotated labels, and a 13% increase in number of unique labels across 100 annotations. ODiSa is a powerful way to improve the efficiency of dataset generation and save money on dataset labeling.
Analysis on Student Comprehension Level to Activities of Group Guidance Service

This research aims to identify level of student comprehension on activities of group guidance service in SMAN 2 Banguntapan. This research is categorized into descriptive quantitative research design. This research is executed on 98 students who are then taken as the research sample, particularly who have joined the activities of group guidance service. Moreover, the research instrument is a closed questionnaire that has been through validity test in formula of product moment and reliability in formula of alpha. Next, the method of data analysis in this research exerts descriptive analysis. Due to the research findings, it is indicated that the level of student comprehension to the activities of group guidance service in low category is 15 students (15%), mediate category is 67 students (69%), and high category is 16 students (16%). Regarding to this assessment result, it is required to optimize the activities of group guidance service in order to achieve the student development, widen knowledge and information, and prevent problems that might happen in the future
Quantum Optical Processes

The aspiration to harness the nonclassical properties of light to improve the way we communicate and analyze data emanates from the expectation that the strong correlation shared among entangled states, or interconnectedness established by the entanglement, can enhance the efficiency of the exchange of information between nodes or sites.
Biometric Authentication in Mobile Banking

In present day world, mobile banking has emerged as a winner among various banking services. The major concern for mobile banking is its security. Since present authentication methods such as passwords, OTPs do not offer high level of security, there is always a risk of fraudulent attacks. To avoid this risk, reliable authentication procedures must be used. Biometric serves the purpose of reliable authentication mechanism. The paper gives the overview and quantitative comparative analysis of various physiological and behavioral biometric techniques that can be used in mobile banking. Various criteria that have been used for analysis are uniqueness, universality, permanence, circumvention, performance, and collectability and acceptability.
Il ruolo della comprensione del testo nella risoluzione dei problemi

In this article some reflections related to the role of text comprehension are proposed in order to be able to face the resolution of a mathematical problem, starting from the interpretation carried out by different researchers in Mathematics Education.
PENGUASAAN KEAHLIAN AKUNTANSI, PAJAK, SOFTWARE AKUNTANSI, BAHASA INGGRIS DAN KEPERCAYAAN DIRI MAMPU BERSAING DALAM DUNIA KERJA

The purpose of this research is to study it there is positive influence of expertise of  mastery accounting, taxation, accounting software   and english language toward confidence being able to compete workplace.  The research method used is causal, with the unit of analysis the accounting student . Sampling method used stratified random sampling. The mastery accounting expertise, tax, accounting software   and english language toward confidence being able to compete workplace by using the instruments measured questionnaire with likert scale. Source analysis performed by descriptive analysis and linear regression. The result  shows that’s influence of mastery accounting expertise,tax,accounting software   and english language and confidence being able to compete workplace with determination coefficient is 12,18%.
The Creation, Use and Development of Educational Platforms

This article provides information on the organization, use and development of educational platforms, as well as the work done and innovations in this area.