Reviewerly/exHarmony huggingface dataset:
Investigating the Effect of Climate on National Rice Production using Machine Learning

This paper examines the influence of climate on national rice productivity using K-Mean Clustering through the Fact Constellation scheme. The research was conducted to see the effect of climate on national rice productivity, especially on the island of Sumatra. For the clustering, the popular machinelearning tool K-Nime was used whose visualization feature was principally useful to determine the patterns, dependencies, and relationships of rice yield on different climate and soil factors of rice production. Research shows that several climateComponents such as temperature, humidity and solar radiation have a very strong influence on national rice productivity.
whyR: why consider using R for your stats

Critical thinking in statistics is supported and can be better developed through practice and application. Coding environments such as R for your stats and viz work promote this literacy and replication. Showing and re-testing your work through code and scripts is a powerful learning tool.
Hand Gesture Recognition in Complex Background Based on YOLOv4

Traditional methods often use a monochrome background such as green as the background so that the recognition process is not affected by the background, but this operation makes recognition limited to the use of the background plate, greatly reducing flexibility and losing the original gesture recognition to improve the level of human-computer interaction. In this paper, we propose the use of background subtraction to first eliminate the background so that recognition is not affected by the background, solving the problem of complex backgrounds affecting gesture recognition; then use YOLOv4 for gesture recognition to recognize Chinese hand gestures. After using background removal, the gesture recognition accuracy of YOLOv4 can be as high as 80%.
PENAKSIR BAYES UNTUK PARAMETER DISTRIBUSI DAGUM MENGGUNAKAN ATURAN PRIOR NON-INFORMATIF DENGAN K-LOSS FUNCTION DAN ENTROPY LOSS FUNCTION

ABSTRACTThe parameter estimator discussed is the parameter estimator of the Dagumdistribution with the k-loss function and the entropy loss function using the Bayesmethod. To get the Bayes estimator from the scale parameter of the Dagumdistribution, the Jeffrey’s non-informative prior distribution is used based on themaximum likelihood function and the loss function for the k-loss function and theentropy loss function to obtain an efficient estimator. Determinant of the bestestimator is by comparing the variance values generated from each estimator. Anestimator that uses the entropy loss function is the best method for estimating theparameters of the Dagum distribution of the data population with efficientconditions met.Keywords: Parameter estimator, Dagum distribution, Bayes method, Jeffrey’sprior, loss function
Dealing with Errors

When you introduce input from users, they won't always provide input that is going to work for your program. When that happens, you need a way to have the program recognize that something is wrong and address it, without completely crashing.
AI3SD Video: How can Explainable AI help scientific exploration?

Although models developed using machine learning are increasingly prevalent in scientific research, their opacity poses a threat to their utility. Explainable AI (XAI) aims to diminish this threat by rendering opaque models transparent. But, XAI is more than just the solution to a problem--it can also play an invaluable role in scientific exploration. In this talk, I will consider different techniques from Explainable AI to demonstrate their potential contribution to different kinds of exploratory activities. In particular, I argue that XAI tools can be used (1) to better understand what a big data model is a model of, (2) to engage in causal inference over high-dimensional nonlinear systems, and (3) to generate algorithmic-level hypotheses in cognitive science and neuroscience.
Chapter 2. Purposes

Chapter 2 of Library Technology Reports (vol. 57, no. 6), “Purposes,” explains that metadata application profiles (MAPs) provide benefits for metadata creators, managers, and consumers throughout the processes of metadata creation, implementation, use, and reuse. Different kinds of MAPs may provide different benefits and be best suited to different applications. Providing support for metadata interoperability and reuse is a significant goal for MAP design and implementation. Keeping considerations of both semantic and technical interoperability in mind during the MAP-making process can help achieve this goal.
Digital Poetry Circuit: Methodology for Cultural Applications with AR in Public Spaces

Culture brings knowledge, leisure, and pleasure to society through various forms of manifestation. Both poetry and visual arts enchant us with the form and the recovery of our traditions. In this sense, new technologies have great potential to bring us closer to culture, facilitating access and reach so that we can rescue authors, artists, and our cultural memory. This article seeks to present a methodology to connect culture, technology, and design through an artifact that interacts with public environments and their monuments. Were UX, UCD, and Augmented Reality processes, in addition to studies on urban spaces in order to investigate the relationship of users with the public environment in which they identified themselves, an end of proportion an improvement in the cultural experience of the city of Recife.
Keynote Speech: Learning to Personalise Human Activity Recognition

Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. Innovative, person-centred strategies are required to monitor and predict physical activity and exercise behaviours, to scan and anticipate environmental barriers to activity, and to provide social and motivation support. Integrated Human Activity Recognition (HAR) and assistive technologies promise play a key role in this regard by enabling people to live their life well regardless of their chronic conditions. HAR is the classification of human movement, captured using one or more sensors either as wearables or embedded in the environment (e.g., depth cameras, pressure mats). In our work, we introduce learning-to-learn personalised models from few data. We extend the meta-instance creation process where random sampling of support and query sets is carried out on a reduced sample conditioned by a domain-specific attribute; namely the person or user, in order to create meta-instances for personalised HAR. I will present our recent work on learning personalised HAR models with few data and motivate our contribution through an application where personalisation plays an important role, mainly that of human activity recognition for self-management of chronic diseases.
Campus in Crisis? Process Optimization Can Help!

Assessment, academic success, and retention are at the forefront of higher education. When budgets and programs are systematically reduced, higher education institutions can face these challenges by applying process optimization techniques. In times of crisis, cross-functional teams employing these techniques create direct change in higher education environments. This article will provide college and university presidents, provosts, deans, and other senior leadership with a foundation on process optimization methodologies, such as Lean Six Sigma and Agile. This article also provides a primer for department and unit heads on the application of specific tools and techniques.
TEORI KRITIS THEODOR ADORNO.pdf

Adorno is of Jewish descent. His father, OskarWiesengrund, was a wine merchant, and his mother, Maria Calvelli, was a famoussinger. The influence of his mother made Adorno interested in music which madehim a musicologist. When he was 15 years old, between 1918 and 1919, he studiedunder Siegfried Kracauer. After completing his education at the Gymnasiumlevel, he continued his studies at the University of Frankfurt, where hestudied philosophy, music, and sociology. In 1924, he received a doctorate inphilosophy. His interest in music led him to study music in Vienna to studycomposition under the guidance of Alban Berg in 1925. Adorno joined theFrankfurt Institute for Social Research, after he wrote a thesis entitled:Kierkegaard: the Construction of the Aesthetic in 1933. At thattime Hitler was in power, and to avoid Nazism (because he was of Jewishdescent), he moved to England.
Stochastic Approach of Parsing Bengali Sentences

The parsing technique based on associate grammar rules as well as probability is called stochastic parsing. This paper suggested a probabilistic method to eliminate the uncertainty from the sentences of Bangla. The technique of Binarization is applied to increase the precision of the parsing. CYK algorithm is used in this paper. The work mainly focused on intonation-based sentences, for these reasons PCFGs (Probabilistic Context-Free Grammars) is based on proposed. About 30324 words are used to test the proposed system; average 93% accuracy is achieved.&#x0D; GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 51-56
Ensemble Labeling towards Scientific Information Extraction (ELSIE) - Blob Extraction.

Scientific publications constitute an extremely valuable repository of knowledge and collection of facts crucial to the advancement of science and development of applications, which grows as researchers learn from previous works and scientists use results in the literature to design and create. With the exponential growth of available publications, reading and extracting this wealth of information has become impractical for humans. Despite great progress in natural language processing, machine-learned solutions require large amounts of carefully annotated data for good performance. This is especially true in the context of accurately labeling and extracting complex scientific data. Towards our ultimate goal of extracting scientific facts from the literature, we first aim to identify blobs of text that contain all of the facts in a publication to be later automatically extracted or scrutinized by experts. Our previous work identified some facts missed by experts yet missed others due to the assumption that the target relation—here, a polymer and its glass transition temperature—would be contained within the same sentence. We set out to enhance our approximate labeling system to look back and ahead for missing information and successfully achieved 100% recall of scientific facts while reducing the full-text publication to 6% of its original size. Moreover, we assign confidence scores to sentences to further assist expert curators in identifying important sentences and facts locked in unstructured text.
The Dilemma and Countermeasures of the Construction of Design Teaching and Reasearch Section in Application-Oriented Universities. ----Take Guangzhou Nanfang University as an Example

The teaching and research section undertakes the most basic teaching tasks and scientific research management work. In the construction of application-oriented universities, application-oriented teaching and industry-university-research development are emphasized, the design discipline emphasizes the teaching content for the purpose of application. This paper takes the design teaching and reseach section of south GuangZhou University(It was the South College of Sun Yat-sen University) as the research object, which is representative in the poilot transformation process of application-oriented undergraduate universities. Organize relevant contents through interviews with relevant persons in charge, this paper points out the difficulties encountered in the construction of this kind of teaching and research section and puts forward some optimal policies.
Fluxo de informação na Ciência da Informação: Tendências e direções na pesquisa brasileira

It presents the directions or trends of Brazilian research on the topic of Information Flow (FI), revealed in publications in three years (2017-2019) in Brazilian journals in the field of Information Science (CI). Research with a qualitative approach that used bibliographic research as a technical procedure to collect publications in the period of three years, between 2017 and 2019, in the Referential Database of Articles in Journals in Information Science (BRAPCI). The content analysis method and the categorical analysis technique were used to group the 37 selected publications, being classified into four axes: FI in scientific information; FI in document management; FI in organizations; FI and interrelationships. It is evident that IF studies in organizations, as well as their interrelationships with other concepts from CI are presented as directions or trends in Brazilian research.
Improved Accuracy of Suspicious Activity Detection in Surveillance Video

Suspicious activity detection from surveillance video is the main objective of the work presented in this paper. The method developed consist of various stages of suspicious frame detection, and verifying the frame for suspicious activity related analysis of human movements within obtained set of suspicious frames. The method consist of GLCM feature extraction which constitutes the features such as energy, prominence, contrast, entropy, homogeneity type of features and matching using Euclidian distance along with descriptor features obtained by using Harris corner features and cosine similarity index estimation. The successful suspicious activity detection rate is analyzed which shows better performance and time saving method while analyzing large surveillance video dataset.
LibGuides: ESU Institutional Repository: Submit your Thesis or Dissertation

This guide highlights East Stroudsburg University's Institutional Repository (IR), how to use it, and how to submit your scholarly or creative works to the IR.
Mapas cognitivos neutrosóficos para el análisis de la vulnerabilidad socioeconómica

The study of territorial socioeconomic vulnerability (VSE) constitutes an imperative for the formation of the public agenda of local governments. Identifying the factors that generate SSE becomes the starting point for public action with the objective of minimizing or neutralizing them. Consequently, a structured sequence of analysis is offered to identify the dimensions and factors that influence socio-economic vulnerability in a territory. The starting point is to determine the existing interrelations between the factors as a way to model the uncertainty and indetermination of the factors that influence the levels of VSE. The analysis is complemented with the application of Neotrophic Cognitive Maps (NKM). The developed proposal was applied in Calixto Garcia municipality, which allowed revealing the factors that influence the VSE of the municipality, as well as the interdependencies, uncertainty and indetermination among them.
Reproducibility and Open Science

Presentation slides for my keynote at the Symposium on open science research practices organized by the University of Groningen Library and the Central Medical Library in cooperation with the Open Science Community Groningen (https://www.rug.nl/library/calendar/symposium-on-open-science-research-practices), delivered on 22 October as part of Open Access Week 2019 (http://www.openaccessweek.org).
Παρακολούθηση και ανάλυση συστήματος και υπηρεσιών σε περιβάλλον υπολογιστικού νέφους με υπηρεσίες που βασίζονται σε περιέκτες

Initially, the study and analysis of cloud computing, cloud service providers, their service models, hybrid cloud and multi-cloud architectures, as well as virtualization technology in parallel with containers, was carried out. Distributed systems and their relationship with the cloud were then studied, with particular emphasis on independence in the cloud in view of thelock-in that a user may experience when trying to switch to the cloud. In addition, the topology of decentralization, the importance and practicality of edge computing through Cloudflare services was analyzed and explained, as well as the advantages along with the challenges of clusters in the cloud. Also, a study of the necessity of monitoring, collecting and analyzing log data in a cluster environment was carried out, and some best practices of approaching an application that collects and analyzes this data were presented. In addition, the tools and design of the final application developed were presented. Finally, an application that collects and analyzes log data (logs) in an environment, where the underlying information system is based on a dockerized infrastructure was developed and explained, with the ultimate goal of monitoring the system and controlling its proper operation.
Hybrid Cloud Architecture for Better Cloud Interoperability

Cloud computing's power house are the data centres which help to These are the one provide services to the client with its resources like hardware or system software etc. There are various advantages provided by cloud computing environment and to the biggest of them is reuse. Interoperability is the reuse of infrastructure, software, or platforms through cloud ubiquitously. We consider only the interoperability use cases which are 1. User Authentication 2. Workload Migration and Management 3. Data Migration and 4. Load Balancing. Hybrid cloud is analysed and studied for three main metrics like Average Response Time(ART), Average Data Centre Processing Time(ADCPT), and Grand Total Cost. Comparative Analysis of Public, Private and Hybrid Cloud is done for above three parameters. The simulation has been done using Cloud Analyst Toolkit using Netbeans as it provide the values as good as real time implementation of clouds. We conclude that Hybrid Cloud is ideal solution to cloud interoperability as compared to Public Cloud and Private Clouds with respect to Average Response Time, Processing Times, Grand Total Cost.
Performance Evaluation of Semantic Segmentation using Efficient Neural Network (ENet) on Various Traffic Scene Conditions

Object recognition, object detection, and semantic segmentation are fundamental components of the intelligent vehicle.Recently, there have been various methods proposed to create a reliable and accurate model to provide intelligent assistance to drivers.However, a reliable and accurate model in adverse conditions such as snow, rain, and fog remain a problem for advance driving assistance systems.The methods proposed only effectively solve the problem in a specific condition.Therefore, in this work, we focus on performing semantic segmentation in normal, rainy, foggy, and low light conditions using Efficient Neural Network (ENet) and ResNet18 and subsequently evaluating the trained model's performance in these conditions.In the experiment, we used a daytime dataset from CamVid and synthetically transformed the daytime dataset into rainy, foggy, and low light conditions.To verify the accuracy of the proposed method, the Intersection over Union (IoU) is used, and the result is elaborated in the section result and discussion.This approach only performs accurately during daylight.From the experiments, both methods do suffer from various conditions, but the ENet method performs better in certain conditions compared to ResNet18.
The Role-Playing Game Character

When we program multiple lines of code in a process, we refer to this as the program flow. As we run through our program, there is a flow, like the flow of a river that carries water from the origin to the river's end. A flow is how the program operates and what happens with values throughout our code.
A Circle Detection Algorithm Based on Ellipse Removal

In optical CCD detection, due to distortion, the circle will appear elliptical shape after projection onto a two-dimensional plane through perspective. In order to solve this problem, a circle detection algorithm based on ellipse de-falsification was proposed. The image was preprocessed by filtering, the axial ratio of the distorted circle was set, and the ten points randomly selected on the contour of the image were used to determine whether the circle was within the reasonable distortion range. Quadratic interpolation method was used to detect the sub-pixel edge of the contour point set, and based on the principle of Random Sampling Consensus (RANSAC), the outliers outside the threshold range were removed to achieve the effect of false elimination. Finally, the distorted circle was fitted by the least square method. Experimental results show that the detection error of this method is about 0.3%.
Standard of Four-Star Hotel Toilet Hygiene in Palembang

Since hosting the Sea Games and Asean Games, Palembang has become one of the tourist destinations in Indonesia. To support its role as a tourist destination, the existence of accommodation is clearly needed. The study was conducted to find out the standards of public toilet hygiene, which is one of the most important parts of a hotel. There are five hotels that are used as qualitative research objects. Data is taken through interviews, surveys and literature studies. The results obtained state that the standard in each hotel is perfect with a value of one hundred each. The advice given is that the cleanliness that has been achieved should be maintained.
Leveraging Prediction Confidence For Versatile Optimizations to CNNs

Modern convolutional neural networks (CNNs) incur huge computational and energy overheads. In this paper, we propose two techniques for inferring the confidence in the correctness of a prediction in the early layers of a CNN. The first technique uses a statistical approach, whereas the second technique requires retraining. We argue that prediction confidence estimation can enable diverse optimizations to CNNs. We demonstrate two optimizations. First, we predict selected images in early layers. This is possible because in a dataset, many images are easy to predict and they can be predicted in the early layers of a CNN. This reduces the average computation count at the cost of accuracy and parameter count. Second, we propose predicting only selected images for which the prediction-confidence is high. This reduces the coverage; however, the accuracy on the images that are predicted is higher. Our results with VGG16 and ResNet50 CNNs on the Caltech256 dataset show that our techniques are effective. For example, for ResNet, our first technique reduces the accuracy from 71.6% to 69.8% while reducing the computations by 14%. Similarly, with the second technique, on reducing the coverage from 100% to 90%, the accuracy is increased from 71.6% to 75.6%.
The contextual Teaching And Learning (CTL) Based On Contextual Teaching And Learning (CTL) Based On Student Worksheets

Teaching materials that are too monotonous and have no color in the LKPD which are currently used by many students in schools make students less interested in seeing and even reading them. The purpose of this study is how to design the development and feasibility of student worksheets (LKPD) Mathematics using a contextual teaching learning model on the subject of the Himpunan. Can the developed student worksheets (LKPD) use the contextual teaching learning model on the subject of the Association. This research is a development research (RnD) using a 4-D model, namely the definition stage, the design stage, the develop stage and the dissemination stage. However, it was modified to be 3-D because the application media did not reach the deployment. The research instrument is the RPP feasibility test questionnaire and the LKPD feasibility test questionnaire. Based on the results of expert validation, from the results of the final assessment on RPP validation by the three experts with an average of 4.4 with a percentage of 88.6%, namely the Very feasible category. The results of the final assessment on LKPD validation by the three experts with an average score of 4.5 with a percentage of 87.2%, namely the Very feasible category. Based on these data, it can be concluded that the development of Contextual Teaching Learning (CTL)-Based Student Worksheets on the subject matter of the Association is very feasible to use.