Reviewerly/exHarmony huggingface dataset:
Using semantics of textbook highlights to predict student comprehension and knowledge retention

As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.
Study on Vector Operations Transform into Matrix Operations

In this paper a useful technique, "Vector operation Transforms into Matrix operation" (VTM), is developed to simplify the manipulation of vector algebraic and differential operations. An efficient technique is developed to simplify the computations in the field of vector analysis. The evaluation of vector algebraic and differential operations becomes more simple and straightforward by simply transforming the vector operations into matrix operations. The matrix operations are especially useful when there are mixed coordinate basis involved in the vector operations. &nbsp
Clustering Image Noise Patterns By Embedding And Visualization For Unknown Source Camera Detection

We consider the problem of clustering a large set of images based on similarities of their noise patterns. Such clustering is necessary in forensic cases in which detection of common source of images is required, when the cameras are not physically available. We propose a novel method for clustering combining low dimensional embedding, visualization, and classical clustering of the dataset based on the similarity scores. We evaluate our method on the Dresden images database showing that the methodology is highly effective.
LibGuides: STEM Librarians South 2021: Program Abstracts

A resource guide to the STEM Librarians South Conference for 2021, held virtually, hosted by Texas State University Libraries.
A new defuzzifying process of type-2 fuzzy data modeling

In modeling uncertainty complex data that consisted of uncertainty in uncertainty data problem need specific theory to define the data problem. The specific theory to overcome this problem is the type2 fuzzy theory or type-2 fuzzy number since it deals with numbers. In order to obtain a crisp fuzzy solution, the processes such as fuzzifying, fuzzification, type-reduction and defuzzification are needed. But in this paper, the new defuzzification process will be proposed which can be used to reduce the calculation or execution time. Therefore, this new defuzzification process will give the crisp fuzzy result in which the type-reduction process is excluded from this processing. A numerical example will be given to give more comprehension of the existing and proposed methods.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE MAKE A MATCH UNTUK MENINGKATKAN KEMAMPUAN MEMBACA BAHASA INGGRIS PADA ANAK TUNAGRAHITA RINGAN DI SLB

Mental retardation is someone who experiences mental retardation which includes gradations ofmild, moderate, and severe which causes the disruption of the process of capturing, obtaining, andunderstanding information. Suffering of mentally retarded children stems from the limitations ofintelligence which makes it difficult for him to attend education programs in ordinary schoolsclassically. Besides that they experience retardation in adjusting to the environment, they are alsoless capable in thinking things that are abstract, difficult, and convoluted. The purpose of thisstudy is to improve the ability to read English in mild retarded children by applying the make amatch type of cooperative learning model. This research uses a qualitative approach. Datacollection is taken from the results of observations. This study uses classroom action researchprocedures. Data analysis techniques used are reduction, data display, and drawing conclusions.The results showed that the application of the make a match type of cooperative learning modelcould improve English reading skills in mild retarded children in SLB
On the Increasing the Motivations of Students-Biologists of Pedestrians in the Course of General Physics

The article examines the technologies of integration education as one of the regulators (motives) of its cognitive activity on the example of a course in general physics for students of biology. Integration learning reveals approaches to reflecting content through not separate isolated disciplines like physics and biology, but through integrated educational areas (modules). The study of natural science subjects in schools of the republic is more focused on the acquisition and demonstration of knowledge than on their application, as well as the development of the integrity of the practice’s characteristic of the natural sciences. These significant gaps in schooling can be filled in university practice by including in the curriculum a training module that allows you to move to interrelated, complementary, and integrated learning. The implementation of the training module during the academic year is not required and can be included in the structure of a regular training course, have the status of a meta-subject topic or section. Motivation is an important component of educational activity, through the implementation and through which the activation of educational activity and the development of the creative potential of future biology teachers to solve a number of professionally oriented tasks is carried out.
A binary variant of gravitational search algorithm and its application to windfarm layout optimization problem.

In the binary search space, GSA framework encounters the shortcomings of stagnation, diversity loss, premature convergence and high time complexity. To address these issues, a novel binary variant of GSA called `A novel neighbourhood archives embedded gravitational constant in GSA for binary search space (BNAGGSA)' is proposed in this paper. In BNAGGSA, the novel fitness-distance based social interaction strategy produces a self-adaptive step size mechanism through which the agent moves towards the optimal direction with the optimal step size, as per its current search requirement. The performance of the proposed algorithm is compared with the two binary variants of GSA over 23 well-known benchmark test problems. The experimental results and statistical analyses prove the supremacy of BNAGGSA over the compared algorithms. Furthermore, to check the applicability of the proposed algorithm in solving real-world applications, a windfarm layout optimization problem is considered. Two case studies with two different wind data sets of two different wind sites is considered for experiments.
Evidential Deep Learning for Open Set Action Recognition

In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamics and static bias of human actions. In this paper, we propose a Deep Evidential Action Recognition (DEAR) method to recognize actions in an open testing set. Specifically, we formulate the action recognition problem from the evidential deep learning (EDL) perspective and propose a novel model calibration method to regularize the EDL training. Besides, to mitigate the static bias of video representation, we propose a plug-and-play module to debias the learned representation through contrastive learning. Experimental results show that our DEAR method achieves consistent performance gain on multiple mainstream action recognition models and benchmarks. Code and pre-trained models are available at {\small{\url{https://www.rit.edu/actionlab/dear}}}.
Statistical Guarantees for Fairness Aware Plug-In Algorithms

A plug-in algorithm to estimate Bayes Optimal Classifiers for fairness-aware binary classification has been proposed in (Menon &amp; Williamson, 2018). However, the statistical efficacy of their approach has not been established. We prove that the plug-in algorithm is statistically consistent. We also derive finite sample guarantees associated with learning the Bayes Optimal Classifiers via the plug-in algorithm. Finally, we propose a protocol that modifies the plug-in approach, so as to simultaneously guarantee fairness and differential privacy with respect to a binary feature deemed sensitive.
Constraints on Cubic and $f(P)$ Gravity from the Cosmic Chronometers : Use of Machine Learning Algorithms.

In this work we perform an observational data analysis on Einsteinian cubic gravity and $f(P)$ gravity with the objective of constraining the parameter space of the theories. We use the 30 point $z-H(z)$ cosmic chronometer data as the observational tool for our analysis along with the BAO and the CMB peak parameters. The $\chi^2$ statistic is used for the fitting analysis and it is minimized to obtain the best fit values for the free model parameters. We have used the Markov chain Monte Carlo algorithm to obtain bounds for the free parameters. To achieve this we used the publicly available \textit{CosmoMC} code to put parameter bounds and subsequently generate contour plots for them with different confidence intervals. Besides finding the Hubble parameter $H$ in terms of the redshift $z$ theoretically from our gravity models, we have exercised correlation coefficients and two \textit{machine learning} models, namely the linear regression (LR) and artificial neural network (ANN), for the estimation of $H(z)$. For this purpose, we have developed a \textit{Python} package for finding the parameter space, performing the subsequent statistical analysis and prediction analysis using machine learning. We compared both of our theoretical and estimated values of $H(z)$ with the observations. It is seen that our theoretical and estimated models from machine learning performed significantly well when compared with the observations.
CSECU-DSG at SemEval-2021 Task 5: Leveraging Ensemble of Sequence Tagging Models for Toxic Spans Detection

The upsurge of prolific blogging and microblogging platforms enabled the abusers to spread negativity and threats greater than ever.Detecting the toxic portions substantially aids to moderate or exclude the abusive parts for maintaining sound online platforms.This paper describes our participation in the SemEval 2021 toxic span detection task.The task requires detecting spans that convey toxic remarks from the given text.We explore an ensemble of sequence labeling models including the BiLSTM-CRF, spaCy NER model with custom toxic tags, and fine-tuned BERT model to identify the toxic spans.Finally, a majority voting based fusion method is used to determine the unified toxic spans.Experimental results depict the competitive performance of our model among the participants.
PENGARUH KECEPATAN ALIRAN FLUIDA TERHADAP LAJU KOROSI PIPA GALVANIS JIS G3444-ANMAN

Corrosion phenomenon is a natural process that always occurs in metals, especially in galvanized pipes used for distribution of drinking water. Pipe is one component that is attacked by electrolytes or corrosive media such as water, crude oil, gas or other liquids. In addition to corrosive media, the flow rate also affects the corrosion rate. Corrosion rates can be measured by many methods. One method is losing weight. The purpose of this study is to determine the corrosion rate that occurs in galvanized pipe material used as a distribution pipe for drinking water in the H2SO4 environment. This study uses galvanized pipes as specimens with variations in testing time of 6 hours, 12 hours, 18 hours, 24 hours, 30 hours, 36 hours, composition of H2SO4 3%, 4%, and 5% and fluid flow velocity 0.128 m / s, 0.071 m / s and 0.038 m / s. Based on testing, weight loss increases with the length of the specimen in the solution and the faster the fluid flow rate, the higher the corrosion rate will occur.Keywords: Corrosion, galvanized pipe, H2SO4, velocity of fluid flow rate.
IBSI Education “SIWESI” Quality Measurement Based on System Technical Viewpoint

The need for information systems is increasing every year. The information system of an organization or company is useful for managing data to produce useful and effective information that supports activities and all levels of management that use it. The aspect of software quality is one of the important things in the development of a software. To determine the quality of software products, a measurement is needed. IBSI Education, one of which is a Tutoring Institution that uses “SiWeSi” to support administration, view schedules, see student progress, and so on. The success of the software that is built is based on whether or not the software works according to the objectives to be achieved. In the scientific concept of software engineering, the success of software is not only seen from the suitability of the resulting product to existing needs. The success of the software is also seen from the software development process. This study uses the McCall quality model because this method fulfills many components of the assessment and looks at quality from the user's point of view. The quality model used to measure the quality of SIWESI is determined from the results of the literature study.
A Critical Analysis of the Impacts of COVID-19 on the Development of Research

The development of research during COVID–19 times has been disrupted in myriad of ways where the long- term planned meetings between authorities and scholars were hung, the usual communication channel was disrupted due to the unplanned movement of people back to their hometowns. An uncertainty had encompassed the entire world where no one knew whether the alternative out of this shall be short or long- term. Since this issue is new in the field itself, a proper literature and methodology are missing. But one thing unanimously observed by the scholars all over the globe was the delay in working and submission of research projects due to the unprecedented barriers. Thus, this research paper tries to analyse the impact of COVID-19 in general and imposition of lockdown in particular on the research area. Then we attempt to analyse the solutions that were come up with to deal with these challenges and their varying effectiveness. We attempt to emphasis the need to make better use of technology in the field of higher education learning and research to deal with future challenges.
Hybrid Cloud: The Next Generation of EAI

Today’s large enterprises run on a wide variety of independent applications, data sources, and infrastructure which is either disconnected or tied together using point-to-point integration or integration using middleware technologies. With the focus on Move to Cloud many new applications are hosted on Public cloud while many legacy applications, for a variety of compliance and security reasons, are still hosted on On-premises data centers or in private cloud across different networks/firewalls. In recent years, Hybrid cloud is a promising paradigm in cloud computing space. It provides seamless integration between on-prem, private and public cloud services and applications. There are many advantages to move to a hybrid cloud as compared to the existing middleware network. Migration to hybrid is as simple as ‘a lift and shift’, provided, the applications are containerized and cloud-ready. The native non-cloud applications can still use the benefit of hybrid cloud by replacing legacy middleware tools and achieving a point-to-point integration with hybrid cloud clusters. In this article, we explore the current middleware landscape with it’s associated pitfalls and compare it with the Hybrid Cloud approach. Additionally, this article will cover a brief introduction about Redhat OpenShift, a hybrid cloud product from IBM.
Estimating Predictive Uncertainty Under Program Data Distribution Shift

Deep learning (DL) techniques have achieved great success in predictive accuracy in a variety of tasks, but deep neural networks (DNNs) are shown to produce highly overconfident scores for even abnormal samples. Well-defined uncertainty indicates whether a model's output should (or should not) be trusted and thus becomes critical in real-world scenarios which typically involves shifted input distributions due to many factors. Existing uncertainty approaches assume that testing samples from a different data distribution would induce unreliable model predictions thus have higher uncertainty scores. They quantify model uncertainty by calibrating DL model's confidence of a given input and evaluate the effectiveness in computer vision (CV) and natural language processing (NLP)-related tasks. However, their methodologies' reliability may be compromised under programming tasks due to difference in data representations and shift patterns. In this paper, we first define three different types of distribution shift in program data and build a large-scale shifted Java dataset. We implement two common programming language tasks on our dataset to study the effect of each distribution shift on DL model performance. We also propose a large-scale benchmark of existing state-of-the-art predictive uncertainty on programming tasks and investigate their effectiveness under data distribution shift. Experiments show that program distribution shift does degrade the DL model performance to varying degrees and that existing uncertainty methods all present certain limitations in quantifying uncertainty on program dataset.
Development Neural Network Models on Prediction of Human Purpose

As human-robotic (HRI) interactions are the, more main determinants are predicted. However, the large percentage of robotics follows strict guidelines that seriously limit their mobility and functionality. A key fact which contradicts the understanding of HRI is that robots are unable to understand social desires. The goal of this study is to enhance robotic intelligence by educating people to understand the purposes of humans. Contrary to the previous report, this paper offers a mechanism for predicting human intentions before a single procedure is completed. The test of bouncing a baseball at designated targets is carried out to check the process' performance. The deep learning approach proposed shows that neural networks (CNNs) can be completely used in a new situation. Experimental results suggest that three conventional machine training strategies compete with the proposed CNN-vote system. The CNN Vote Predictor achieves high test accuracy with comparatively less data available in the current context.
Approach to Pancytopenia

Pancytopenia is reduction in all the three cellular components of the blood, namely Red blood cells (RBCs), leucocytes (WBCs) and platelets. It is a common entity which is encountered by practitioners. The presentation is in the form of cytopenias leading to infections, anemia or bleeding manifestations. All the cases of pancytopenia need a through approach to reach to the cause of the same so that it can be it can be managed in the best possible manner. This paper will mainly concentrate on the approach to a case of pancytopenia.Keywords: Pancytopenia, Anemia, Leucopenia, Thrombocytopenia
Type-based Enforcement of Infinitary Trace Properties for Java

A common approach to improve software quality is to use programming guidelines to avoid common kinds of errors. In this paper, we consider the problem of enforcing guidelines for Featherweight Java (FJ). We formalize guidelines as sets of finite or infinite execution traces and develop a region-based type and effect system for FJ that can enforce such guidelines. We build on the work by Erbatur, Hofmann and Zălinescu, who presented a type system for verifying the finite event traces of terminating FJ programs. We refine this type system, separating region typing from FJ typing, and use ideas of Hofmann and Chen to extend it to capture also infinite traces produced by non-terminating programs. Our type and effect system can express properties of both finite and infinite traces and can compute information about the possible infinite traces of FJ programs. Specifically, the set of infinite traces of a method is constructed as the greatest fixed point of the operator which calculates the possible traces of method bodies. Our type inference algorithm is realized by working with the finitary abstraction of the system based on Büchi automata.
Code and Structure Editing for Teaching: A Case Study in using Bibliometrics to Guide Computer Science Research

Structure or projectional editors are a well-studied concept among researchers and some practitioners. They have the huge advantage of preventing syntax and in some cases type errors, and aid the discovery of syntax by users unfamiliar with a language. This begs the question: why are they not widely used in education? To answer this question we performed a systematic review of 57 papers and performed a bibliometric analysis which extended to 381 papers. From these we generated two hypotheses: (1) a lack of empirical evidence prevents educators from committing to this technology, and (2) existing tools have not been designed based on actual user needs as they would be if human-centered design principles were used. Given problems we encountered with existing resources to support a systematic review, and the role of bibliometric tools in overcoming those obstacles, we also detail our methods so that they may be used as a guide for researchers or graduate students unfamiliar with bibliometrics. In particular, we report on which tools provide reliable and plentiful information in the field of computer science, and which have insufficient coverage and interoperability issues.
Design and Analysis of Buoy Geometries for Wave Energy Generator

Objectives/Methods: The main objective here is to make an optimum design of buoy.Wave power generation is one of the extensive research fields all over the world.The ocean waves are usually converted into electrical energy by a device called a wave energy converter (WEC).The electricity generated depends upon the intensity of the waves.In a traditional way, the motion of a buoy that occurred in a vertical direction due to waves in water, can be converted into current generation.Here, the numerical simulation using the Ansys tool is done to find an optimum design of buoy shape to get maximum energy conversion.Findings: The simulations have shown that the drag force for the Oval shape is minimum 0.063 N and the first mode of frequency for the Conical shape is minimum 165 Hz for Point-Absorber type Wave Energy Converter.