Reviewerly/exHarmony huggingface dataset:
The Effect Of Padlet Application Towards Students’ Writing Skill of Recount Text At the Tenth Grade of SMAN 1 Malingping Lebak-Banten

This research paper aims to know whether there is a significant effect of Padlet application towards students’ writing skill of Recount text at the Tenth Grade of SMAN 1 Malingping Lebak-Banten in Academic Year 2021/2022. The research paper used the quantitative approach and quasi-experimental design. The technique in choosing the sample is cluster random sampling; the researcher used 10 IPA 4 (32 students) as class control and 10 IPA 2 (32 students) as class experimental. In the Collecting data technique, the researcher gives pre-test and post-test. In analyzing the data, the researcher used SPSS 22 statistical computation and used the result of the post-test both class control and experimental to analyze the hypotheses test. The researcher has found the result t count (-3.295) and Significant two-tailed = 0.002 &lt; α 0.05. Therefore, the researcher concluded that the Padlet application has a good effect on Students' Writing Skills
Enhancing students’ literacy skills through social constructivism-based literacy learning environment model

The aim of this study was to enhance students’ literacy skills through a social constructivism-based literacy learning environment model. The research method used was a quasi-experimental design with a non-equivalent pre-test and post-test control group design. Participants in this study were 74 third-year students of one of the public elementary schools. The instruments were the reading skill test and rubric instruments to assess narrative essay writing skills. Data were analysed through the average score and hypothesis test. The average scores of reading and writing in the experimental class were higher than the control class. Then, there is a significant influence of the social constructivism-based literacy learning environment model and regular learning on students’ literacy skills. Moreover, there is a significant difference between the influence of learning in the experimental and control classes. It can be concluded that a social constructivism-based literacy learning environment model can enhance students’ literacy skills. Keywords: Literacy learning environment model; literacy skills; social constructivism
Research on Intelligent System of Russian Language Teaching and Cultural Fusion based on Facial Expression Recognition Algorithm

In the process of carrying out Russian language and culture teaching, the cross-cultural communication system can be used to improve the overall teaching efficiency. Expression recognition uses computers to recognize facial expressions and understand people's psychological states; gaze tracking uses the characteristics and positions of eyes to determine the area of gaze. Taking facial expression recognition as the key technology, by capturing and recognizing learners' expressions, judging and understanding their emotional states, this paper discusses language and culture from the aspects of conceptual definition, the relationship between language and culture, and the relationship between language teaching and cultural teaching, the inseparable relationship between language teaching and cultural teaching.
Design of Auxiliary Monitoring Platform for College Students' Mental Health Education based on Internet Trace Data Mining

Combined with the network traces of scientific research groups, data are obtained through tools such as web crawlers, and key indicators of scientific research group ability evaluation are selected, and a scientific research ability cluster analysis method based on PAM algorithm is established. The psychological characteristics of college students are analyzed, and the psychological characteristics are introduced Some basic knowledge of evaluation and mental health, and fully analyzed and explained the necessity of developing this platform. Active traces and passive traces, dominant traces and invisible traces, etc.; and propose different data acquisition methods for different network traces. The platform uses object-oriented technology to develop the entire system, mainly using ASP.NET and AJAX technology to develop.
The Pandemic as a Catalyst for Digital Pathology Adoption as an LDT

Abstract Whole-slide images (WSI) are the basis for the application of artificial intelligence/machine learning and other informatics methods to histological diagnosis and will further blur the line separating anatomic and clinical pathology. FDA classified WSI systems for primary diagnosis as class III (highest risk) medical devices until 2017. This discouraged anatomic pathology laboratories at risk-averse domestic institutions like mine from investing in these digital pathology (DP) platforms. In 2017, FDA downgraded WSI to class II (moderate risk) when they de-novo approved a system marketed by Philips. We were not interested in that system at my institution, but the downgrade caused us to reset our perception of the risk of validating a RUO system for primary diagnosis. Cost remained a barrier. In April 2020, FDA issued temporary guidance stating they would not enforce premarket approval of WSI systems to facilitate pathologists working remotely during the SARS-CoV-2 pandemic. The guidance included a statement that “laboratories and hospitals consider performing a validation study.” In January 2021, FDA proposed making the temporary non-enforcement guidance permanent. So, in a little more than three years, WSI for primary diagnosis had gone from class III to exempted from pre-market approval! This nicely aligned the approval framework for WSI with the approval framework for our conventional optical microscopes, which are statutorily exempted from approval, and further reset our perception of risk. In April 2021, FDA withdrew the proposal to make non-enforcement permanent, but the temporary non-enforcement guidance is still in effect at the time of writing. Amid all this FDA activity, the College of American Pathologists updated and reissued their consensus guidelines for validating WSI systems for diagnostic purposes in March 2021. The narrative mentions the FDA’s recent approval of a few WSI systems and anticipates more, but the expert panel recommendations do not include any related to the approval status of systems. The reissue of this document reminded us that, as clinical laboratorians, we are capable of safely validating WSI as a laboratory-developed test and are supported in doing so by consensus guidelines from one of our leading professional organizations. In early 2021 we committed to funding a DP initiative to make WSI part of our routine histological process for 10% of our anatomic pathology cases. The initial capital investment is $1.5M. When realized, the microscope slides for designated pathology services will be transported directly from the cover slipper to a slide scanner and electronically distributed to pathologists using a clinical-grade image management system that we share with our radiology department. We made the decision to fund this in the context of the regulatory (decreased perception of risk), sociological (demand for remote telepathology), and technological (availability of scalable WSI systems) changes that occurred during the pandemic.
High‐frequency k‐mer counting at low memory footprint

Genomics data analysis requires efficient tools to address the vast amount of data generated by current next-generation sequencing technologies. K-mer counting works face difficulties in balancing high memory overhead with statistical precision. We designed a high-frequency k-mer statistical computation based on the Space Saving algorithm and a novel hash table structure, which reduces the memory overhead by 46% while ensuring high computational efficiency.
Diagnose colon disease by feature selection based on artificial neural network and group teaching optimization algorithm

There are various images available today for doctors to diagnose various diseases. The variety of medical imaging techniques increases the likelihood of early disease detection, allowing physicians to begin the treatment process more quickly. This article presents an intelligent approach to diagnosing colorectal cancer. The proposed method uses the group teaching optimization algorithm for feature selection to select the vital features of the image for plant disease. This feature uses to learn the multilayer artificial neural network to classify images into two categories, normal and malignant. Experiments show that the mean index of accuracy, sensitivity, and F1-Score in the proposed method on the color data set for the diagnosis of colon cancer is 92.72 %, 93.14 %, and 94.26 %, respectively. The proposed method's accuracy, precision, sensitivity, plant disease, and F1-Score in classifying Kvasir data set images are 96.42 %, 88.62 %, 92.69 %, and 89.54 %, respectively. Experiments show that the proposed method is more accurate in classifying plant disease images than 3Layer CNN, TFL, Random Forest, and CNN DropBlock.
Yantramanav: a humanoid to spread literacy among rural children through eGurukul education system

eGurukul system is derived from the ancient Indian’s Gurukul education, where students gather in one place and learn from their master. Early childhood is the right time for children to improve their foundation skills. Children in Urban areas are exposed to such an environment where parents put their kids in kindergarten school. In rural areas, children would be admitted in school only at the age of eight years. Also, in our current education system, many rural schools across India do not have sufficient teachers to educate the children. So, we propose an eGurukul system, teaching aid to engage the students to learn the basics of communication language without the presence of a teacher/human. The Yantramanav is designed to teach the students with basic language (English, Tamil & Hindi) alphabets writing, pronunciation of the alphabets and rhymes, also the basics of Mathematics to the rural kids under the age of eight. The Yantramanav is designed with a mini projector, camera & speakers as a teaching and monitoring aid during time of activity. The humanoid uses the projector as a major tool for teaching. The projector helps the students to gather in one place and make the students to be attentive during the activities. The humanoid takes the attendance using the camera with the help of face recognition. Humanoid uses the speakers’ to deliver audio contents during activities. The entire activities are controlled by a MCU Board, which operates based on the programmed instructions. Switches are used to choose the communication language and activities. The content can also be delivered to other regions of various districts through online mode at the same time.
A Popularity-Based Recommendation System Using Machine Learning

The main function of a recommendation engine is to filter the data and to use algorithms and to suggest the items which is more applicable to the users. It also identifies the past behaviour of the customer and planted on products that the users want to buy most. Now when a new user visits their e-commerce site, then it will not show any past of that new user. Now question is how that site come to know about the recommended products to the new user. There will be a solution that the bestselling product of that e-commerce site is the highly demanded products. There will be another solution that could be the product which gave the maximum product to the business to that e-commerce site. In this present work, we have created a model for popularity-based recommendation system using Machine Learning. Finally, we got top 5 popular products which are recommended for users using KNN algorithm.
Multi-level Domain Adaptation for Lane Detection

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, ie TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.
A Framework for Evaluating the Reusability of Software-As-A-Service Using Fuzzy Logic Approach

Abstract Cloud computing provides software and hardware resources in the form of cloud services with the help of internet. Cloud Computing is also coming forward as an efficient criterion for reuse. Software-as-a Service is one of the three types of cloud services which provides on demand software services on pay-as-you-go basis. In order to make SaaS more advantageous and to assist end users efficiently, there is a need to build a reusability model for SaaS. Reusability plays a very vital role in developing a product easily and efficiently. In this paper, we have proposed a software reusability estimation model for SaaS. We have been used Fuzzy Logic Methodology for evaluating the SaaS reusability. For this model, we used the SaaS reusability attributes i.e., Commonality, Accessibility, Availability, Customizability and Efficiency. Centroid Method is used for validating the results obtained from the proposed approach. This model evaluates the reusability of SaaS services with the required accuracy level. On the basis of experiment results, it has been proved out that machine learning techniques gives enhanced accuracy of estimation as compared to other reusability estimation techniques, found helpful and more useful for SaaS service providers as well as users.
Agricultural sector and economic development in Nigeria: Empirical evidence from Kebbi State

This research examined the effect of agricultural sector on economic development in Sokoto state, Nigeria. The research design used in this was survey. The population and sample size for this study are 142,741 farmers belonging to three senatorial districts in Kebbi state. However, a total of 399 were sampled using Taro Yamane‘s formula. A stratified sampling technique was used in selecting the three senatorial districts across the state. However, farmers were randomly selected for the study. The data collected from the respondents was analyzed using multiple regression. Empirical results show that food supply, poverty alleviation and job creation have significant effect on economic development. However, the results also indicate that there is positive insignificant relationship between foreign exchange and economic development. The study recommend that the current import and export regulations need to be revised in order to convince other countries to accept agricultural products from Nigeria.
Teachers’ and Students’ Perception on Acquiring English Vocabulary for Young Learners Using Mobile Apps in EFL Context

This study investigated the teachers’ and students’ perceptions on language acquisition for young learners using Mobile App in EFL context. Many ways have been found to assist young learners in acquiring English as a Foreign Language. This study applied survey where the teachers’ perceptions were obtained from the observation, while the students’ perceptions were taken from the observation questionnaire. The respondent and participants were taken from six elementary schools in which there were twelve English teachers and 569 students of grade 4th in Indonesia. The result showed that teachers thought most students were anxious and didn’t have self-esteem and a good motivation while learning English. The teachers’ perceptions don’t always match the students’ ones or vice-versa in learning English in EFL context. It is expected that the mismatches of teachers’ and students’ perception are not greater than the match ones.
An Analysis of Students Writing Skill in Recount Text at the Tenth Grade of SMAN 1 Panggarangan Lebak-Banten

The aims of this research is to analyze students’ writing skill in recount text based on content, organization, grammar, and mechanism in writing recount text at the class X MIPA 2 of SMAN 1 Panggarangan Lebak-Banten. Students' skills in writing recount text entered the excellent category. which, if explained in more detail, four students (40%) got a score of 80-100 (excellent), three students (30%) got a score of 60-69 (poor), two sudents (20%) got a score of 70-79 (good averange), and one student (10%) got a score of 25 – 59 (very poor). The results of the analysis of several aspect, namely content, organization, grammar, and mechanics, showed that students mistakes in writing recount texts were dominated by the grammar aspect where (60%) students were wrong in grammar placement, it was triggered by a lack of knowledge about the grammar used in recount text. Based on the results of interviews, most of them difficulties in writing recount text are caused by difficulties in finding ideas, difficulties in grammatical placement, difficulties in arranging the sentences in paragraphs because the ideas for writing are very limited, hard to compile words in sentences to be a paragraphs, and lack of vocabulary makes it difficult for students to write recount text.
flexBART: Flexible Bayesian regression trees with categorical predictors

Most implementations of Bayesian additive regression trees (BART) one-hot encode categorical predictors, replacing each one with several binary indicators, one for every level or category. Regression trees built with these indicators partition the discrete set of categorical levels by repeatedly removing one level at a time. Unfortunately, the vast majority of partitions cannot be built with this strategy, severely limiting BART's ability to partially pool data across groups of levels. Motivated by analyses of baseball data and neighborhood-level crime dynamics, we overcame this limitation by re-implementing BART with regression trees that can assign multiple levels to both branches of a decision tree node. To model spatial data aggregated into small regions, we further proposed a new decision rule prior that creates spatially contiguous regions by deleting a random edge from a random spanning tree of a suitably defined network. Our re-implementation, which is available in the flexBART package, often yields improved out-of-sample predictive performance and scales better to larger datasets than existing implementations of BART.
Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses

Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task. The main technical challenge associated with DFL is that it requires being able to differentiate through the optimization problem, which is difficult due to discontinuous solutions and other challenges. Past work has largely gotten around this issue by handcrafting task-specific surrogates to the original optimization problem that provide informative gradients when differentiated through. However, the need to handcraft surrogates for each new task limits the usability of DFL. In addition, there are often no guarantees about the convexity of the resulting surrogates and, as a result, training a predictive model using them can lead to inferior local optima. In this paper, we do away with surrogates altogether and instead learn loss functions that capture task-specific information. To the best of our knowledge, ours is the first approach that entirely replaces the optimization component of decision-focused learning with a loss that is automatically learned. Our approach (a) only requires access to a black-box oracle that can solve the optimization problem and is thus generalizable, and (b) can be convex by construction and so can be easily optimized over. We evaluate our approach on three resource allocation problems from the literature and find that our approach outperforms learning without taking into account task structure in all three domains, and even hand-crafted surrogates from the literature.
Multiple Packing: Lower Bounds via Infinite Constellations

We study the problem of high-dimensional multiple packing in Euclidean space. Multiple packing is a natural generalization of sphere packing and is defined as follows. Let $ N>0 $ and $ L\in\mathbb{Z}_{\ge2} $. A multiple packing is a set $\mathcal{C}$ of points in $ \mathbb{R}^n $ such that any point in $ \mathbb{R}^n $ lies in the intersection of at most $ L-1 $ balls of radius $ \sqrt{nN} $ around points in $ \mathcal{C} $. Given a well-known connection with coding theory, multiple packings can be viewed as the Euclidean analog of list-decodable codes, which are well-studied for finite fields. In this paper, we derive the best known lower bounds on the optimal density of list-decodable infinite constellations for constant $L$ under a stronger notion called average-radius multiple packing. To this end, we apply tools from high-dimensional geometry and large deviation theory.
StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects

Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. In this work, we focus on the problem of building physically-valid structures without step-by-step instructions. We propose StructDiffusion, which combines a diffusion model and an object-centric transformer to construct structures given partial-view point clouds and high-level language goals, such as "set the table". Our method can perform multiple challenging language-conditioned multi-step 3D planning tasks using one model. StructDiffusion even improves the success rate of assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model trained on specific structures. We show experiments on held-out objects in both simulation and on real-world rearrangement tasks. Importantly, we show how integrating both a diffusion model and a collision-discriminator model allows for improved generalization over other methods when rearranging previously-unseen objects. For videos and additional results, see our website: https://structdiffusion.github.io/.