Reviewerly/exHarmony huggingface dataset:
Mining Latent Classes for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we add an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and foreground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5i and 7.0% mIOU on COCO-20i at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://github.com/LiheYoung/MiningFSS.
LibGuides: Systematic reviews: Documenting search results and checklists

Overview of systematic review steps and resources to assist researchers conducting reviews.
Analysis of the Factors Influencing University Adult Learners’ Lifelong Education Satisfaction : Focusing on the Individual Background, Institutional Characteristics, and Learning Experience

The purpose of this study is to examine factors influencing adult learners' lifelong education satisfaction in terms of individual background, institutional characteristics linked to lifelong education policy structure, adult learners' learning experience within the university, thereby deriving implications for improvement quality of university lifelong education. To that end, we surveyed adult learners participating in the ‘Lifelong education at universities for the Future of Education(LiFE)’ project on learning experience and educational satisfaction, and the data of 2,359 students were used for analysis. Utilizing hierarchical multiple regression analysis, this study reveals the following results: First, although there are differences in the detailed areas of satisfaction, individual backgrounds, such as gender, age, major, and status of employment, and institutional characteristics linked to lifelong education projects showed a significant impact on adult learners' lifelong education satisfaction in university life. Second, adult learners' learning experience had a greater influence on lifelong education satisfaction than their individual background and institutional characteristics. Among the learning experiences, thinking ability promotion activities during class and the relative influence of and interaction with the university members were found to be high in particular. Based on the results of this study, the implications for improving the quality of lifelong education were suggested.
Powerpropagation: A sparsity inducing weight reparameterisation

The training of sparse neural networks is becoming an increasingly important tool for reducing the computational footprint of models at training and evaluation, as well enabling the effective scaling up of models. Whereas much work over the years has been dedicated to specialised pruning techniques, little attention has been paid to the inherent effect of gradient based training on model sparsity. In this work, we introduce Powerpropagation, a new weight-parameterisation for neural networks that leads to inherently sparse models. Exploiting the behaviour of gradient descent, our method gives rise to weight updates exhibiting a "rich get richer" dynamic, leaving low-magnitude parameters largely unaffected by learning. Models trained in this manner exhibit similar performance, but have a distribution with markedly higher density at zero, allowing more parameters to be pruned safely. Powerpropagation is general, intuitive, cheap and straight-forward to implement and can readily be combined with various other techniques. To highlight its versatility, we explore it in two very different settings: Firstly, following a recent line of work, we investigate its effect on sparse training for resource-constrained settings. Here, we combine Powerpropagation with a traditional weight-pruning technique as well as recent state-of-the-art sparse-to-sparse algorithms, showing superior performance on the ImageNet benchmark. Secondly, we advocate the use of sparsity in overcoming catastrophic forgetting, where compressed representations allow accommodating a large number of tasks at fixed model capacity. In all cases our reparameterisation considerably increases the efficacy of the off-the-shelf methods.
Permutation Compressors for Provably Faster Distributed Nonconvex Optimization

We study the MARINA method of Gorbunov et al (2021) -- the current state-of-the-art distributed non-convex optimization method in terms of theoretical communication complexity. Theoretical superiority of this method can be largely attributed to two sources: the use of a carefully engineered biased stochastic gradient estimator, which leads to a reduction in the number of communication rounds, and the reliance on {\em independent} stochastic communication compression operators, which leads to a reduction in the number of transmitted bits within each communication round. In this paper we i) extend the theory of MARINA to support a much wider class of potentially {\em correlated} compressors, extending the reach of the method beyond the classical independent compressors setting, ii) show that a new quantity, for which we coin the name {\em Hessian variance}, allows us to significantly refine the original analysis of MARINA without any additional assumptions, and iii) identify a special class of correlated compressors based on the idea of {\em random permutations}, for which we coin the term Perm$K$, the use of which leads to $O(\sqrt{n})$ (resp. $O(1 + d/\sqrt{n})$) improvement in the theoretical communication complexity of MARINA in the low Hessian variance regime when $d\geq n$ (resp. $d \leq n$), where $n$ is the number of workers and $d$ is the number of parameters describing the model we are learning. We corroborate our theoretical results with carefully engineered synthetic experiments with minimizing the average of nonconvex quadratics, and on autoencoder training with the MNIST dataset.
PERANCANGAN APLIKASI DATABASE PENJUALAN PADA USAHA YULIWARDI’S SNACK PADANG

Small and Micro Enterprises (SME) are type of enterprises that can be carried out by the community to improve their welfare. The most in-demand enterprise of consumers is the food enterprise, one of which is Yuliwardi's Snack, which then chosen as the subject of this study. The challenge faced by enterprises is inconsistency of their sales records. This study aims to design a bookkeeping application that is needed by enterprises with an order-based sales system. The data were collected through semi-structured interview with the owner of this SME. Whereas the design analysis in this study was based on PIECES analysis in constructing a database design framework. The sales application was created using Microsoft Access 2013.  The design of the sales application program consisted of calculating sales orders, recapitulating daily and monthly sales values, and generating receipts in any sales transaction. In view of this, the benefit obtained by the enterprises was the efficiency of recording monthly sales.
Novel Initial Parameters Computation for EM algorithm-based Univariate Asymmetric Generalized Gaussian Mixture

In histogram-based image segmentation, the Asymmetric Generalized Mixture Model (AGGMM) is a powerful tool to fit accurately the real images histograms by handling, among others, any asymmetry of the modes. However, the Expectation Maximization (EM) algorithm, used for the estimation of the mixture model parameters, is known to be very sensitive to starting conditions and can lead to erroneous segmentation results when the initialization is not adequate. In this paper, we propose a new method to initialize the AGGMM. This method is based on geometrical aspects of the histogram. First experimentations implying synthetic images generated by Asymmetric Generalized Mixture Distribution (AGGD) model, reveal a good recovering of the input mixture parameters when applying the proposed method. Second experimentations involving real-world images have shown, how the initial parameters computed by the proposed method permit to achieve better histogram fitting with less EM algorithm running time in comparison to other initialization methods.
Assistance in early detection of maceration in diabetic foot wounds using flir one smartphone thermography

One of the factors that affect wound healing is maceration which can increase wound healing time. However, the results of observations revealed that many maceration wounds were treated with a washing process and an inappropriate selection of dressings. This community service was carried out by transferring knowledge and skills to wound specialist nurses to detect early maceration using flir one smartphone thermography. Based on the results of the evaluation, the nurse can explain the definition and signs of maceration and can demonstrate using a maceration detection tool in the wound with appropriate and correct steps.
Sistem Pakar Mendeteksi Penyakit Ikan Arwana Asia Menggunakan Metode NaÃ¯ve Bayes

Asian Arowana fish are freshwater fish that are spread across Southeast Asia. Many Indonesian people maintain Arowana fish because of its beauty and also has a high enough price. But in the process of cultivation, the Asian Arowana Fish can be attacked by more than one disease that can threaten the survival of the Arowana fish. With the rapid advancement of technology that is taking place and based on these problems, the author implements an Expert System to detect what diseases attack these fish with symptoms and data obtained from experts so that we know what steps must be taken for the prevention and sustainability of Arowana fish cultivation. The method used is the NaÃ¯ve Bayes Method, which is a probability statistical method that can detect whether a fish is exposed to a disease with pre-existing web-based odds.
Twitter Sentiment Analysis Using Dempster Shafer Algorithm Based Feature Selection and One Against All Multiclass SVM Classifier

Rapid development of social media and internet technologies has conquered much intention on sentiment analysis. Twitter is one of the social media used by numerous users about some subject matter in the form of tweets. Twitter Sentiment Analysis (TSA) is the way of finding sentiments and opinions in the tweets. Still, achieving high accuracy in TSA is difficult due to characteristics of twitter data such as spelling errors, abbreviation and special characters. Therefore, our main intention is to attain high accuracy in TSA. To achieve this intention, we majorly concentrated on five processes: Data cleaning, Preprocessing, Feature extraction, Feature Selection and Classification. In the data cleaning stage, we perform four major processes: URL removal, Username Removal, Punctuation Removal and Spell Correction. By executing the data cleaning process, this work enhances the efficacy of TSA. To increase the accuracy in TSA, we adopt preprocessing where tokenization, stop word removal, lemmatization and stemming, acronyms expansion, slangs correction, split attached word, and POS tagging. In order to improve the classification accuracy, we execute the feature extraction process where eight features are extracted. A key bottleneck in TSA is a huge amount of data which makes difficulties in the training of ML during sentiment classification. To tackle this hurdle, we select the best features from the extracted features using the Dempster Shafer algorithm. Sentiments are classified using the One against All-Multiclass Support Vector Machine (OA2 -SVM) algorithm. It classifies sentiments into five classes: Strongly Positive, Strongly Negative, Positive, Negative and Neutral. We implement these processes using public tweets collected from the open repository. The results obtained from the simulation are auspicious in terms of upcoming metrics including, Accuracy, Precision, Recall, F-Measure and Error Rate. From the comparison results, it perceived that our method enhances 25% in Accuracy and Precision, 30% in Recall, 20% in F-Measure and reduces 29% in error rate compared to the existing methods including LAN2 FIS, GA and HCS.
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration

In complex environments with high dimension, training a reinforcement learning (RL) model from scratch often suffers from lengthy and tedious collection of agent-environment interactions. Instead, leveraging expert demonstration to guide RL agent can boost sample efficiency and improve final convergence. In order to better integrate expert prior with on-policy RL models, we propose a generic framework for Learning from Demonstration (LfD) based on actor-critic algorithms. Technically, we first employ K-Means clustering to evaluate the similarity of sampled exploration with demonstration data. Then we increase the likelihood of actions in similar frames by modifying the gradient update strategy to leverage demonstration. We conduct experiments on 4 standard benchmark environments in Mujoco and 2 self-designed robotic environments. Results show that, under certain condition, our algorithm can improve sample efficiency by 20% ~ 40%. By combining our framework with on-policy algorithms, RL models can accelerate convergence and obtain better final mean episode rewards especially in complex robotic context where interactions are expensive.
Implementation of Moving Object Tracker System

The field of computer vision is increasingly becoming an active area of research with tremendous efforts being put towards giving computers the capability of sight. As human beings we are able to see, distinguish between different objects based on their unique features and even trace their movements if they are within our view. For computers to really see they also need to have the capability of identifying different objects and equally track them. This paper focuses on that aspect of identifying objects which the user chooses; the object chosen is differentiated from other objects by comparison of pixel characteristics. The chosen object is then to be tracked with a bounding box for ease of identification of the object's location. A real time video feed captured by a web camera is to be utilized and it’s from this environment visible within the camera view that an object is to be selected and tracked. The scope of this paper mainly focuses on the development of a software application that will achieve real time object tracking. The software module will allow the user to identify the object of interest someone wishes to track, while the algorithm employed will enable noise and size filtering for ease of tracking of the object.
An Appraisal of Diverse Image Augmentation Methodologies

Image augmentation is very significant, challenging methods in image exploration. The intention of augmentation of the image is to expand the graphical image form, or to give a superior renovated illustration for imminent programmed image treating. Various images such as aerial images, images of satellite, medical images, etc., undergo for noise and meager contrast. Therefore, it is obligatory to eliminate the noise and to augment the contrast in order to upsurge image superiority. Among imperative phases in medical images exposure and investigation is an image development practice that develops the worth or lucidity of images for hominoid seeing, eliminating noise and blurring, rise contrast, and enlightening minutiae are cases of enrichment maneuvers. The augmentation practice varies from one arena to another rendering to its purpose. The present methods of image augmentation could be divided into 2 groupings: frequency, spatial domain enrichment. This work offers a brief summary of image augmentation methods in spatial territory. More precisely, paper classifies processing methods centered representative techniques of Image augmentation. Hence an impact of this paper is to categorize and analyze image augmentation methods, effort for evaluation of inadequacies and common desires in an arena of dynamic investigation and at last paper focuses on auspicious information on exploration for image augmentation for imminent study.
Inovasi Pembelajaran Metode Konvensional dikombinasikan dengan Metode PBL

This article was to find out the innovation of students by using combination of Conventional Method and PBL Method. Based on the result of Classroom Action Research found that there was a differences between pre-test, post-test whether Cycle I or Cycle II. Based on the result, the writer concluded that the result of this research was the result of post-test in Cycle I was lower than post-test in Cycle II with the combination of Conventional Method and PBL Method. Abstrak. Artikel ini bertujuan untuk mengetahui inovasi siswa dengan menggunakan kombinasi Metode Konvensional dan Metode PBL. Berdasarkan hasil Penelitian Tindakan Kelas ditemukan bahwa ada perbedaan antara pre-test, post-test apakah Siklus I atau Siklus II. Berdasarkan hasil tersebut, penulis menyimpulkan bahwa hasil dari penelitian ini adalah hasil posttest siklus I lebih rendah dari posttest siklus II kombinasi metode konvensional dan metode PBL.
Comb-based photonic neural population for parallel and nonlinear processing

It is believed that neural information representation and processing relies on the neural population instead of a single neuron. In neuromorphic photonics, photonic neurons in the form of nonlinear responses have been extensively studied in single devices and temporal nodes. However, to construct a photonic neural population (PNP), the process of scaling up and massive interconnections remain challenging considering the physical complexity and response latency. Here, we propose a comb-based PNP interconnected by carrier coupling with superior scalability. Two unique properties of neural population are theoretically and experimentally demonstrated in the comb-based PNP, including nonlinear response curves and population activities coding. A classification task of three input patterns with dual radio-frequency (RF) tones is successfully implemented in a real-time manner, which manifests the comb-based PNP can make effective use of the ultra-broad bandwidth of photonics for parallel and nonlinear processing.
STUDENTS’ EXPOSITORY WRITING: A CASE STUDY IN PARAGRAPH WRITING CLASS

This paper is entitled Students’ Expository Writing: A Case Study in Paragraph Writing Class. It aims at investigating whether the students' expository writing has met the correct writing structure or not. In addition, this study determines what kind of writing patterns are used by students in conveying their writing. The method used in this research is descriptive analysis method. A method used to find the elements and characteristics of a phenomenon, including correctness of structure of expository writing and types of expository writing used by students in conveying their ideas. The data are taken from students’ expository writing with different topics suggested by lecturers at beginning of the lecturing. The results shows that (1) students have fulfilled the structure of expository writing, which consists of an Introductory Paragraph, Body of Paragraph and closed with a conclusion and (2) students use 4 types of paragraphs in conveying their ideas, namely descriptive composition, cause and effect essay, compare and contrast essay and how to do something essay. More specifically, topic 1 (Education) uses the types of compare and contrast essay and descriptive composition, topic 2 (Healthy lifestyle) uses the types of descriptive composition and how to do something essay. Lastly, topic 3 (The Effect of Technology) uses the types of cause and effect essay and the descriptive composition.
A training on digital book production for teachers at Lab School of UPI, Purwakarta

The Community Service Program (PkM) is designed to provide practical training on digital book production for teachers at Lab School of UPI, Purwakarta. This project was conducted offline with 9 participants, under the COVID-19 health protocol. The material was presented in 3 forms, termed face-to-face, Zoom video conferencing and the training video. Also, 4 open-source web-based softwares were explored, including Canva for book covers, Vocaroo as an audio explainer and Animaker for motion graphics-based videos. The results of these three applications were combined by Sigil into an EPUB-formatted digital book. Subsequently, the training employed an interactive format, with a combination of classical and practical activities. Based on the overall results, the participants were known to obtain extensive knowledge and hands-on experience in digital book compilation. These teachers were able to operate the digital tools to create book covers, online audio recording and animated videos, as well as integrate the independent copies into digital books. Furthermore, the material content, delivery method and practical implementation showed a satisfactory outcome by 86.2, 85 and 88%, respectively. Therefore, the newly acquired skills are expected to reflect on the teaching materials for online learning at SD Lab School of UPI Purwakarta, during the COVID-19 pandemic.
Library Guides: COMM/SOC 20N: Critical Media Literacy: Databases, Journals & Newspapers

This guide is for students in Penn State Harrisburg's COMM/SOC 20N (Spring 2021). It includes sources, search tips, and other tools needed to complete several assignments.
La virtualidad como contexto para el surgimiento de la Olimpiada Internacional de Matemática para Educación Primaria (OLIMPRI)

Students who present some talent that allows them to have an outstanding performance in the artistic, sports or academic area, should have opportunities for encouragement and support to develop those skills to the maximum. In the case of gifted or talent in mathematics, there are events in which healthy competition is fostered between students of the same age, in stages: regional, national and international. At the high school, these types of activities are more common, there has even been an International Olympiad for more than sixty years, although it is preferable to encourage these capacities from the first stages of schooling for an early stimulation and better development of their talent. That is why, taking advantage of the virtual context to which, the pandemic caused by the COVID-19 virus led us, the initiative arises to hold an International Mathematics Olympiad at the Primary level, with which a space is fostered to learn to enjoy mathematics and at the same time, generate cooperation ties between countries. In this way, in 2020 the first edition of OLIMPRI take place with the participation of nine countries and during 2021, the joint will continue to have the second edition, for which the participation of fourteen countries has already been confirmed.