Reviewerly/exHarmony huggingface dataset:
WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction

This paper describes our contribution to the WASSA 2021 shared task on Empathy Prediction and Emotion Classification. The broad goal of this task was to model an empathy score, a distress score and the overall level of emotion of an essay written in response to a newspaper article associated with harm to someone. We have used the ELECTRA model abundantly and also advanced deep learning approaches like multi-task learning. Additionally, we also leveraged standard machine learning techniques like ensembling. Our system achieves a Pearson Correlation Coefficient of 0.533 on sub-task I and a macro F1 score of 0.5528 on sub-task II. We ranked 1st in Emotion Classification sub-task and 3rd in Empathy Prediction sub-task
Website Satisfaction Analysis Using Usability Scale System In Cilongok District Office

Abstract Indonesia is a country with a number of governmental areas where there are 416 districts, 98 cities, 7,094 sub-districts, 8,490 sub-districts, and 74,957 villages in Indonesia. website-based, but there are still problems with the satisfaction of the services used by users or the community. Basically, all website-based information services must be in tune with the needs of what users will do on the website, so from this problem the researcher proposes a solution to creating website-based information services according to user requests both in terms of user interface and user experience. In the existing problem, the researcher uses the system usability scale (SUS) method to find out the points that must be corrected for the convenience of using information services in Cilongok district. The SUS method is very suitable for measuring the satisfaction level of users of information-based services in Cilongok sub-district so that users feel satisfied, feel that the information is very clear and make the community more familiar with website-based information technology. Keywords : Website, Satisfaction Level and System Usability Scale (SUS)
A Session-based Interaction model for Cloud Service

Being executed in the Internet space, cloud services provide elastic computing resource to satisfy customers’ demand. Since single service usually can't suit their complex need, customers need to assemble multiple services into one service workflow which coordinates individual service to fulfill the specific task by means of service interaction. Obviously, the interaction mechanism of cloud service is the glue of workflow and plays an important role in service workflow. However, owing to the dynamic characteristic of the Internet, cloud service interaction is complicated and volatile. Hence, the question how to describe and formalize the complex interaction of cloud service workflow is a non-trivial work, which has directly influence on the overall design of cloud service architecture engineering and its performance.
Implementasi Model Circuit Learning dalam Meningkatkan Keterampilan Menulis Teks Nonfiksi pada Peserta Didik Kelas V Sekolah Dasar

Abstract. This research aims to improve nonfiction text writing skills by implementing a Circuit Learning Model. The Type of this research is Classroom Action Research with 2 cycles. The subject of this research were 24 fifth grade students of Ta’mirul Islam Surakarta Primary School during 2018/2019 academic year. Data collection in this research uses interviews, tests, observations, and documentation with triangulation and data analysis of content. Qualitative data analysis used Miles- Huberman interactive analysis model. While, quantitative data analysis are the result of nonfiction text writing skills. The pre-action cycle resulting in percentage of 4,2% in classically, continued with the first cycle with the percentage of 33,3% in classical and in the last cycle resulting in percentage of 95,5% classically. Based on the result obtained, it can be concluded that nonfiction text writing skills of Ta’mirul Islam Surakarta Primary School in the 2019/ 2020 academic year can be upgraded through the implementation of the Circuit Learning Model
Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm

Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years. While effective and prevalent, these models are usually prohibitively large for resource-limited deployment scenarios. A thread of research has thus been working on applying network pruning techniques under the pretrain-then-finetune paradigm widely adopted in NLP. However, the existing pruning results on benchmark transformers, such as BERT, are not as remarkable as the pruning results in the literature of convolutional neural networks (CNNs). In particular, common wisdom in pruning CNN states that sparse pruning technique compresses a model more than that obtained by reducing number of channels and layers (Elsen et al., 2020; Zhu and Gupta, 2017), while existing works on sparse pruning of BERT yields inferior results than its small-dense counterparts such as TinyBERT (Jiao et al., 2020). In this work, we aim to fill this gap by studying how knowledge are transferred and lost during the pre-train, fine-tune, and pruning process, and proposing a knowledge-aware sparse pruning process that achieves significantly superior results than existing literature. We show for the first time that sparse pruning compresses a BERT model significantly more than reducing its number of channels and layers. Experiments on multiple data sets of GLUE benchmark show that our method outperforms the leading competitors with a 20-times weight/FLOPs compression and neglectable loss in prediction accuracy.
Problems and Countermeasures in Natural Language Processing Evaluation

Evaluation in natural language processing guides and promotes research on models and methods. In recent years, new evalua-tion data sets and evaluation tasks have been continuously proposed. At the same time, a series of problems exposed by ex-isting evaluation have also restricted the progress of natural language processing technology. Starting from the concept, com-position, development and meaning of natural language evaluation, this article classifies and summarizes the tasks and char-acteristics of mainstream natural language evaluation, and then summarizes the problems and causes of natural language pro-cessing evaluation. Finally, this article refers to the human language ability evaluation standard, puts forward the concept of human-like machine language ability evaluation, and proposes a series of basic principles and implementation ideas for hu-man-like machine language ability evaluation from the three aspects of reliability, difficulty and validity.
Towards super-collated gridded ACSPO SST product from multiple geostationary satellites (L3S-GEO)

In response to users’ needs, NOAA is developing a suite of multi-sensor gridded (0.02º resolution) super-collated (L3S) Advanced Clear Sky Processor for Ocean (ACSPO) SST products, which reduce number of files and data volume, while retaining high spatial and temporal resolution of the data. In 2020, two ACSPO L3S-LEO products were initiated from Low Earth Orbit satellites. Our next priority is the development of an hourly super-collated product generated from geostationary satellites (L3S-GEO). This paper describes the initial implementation of the L3S-GEO product, including end-to-end data production and initial evaluation, including consistency of satellite-specific GEO products where they overlap.
Social conditions, defining premises of the learning context

Having analyzed the state of modern society and formal education, we have highlighted the following phenomena: the irrelevance, the accelerated change, the complexity, the erosion of human relations, the global crisis of education, the disappearance of responsibility, the adaptive stress etc. These findings are grounds for foreseeing a reconfiguration of the learning process in language education. The unique purpose of education/learning is social integration by personal fulfillment. In order to achieve this, the educational process should be based on promoting learning through communion, which aims to form sociable students, aware of and responsible for the communicative act.
A De-raining semantic segmentation network for real-time foreground segmentation

Few researches have been proposed specifically for real-time semantic segmentation in rainy environments. However, the demand in this area is huge and it is challenging for lightweight networks. Therefore, this paper proposes a lightweight network which is specially designed for the foreground segmentation in rainy environments, named De-raining Semantic Segmentation Network (DRSNet). By analyzing the characteristics of raindrops, the MultiScaleSE Block is targetedly designed to encode the input image, it uses multi-scale dilated convolutions to increase the receptive field, and SE attention mechanism to learn the weights of each channels. In order to combine semantic information between different encoder and decoder layers, it is proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder employs bilinear interpolation and the output passes through pointwise convolution, then added element-wise to the lower semantic layer of decoder. According to the control experiments, the performances of MultiScaleSE Block and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively are improved to a certain degree on the Foreground Accuracy index. The parameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M and 0.20GFLOPs separately. The state-of-the-art results and real-time performances are achieved on both the UESTC all-day Scenery add rain (UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain) benchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index is also the best among the similar magnitude networks on both benchmarks.
THE MORPHOPHONEMICS IN REJANG LANGUAGE

This study aims to describe morphophonemic in Rejang language. Data is collected by library method. The literature is in the form of a Rejang-Indonesian dictionary book compiled by Chairil Chile et al. Considering that morphophonemic is studying phoneme changes that arise as a result of meeting morphemes with other morphemes, a morphological row is used to determine morphemes. Therefore, it can be stated that in the Rejang language there are three morphophonemic processes, namely 1) the phoneme change process, 2) the phoneme addition process, 3) the phoneme loss process. In connection with the morphophonemic process in Rejang there is one thing that needs attention, namely the morphophonemic process of nasal affix / N / morpheme. Therefore, the morphophonemic process depends on the basic shape conditions that follow. So that the Rejang language morphophonemic rules can be stated. Keywords: Morphophonemic, and Rejang language
Peningkatan Keterampilan Menulis Deskripsi Tokoh Pahlawan melalui Model Pembelajaran Talking Stick Siswa Kelas IV SDN Tegalasri 4

The purpose of this research is to describe the application of the talking stick learning model and the improvement of writing skills. The type of research used is Classroom Action Research (PTK). The research subjects were fourth grade students of SDN Tegalasri 4, Wlingi District, Blitar Regency. The results of the research cycle I meeting 1 an average of 66 with a percentage of 45% completeness of the 29 students, meeting 2 an average of 67 with a percentage of 52% completeness. Cycle II meeting 1 total 2218 average 76 69%, Meeting 2 average 82 with percentage completeness 83%. The conclusion is that the talking stick learning model is able to improve the writing skills of a hero character description while also motivating students to learn to write and can stimulate students' creativity in expressing ideas.
FEATURES OF FORMATION OF ENGINEERING STEM COMPONENT IN TEACHING PHYSICS AND TECHNICAL DISCIPLINES IN INNOVATIVE EDUCATIONAL AND SCIENTIFIC ENVIRONMENT

The article considers the current problems of implementation of STEM-technologies and the separation of engineering STEM-component, which is important for the formation of STEM-competencies in subjects of study. Teaching physics to students of non-physics specialties in technical institutions of higher education in the context of reforming physics education is carried out, as a rule, in the I–II courses. The content of the discipline “Physics” needs to be modernized in accordance with modern achievements of physics science and didactic principles of construction of the course of physics, considering the fundamentals, scientific, continuity and interdisciplinary connections. Therefore, the modernization of higher education in Ukraine requires taking into account the general trends in the development of higher education systems in the context of globalization and European integration processes, namely the trends in the development of STEM-education. It is established that the change in the field of higher education, in particular technics, taking into account the development of STEM-education involves revising the concept of training in each field, so modernizing the content of education requires updating the educational base (goals, content, methods, forms and means), through which in the future the implementation of modern innovative STEM-approaches will be carried out. Taking into account current trends and the main directions of improving the educational process, the method of teaching physics to effectively acquaint students with the basics of physics, which is required for further study of professionally oriented disciplines, should be aimed not only at qualitative, scientifically and methodologically sound teaching, provided by the educational activities of the teacher, and mainly to intensify the independent educational and research activities of students. Such a technique should develop and stimulate interest in knowledge and understanding of physics, their application in explaining the phenomena and processes of the microworld and the world as a whole and give students an effective system of knowledge, skills and form a natural worldview. The article outlines the peculiarities of the formation of the engineering STEM-component and gives an example of consideration of the work of the physics workshop with programming elements. It is determined that the physics experiment is an important factor in the development of STEM-education in higher education institutions of technical profile and methods of teaching physics. The use of STEM-technologies activates the independent cognitive-search activity of students before studying physics and technical disciplines.
Need to Broad-Base Higher Education

Establishment of human intellect and expertise necessitates wide scope of knowledge and understanding. Post qualification, many practical jobs wherein the required expertise includes social understanding and abilities which are not included in STEM subjects. Organization needs civic sense, team work, understanding, compassion and good communication. In reality, the market needs that the product of higher education being employed has these abilities so as to be able to get well within the organizational work forces.&#x0D; A well-developed human package is hardly complete without appropriate knowledge and insight of, for example, philosophy, anthropology, economics, law, psychology, belief system/religions, geography , history and politics, amongst others. Without these, the very purpose of one's role within an organization, and the society at large, remains very incomplete.&#x0D; Obtaining passing marks in merely a few science specific subjects as usually defined in narrowly defined curricular stop way short of meeting the market demands. Inclusion of some knowledge of social sciences, humanities and arts would help create higher educated people with better understanding of the human condition and the society. Better understanding is the essential prerequisite for being comfortable with oneself and one's society.&#x0D; &#x0D; On the same account, integrative education and research needs to be put in place to bridge knowledge, modes of inquiring and pedagogies from multiple disciplines, within every higher education program.
Learning to Jointly Deblur, Demosaick and Denoise Raw Images

We address the problem of non-blind deblurring and demosaicking of noisy raw images. We adapt an existing learning-based approach to RGB image deblurring to handle raw images by introducing a new interpretable module that jointly demosaicks and deblurs them. We train this model on RGB images converted into raw ones following a realistic invertible camera pipeline. We demonstrate the effectiveness of this model over two-stage approaches stacking demosaicking and deblurring modules on quantitive benchmarks. We also apply our approach to remove a camera's inherent blur (its color-dependent point-spread function) from real images, in essence deblurring sharp images.
Can Public Health Workforce Competency and Capacity be built through an Agent-based Online, Personalized Intelligent Tutoring System?

The COVID-19 pandemic hit the United States in 2020 resulting in a public health caseload surge precipitating deployment of military and federal medical units, states issuing emergency orders to engage retired medical professionals, and novice or inadequately trained healthcare workers thrust into service to meet the pressing need The novelty and scope of the pandemic exposed a gap in the competency and the surge capacity of the public health workforce to address the societal needs during the pandemic This research investigated the capability of an agent-based, online personalized (AOP) intelligent tutoring system (ITS) that adaptively uses aptitude treatment interaction (ATI) to deliver public health workforce training in a prescribed health regime and assure their competency This research also considers the ability of such an AOP ITS to support rapidly surging capacity of the public workforce to scale to meet healthcare demands while remaining accessible and flexible enough to adapt to changing healthcare guidance Findings indicate such a system increases participant performance while providing a high level of acceptance, ease of use by users, and competency assurance However, discussion of our findings indicates limited potential for an AOP ITS using the current ATI paradigm to make a major contribution to adding public health workforce surge capacity unless workforce members are directed to utilize it and technology barriers in the current public health IT infrastructure are overcome
Holmes: An Efficient and Lightweight Semantic Based Anomalous Email Detector

Email threat is a serious issue for enterprise security. The threat can be in various malicious forms, such as phishing, fraud, blackmail and malvertisement. The traditional anti-spam gateway often maintains a greylist to filter out unexpected emails based on suspicious vocabularies present in the email's subject and contents. However, this type of signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various evolving malicious payloads. To address the problem, in this paper, we present Holmes, an efficient and lightweight semantic based engine for anomalous email detection. Holmes can convert each email event log into a sentence through word embedding and then identify abnormalities that deviate from a historical baseline based on those translated sentences. We have evaluated the performance of Holmes in a real-world enterprise environment, where around 5,000 emails are sent/received each day. In our experiments, Holmes shows a high capability to detect email threats, especially those that cannot be handled by the enterprise anti-spam gateway. It is also demonstrated through our experiment that Holmes can discover more concealed malicious emails that are immune from several commercial detection tools.
Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information

In many real-world problems, we want to infer some property of an expensive black-box function $f$, given a budget of $T$ function evaluations. One example is budget constrained global optimization of $f$, for which Bayesian optimization is a popular method. Other properties of interest include local optima, level sets, integrals, or graph-structured information induced by $f$. Often, we can find an algorithm $\mathcal{A}$ to compute the desired property, but it may require far more than $T$ queries to execute. Given such an $\mathcal{A}$, and a prior distribution over $f$, we refer to the problem of inferring the output of $\mathcal{A}$ using $T$ evaluations as Bayesian Algorithm Execution (BAX). To tackle this problem, we present a procedure, InfoBAX, that sequentially chooses queries that maximize mutual information with respect to the algorithm's output. Applying this to Dijkstra's algorithm, for instance, we infer shortest paths in synthetic and real-world graphs with black-box edge costs. Using evolution strategies, we yield variants of Bayesian optimization that target local, rather than global, optima. On these problems, InfoBAX uses up to 500 times fewer queries to $f$ than required by the original algorithm. Our method is closely connected to other Bayesian optimal experimental design procedures such as entropy search methods and optimal sensor placement using Gaussian processes.
A Fuzzy Logic in Election Sentiment Analysis: Comparison Between Fuzzy Naïve Bayes and Fuzzy Sentiment using CNN

Sentiment analysis is an analysis with an objective to identify like, dislike, comments, opinion, or feedback on certain content which will be categorized into positive, negative, or neutral. In general selection, sentiment analysis widely known to be used to predict the winner on election process. This method tries to dig the people sentiment on their governor candidates during election, whether it’s positive, negative, or neutral opinion. The output of the positive sentiment is related to people acceptance towards one of the election nominee. That statement usually applied as a base reference for determining the result of the election process. In sentiment analysis, the importance of its fuzzy logics must be considered. Each of the people statement is assumed to have the level of positive, negative, or neutral percentage. The concept of fuzzy logic is developed and applied on one of this text mining method. This research is focusing on comparison analysis and fuzzy logic application in sentiment analysis method. Two method which discussed in this research are Fuzzy Naïve Bayes and Sentiment Fuzzy with convolutional neural network. This research is applied on PILKADA of Solo and Medan district case study. The data of the people opinion are acquired from twitter and collected on September 2020 to December 2020. The two methods which mentioned before are implemented on the acquired data and the output of these method application then compared. The conclusion of this research suggest that different approach will resulting in different output.