Reviewerly/exHarmony huggingface dataset:
The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders

Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency while a question still remains whether or not it would perform as well on tasks that are distinct in nature. We first present MTL results on five NLP tasks, POS, NER, DEP, CON, and SRL, and depict its deficiency over single-task learning. We then conduct an extensive pruning analysis to show that a certain set of attention heads get claimed by most tasks during MTL, who interfere with one another to fine-tune those heads for their own objectives. Based on this finding, we propose the Stem Cell Hypothesis to reveal the existence of attention heads naturally talented for many tasks that cannot be jointly trained to create adequate embeddings for all of those tasks. Finally, we design novel parameter-free probes to justify our hypothesis and demonstrate how attention heads are transformed across the five tasks during MTL through label analysis.
IMPLEMENTASI KEBIJAKAN PENDIDIKAN DASAR (WAJIB BELAJAR 9 TAHUN) ANAK SUKU LAUT DI DESA BERAKIT KAMPUNG PANGLON KECAMATAN TELUK SEBONG

The problem of education is a very important problem in life, not only very important, but even the problem of education cannot be separated from life at all. Both in family life and in the life of the nation and State. The progression of a nation is largely determined by the advancement of education in the family and the State. Because education is a tool to educate the nation's life. In Bintan district, the marine tribe who lives in Panglong village, Berakit village, Teluk Sebong sub-district, still low for education problems. This study aims to determine how the implementation of the 9-year compulsory education policy in the village of Kampung Panglong, Teluk Sebong sub-district. The research method used is descriptive research method with a qualitative approach. The data collection technique was carried out by means of interviews, observation and documentation related to the 9 year compulsory education. The data obtained are then analyzed qualitatively by examining all the data that has been collected which is supported by the results of interviews with a theoretical approach to the theory of David C. Corten's (1988) theory of the success of the implementation program, namely the program, utilization, and organization. Based on the research results, it can be concluded that the implementation of the 9-year compulsory education policy in Bintan District, Telok District, Sebong District, Riau Islands Province has not been implemented properly.
SISTEM PAKAR PENYELEKSIAN KULIT ULAR UNTUK EKSPOR MENGGUNAKAN METODE FORWARD CHAINING PADA CV. ALONA JAYA

Snake skin is one type of reptile skin that has now been used as a commodity trade with a fairly&#x0D; high price. Snake skin that is ripe for export certainly experienced some process first, one of the&#x0D; process is the selection to know the skin into the category class I, II or III. CV. Alona Jaya is a&#x0D; company engaged in the export of reptile skin. Human resources owned CV. Alona Jaya not all&#x0D; experts in the field of skin selection, so still need the help of experts in handling the process.&#x0D; Expert system applications capable of imitating the work of an expert in various fields, one of&#x0D; them is snake skin selection. This expert system is built using forward chaining method, as a&#x0D; search technique that starts from a known fact to reach a goal. The design of this application&#x0D; using UML and Visual Basic programming language with Microsoft Access database as its&#x0D; application. This research produces an expert system application that can be used in snake skin&#x0D; selection for export, so it can help human resources who are not experts at the time of skin selection
Noise Reduction of Images using BFO Cascaded with Wiener Filter and Framelet Transform

Image noise removal is necessary for image processing systems. In this paper, a hybrid denoising that combines Bacterial Foraging Optimization (BFO) cascaded with spatial domain Wiener filter and thresholding function in the Framelet domain is done. Three algorithms are proposed in this study. The first one stands for hybrid denoising algorithm that employs Wiener filter with 2-level Discrete Wavelet Transform (DWT). The second algorithm uses Wiener filter with 2-level Framelet Transform (FLT). The last one is hybrid denoising algorithm that combines Wiener filter with 1-level WT, and then applies FLT on LL of WT. After that, the BFO algorithm is applied to minify the error quantities between thea noisy image and the produced image. The adopted procedure has been tested on gray and color images compared with MAX, MINMAX algorithms, and FUZZY filters in the wavelet domain. Simulation results based on MATLAB simulator for the first proposed algorithm with DWT (db5 type) is superior to the second, third, and the conventional denoising approaches for most test noisy images with Gaussian, Salt and Pepper noises. The third proposed algorithm with hybrid Wavelet & FLT is superior to others for noisy images with speckle noise; this algorithm has given good results with medical images.
Robustness via Uncertainty-aware Cycle Consistency

Unpaired image-to-image translation refers to learning inter-image-domainmapping without corresponding image pairs. Existing methods learn deterministicmappings without explicitly modelling the robustness to outliers or predictiveuncertainty, leading to performance degradation when encountering unseenperturbations at test time. To address this, we propose a novel probabilisticmethod based on Uncertainty-aware Generalized Adaptive Cycle Consistency(UGAC), which models the per-pixel residual by generalized Gaussiandistribution, capable of modelling heavy-tailed distributions. We compare ourmodel with a wide variety of state-of-the-art methods on various challengingtasks including unpaired image translation of natural images, using standarddatasets, spanning autonomous driving, maps, facades, and also in medicalimaging domain consisting of MRI. Experimental results demonstrate that ourmethod exhibits stronger robustness towards unseen perturbations in test data.Code is released here:https://github.com/ExplainableML/UncertaintyAwareCycleConsistency.
Wildebeest optimization algorithm based on swarm intelligence method in solving optimization problems

Metaheuristic algorithms are effective ways to solve optimization problems and use existing phenomena in nature to solve these problems. Due to the independence of metaheuristic algorithms from the gradient information, the objective function can be used to solve large-scale problems by optimization solutions. The organisms’ behavior in nature in their interaction with each other is one of the optimization methods that are modeled as swarm-based algorithms. Swarm-based algorithms are a set of metaheuristic algorithms which are modeled based on group behavior of their organisms and social interactions. The behavior of wildebeests in nature is considered as a swarm-based algorithm for survival because it can be seen that these organisms migrate in groups and try to survive for themselves and their own herd. In this paper, a new metaheuristic algorithm (WOA) based on migratory and displacement behavior of wildebeests is presented of solving optimization problems. In this algorithm, problem solutions are defined as wildebeest herds that search the problem space for appropriate habitat. The results of the implementation of a set of benchmark functions for solving optimization problems such as the Wildebeest Optimization Algorithm, Whale Optimization Algorithm, BAT, Firefly and Particle Swarm Optimization (PSO) algorithms show that the proposed algorithm is less error rate to find global optimum and also caught up rate in the local optimum is less than the methods.
STRATEGI PENGELOLAAN KELAS BAHASA INGGRIS SELAMA PANDEMI COVID-19

Classroom management in the teaching and learning process is very important, especially at this time. To ensure that learning goes well, the teacher should know how to manage the classroom properly and correctly. This journal discusses about how to manage classes, the objectives of class managements, the principles of classroom management, the classroom management strategies during covid 19 and strategies for managing English classes. The purposes of classroom management are to build student’ enthusiasm and creativity, build conducive situations and condition, provide and manage facilities, foster and guide students. The principle of classroom management is influenced by internal and external factors of students, emotions, thoughts, and behavior. Moreover, the flexibility, warmth, interest, creativity and discipline of teachers.Classroom management strategies during the covid 19 pandemic include habituation from offline learning to online classes, short, concise and clear using Pdf or video tutorials, class discussions, groups and collaborationbetween teachers and parents. English classroom management strategy is to use the edutainment learning method and the application of quizziz, kahoot,dou lingo and word puzzle.Keyword: classroom management, online learning, english learning
Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra

Blood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%.
HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text

Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and metrics have been proposed over the years. Unfortunately, text generation and evaluation are relatively understudied due to the scarcity of high-quality resources in code-mixed languages where the words and phrases from multiple languages are mixed in a single utterance of text and speech. To address this challenge, we present a corpus (HinGE) for a widely popular code-mixed language Hinglish (code-mixing of Hindi and English languages). HinGE has Hinglish sentences generated by humans as well as two rule-based algorithms corresponding to the parallel Hindi-English sentences. In addition, we demonstrate the inefficacy of widely-used evaluation metrics on the code-mixed data. The HinGE dataset will facilitate the progress of natural language generation research in code-mixed languages.
An Optimization Based Feature Extraction And Machine Learning Techniques For Named Entity Recognition In Biomedical Field

Unstructured and structured documents processing are involved in Named Entity Recognition (NER) for recognizing definite entity classes and categorization of these entities into some predefined classes. Biomedical instances like RNAs, DNAs, disorders, viruses, proteins, genes and chemical components are identified using Biomedical Named Entity Recognition (BNER). Techniques used to extract those entities plays a major role in this BNER.Supervised Machine Learning (SML) approaches are used in various BNER techniques. In these approaches, in order to enhance the recognition process’s effectiveness, these features are used. A set of distinguishing and discriminating characteristics are used for identifying features, which is having an ability for indicating entity occurrence.Biocurators annotates only limited number of articles also consumes more processing time.In this work, propose an Enhanced System for Curatable-Biomedical Named Entities Recognition (ECBNER) and feature extraction approaches for bio-medical named entity recognition using aImproved Particle Swarm Optimization (IPSO). In machine learning, fusion of classifiers gives a fruitful research direction and it is an effective technique of it. A standalone classifier’s performance in classification can be enhanced using this. Result of various classifiers combination are aggregated to overcome individual classifier’s possible local weakness for producing highly robust recognition.Gene/Disease NER are processed under Conditional Random Field (CRF) and all action terms are collected and processed in concurrent manner to extract the accurate biomedical named entities.Finally this general framework to learn the representation by combining general and domain-specific features is proposed and evaluated, showing empirical results compare to the existing frameworks.
cushLEPOR uses LABSE distilled knowledge to improve correlation with human translations

Automatic MT evaluation metrics are indispensable for MT research. Augmented metrics such as hLEPOR include broader evaluation factors (recall and position difference penalty) in addition to the factors used in BLEU (sentence length, precision), and demonstrated higher accuracy. However, the obstacles preventing the wide use of hLEPOR were the lack of easy portable Python package and empirical weighting parameters that were tuned by manual work. This project addresses the above issues by offering a Python implementation of hLEPOR and automatic tuning of the parameters. We use existing translation memories (TM) as reference set and distillation modeling with LaBSE (Language-Agnostic BERT Sentence Embedding) to calibrate parameters for custom hLEPOR (cushLEPOR). cushLEPOR maximizes the correlation between hLEPOR and the distilling model similarity score towards reference. It can be used quickly and precisely to evaluate MT output from different engines, without need of manual weight tuning for optimization. In this session you will learn how to tune hLEPOR to obtain automatic custom-tuned cushLEPOR metric far more precise than BLEU. The method does not require costly human evaluations, existing TM is taken as a reference translation set, and cushLEPOR is created to select the best MT engine for the reference data-set.
New Paths Generation Using Crossover And Mutation Genetic Operations For Mobile Robot To Find The Optimal Path In 2-D Static Environment

The development of new pathways is the essential technique of robot navigation decision-making and guidance, as well as hotspots for study in the domain of artificial intelligence. In this research, an enhanced multi-objective genetic algorithm (IMGA) has been suggested to handle 2D stable environment difficulties such as sluggish reaction speed, dangerous factors, a lot of turns within the traditional path planning approach, and the simplest planning path. To make sure the efficiency of such a planned route, the algorithm employs the heuristic median implantation technique to demonstrate the actual population that also enhances the viability of an actual path and creates multiple objective fitness functions depending upon three factors: path protection, path energy usage, and path length. Furthermore, by employing a layered approach, as a single-point crossover technique, as well as an eight-neighborhood-domain single-point modification technique, the selection, crossovers, and modification operators were created. Eventually, the deletion action is included to assure the mobile robot's effective service. Simulation studies in a 2D stable environment allow for a modest converging rate and an easier fall into such a regional optimal through the simplest route to the destination location.
Mammographic Breast Densities among 200 Nigerian Females in Sokoto, North Western Nigeria

Background: Breast density has been reported to be an independent predictor of breast cancer risk. Women with highest mammographic breast density may have four to six fold increased risk of developing cancer. Aim and Objective: This study is aimed at evaluating the various forms of breast densities following screening and diagnostic mammographic examination. Materials and Methods: From December 2010 and November 2012, 200 females had mammographic breast examination. Seventy-seven (77) females had routine screening mammography while 123 had diagnostic mammographic examination in the department with the General Electric (GE) Alpha-RT machine with model number MGF-101(manufactured 2010). All the subjects had to fill a mammographic form consisting of variables like age, sex, occupation, family history of breast cancer, tribe, contraception, parity, and caffeine consumption, history of surgical intervention (lumpectomy, biopsy and/or mastectomy), previous mammography and last child birth. MLO and CC views were done for the breast examination though additional views were occasionally employed. Results: Breast density decreases with increase in age of the female subjects and breast lesions are better detected following mammography on fatty or less dense breast.
Remembering “Andy” Aines: A look back at his call for a national STI policy

A commemoration is presented of the passing 25 years ago of Andrew A. Aines, a pioneer and staunch advocate for a rational national information policy and approach to the dissemination of scientific and technical information. Included in this recognition of his efforts is information on an unfinished manuscript of major proportions covering the post-World War II efforts to create such a national policy. This manuscript includes a detailed history of the establishment and evolution of the Committee on Scientific and Technical Information (COSATI) and is available on the Internet Archive.
Improving Text Auto-Completion with Next Phrase Prediction

Language models such as GPT-2 have performed well on constructing syntactically sound sentences for text auto-completion tasks. However, such models often require considerable training effort to adapt to specific writing domains (e.g., medical). In this paper, we propose an intermediate training strategy to enhance pre-trained language models’ performance in the text auto-completion task and fastly adapt them to specific domains. Our strategy includes a novel self-supervised training objective called Next Phrase Prediction (NPP), which encourages a language model to complete the partial query with enriched phrases and eventually improve the model’s text auto-completion performance. Preliminary experiments have shown that our approach is able to outperform the baselines in auto-completion for email and academic-writing domains.
A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm

Abstract As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.
Application of Machine Learning to Improve Consumer Devices and Services

Machine learning is a subtype of artificial intelligence in which computers use algorithms to learn from data and discover patterns, a capacity that businesses may exploit in various ways to improve customer service. Machine learning (ML) is improving nearly every function and process automation by enabling operational optimisation. As a result, it improves customer service, speeds up work, lessens errors and improves accuracy. Customer service is one area of the company that might benefit from machine learning. Natural language processing and sentiment analysis are two technologies that can help businesses better understand how to respond to consumer comments and queries. The current study is being carried out to have a better knowledge of the ML. The researcher also examines the application of ML to improve consumer services and devices. The researcher uses secondary data to analyse every element of ML in this study. In order to get reliable findings, this article employed the descriptive research approach. Secondary data from research articles and peer-reviewed papers, and a literature review were employed in this study.
Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages

We explore the impact of leveraging the relatedness of languages that belong to the same family in NLP models using multilingual fine-tuning. We hypothesize and validate that multilingual fine-tuning of pre-trained language models can yield better performance on downstream NLP applications, compared to models fine-tuned on individual languages. A first of its kind detailed study is presented to track performance change as languages are added to a base language in a graded and greedy (in the sense of best boost of performance) manner; which reveals that careful selection of subset of related languages can significantly improve performance than utilizing all related languages. The Indo-Aryan (IA) language family is chosen for the study, the exact languages being Bengali, Gujarati, Hindi, Marathi, Oriya, Punjabi and Urdu. The script barrier is crossed by simple rule-based transliteration of the text of all languages to Devanagari. Experiments are performed on mBERT, IndicBERT, MuRIL and two RoBERTa-based LMs, the last two being pre-trained by us. Low resource languages, such as Oriya and Punjabi, are found to be the largest beneficiaries of multilingual fine-tuning. Textual Entailment, Entity Classification, Section Title Prediction, tasks of IndicGLUE and POS tagging form our test bed. Compared to monolingual fine tuning we get relative performance improvement of up to 150% in the downstream tasks. The surprise take-away is that for any language there is a particular combination of other languages which yields the best performance, and any additional language is in fact detrimental.
Skill Level Monitoring Applied to AR Assisted Maintenance

This paper present a work in progress experiment about AR assisted inspection for maintenance. The purpose of this experiment is to define a set of indicators that could help to evaluate the skill level of a user during the inspection task while using AR assistance system.