Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Semantic Segmentation With Pixel-Level Contrastive Learning From a Class-Wise Memory Bank

This work presents a novel approach for semi-supervised semantic segmentation. The key element of this approach is our contrastive learning module that enforces the segmentation network to yield similar pixel-level feature representations for same-class samples across the whole dataset. To achieve this, we maintain a memory bank continuously updated with relevant and high-quality feature vectors from labeled data. In an end-to-end training, the features from both labeled and unlabeled data are optimized to be similar to same-class samples from the memory bank. Our approach outperforms the current state-of-the-art for semi-supervised semantic segmentation and semi-supervised domain adaptation on well-known public benchmarks, with larger improvements on the most challenging scenarios, i.e., less available labeled data. this https URL
Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

Machine reading comprehension (MRC) is a sub-field in natural language processing that aims to assist computers understand unstructured texts and then answer questions related to them. In practice, the conversation is an essential way to communicate and transfer information. To help machines understand conversation texts, we present UIT-ViCoQA, a new corpus for conversational machine reading comprehension in the Vietnamese language. This corpus consists of 10,000 questions with answers over 2,000 conversations about health news articles. Then, we evaluate several baseline approaches for conversational machine comprehension on the UIT-ViCoQA corpus. The best model obtains an F1 score of 45.27%, which is 30.91 points behind human performance (76.18%), indicating that there is ample room for improvement. Our dataset is available at our website: http://nlp.uit.edu.vn/datasets/ for research purposes.
Model Dialogues: 3D models as active resources – an experiment in data enrichment of 3D modelled lithic artefacts

This thesis seeks to explore the potential for 3D archaeological datasets to become the ‘living resources’ advocated by Morgan and Wright (2018). This process is examined through lenses of object agency, object biography, Chaine operatoire, formation processes and posthumanist theory with an aim to assess the capacity of digital approaches for enabling the self-reflexive ‘slow archaeology’ idealised by Caraher (2016). Finally, an analysis of the tensions between digital and traditional illustrative means was explored alongside considerations of data standardisation and metadata frameworks. The explicit questions this thesis seeks to answer are, what datasets can be used to enrich a 3D mesh? how does this palimpsest approach impact data analysis and interaction? can we identify the best practices for this approach? and can we identify broader considerations for future research? The sample material used to explore these issues was a Mesolithic Core Axe currently held within the Lund University Teaching Collection within the Department of Archaeology and Ancient History. This was digitised using photogrammetric methods with the diverse theoretical issues mentioned previously used to identify information that could be utilised to ‘enrich’ the digital artefact in the form of multiple visual models. A hand-drawing was carried out to traditional conventions and projected onto the mesh as a new texture, and the illustrative tools and features of Agisoft Metashape, Meshlab and Blender were compared. The latter part of this research process involves an attempt to synthesise the various concerns above into a coherent and functional 3DHOP output before analysis and reflection of the chosen approaches is carried out. Keywords: Digitisation, Visualisation, Illustration, Slow Archaeology, Photogrammetry, Digital Archaeology, Agisoft Metashape, Meshlab, Blender, 3DHOP, Metadata, Formation Processes, Use-Wear Analysis, Chaine operatoire (Less)
Pengembangan Media Pembelajaran Buku Saku untuk Meningkatkan Hasil Belajar Siswa pada Materi Sumber Daya Alam dan Kemaritiman Indonesia di SMP Negeri 1 Japah Blora

Dwi Sutanti / A610140054, POCKET BOOK INSTRUCTIONAL MEDIADEVELOPMENT TO IMPROVE STUDENT LEARNING RESULTS ON THEPOTENTIAL OF NATURAL RESOURCES AND INDONESIA’S MARITIMETOPIC AT SMP N 1 JAPAH BLORA.Thesis. Faculty of Teacher Training andEducation, Universitas Muhammadiyah Surakarta. December, 2019.This research aimed to 1) Developing pocket book instructional media on thepotential of natural resources and Indonesia’s maritime topic. 2) Knowing studentlearning result after using pocket book media on the potential of natural resourcesand Indonesia’s maritime topic at SMP N 1 Japah Blora. This research wasaresearch and development (R&D). Product developed in this research was pocketbook media. The development model used in this study was the Tiagarajandevelopment model. This research was conducted at SMP Negeri 1 Japah Blora. Thesample of this research were 30 students of class VII C as a control class and 30students of class VII B as an experimental class. The data analysis technique usedthe t test analysis. The results showed that : 1) Based on the assessment of materialexperts and media experts, pocket book learning media has an average value frommaterial experts 4,4 and media experts 4 which means in the good category can beused as a learning media on the potential of natural resources and Indonesia’smaritime topic. 2) The result of pretest and posttest in control class was the pretestvalue 51,52 and posttest value 63,45 showed an increase in learning results. Theresult in experimental class was the pretest value 47,67 and posttest value 69,67showed an increase in learning results after using pocket book learning media. Bothof control class and experimental class has increased learning results, but theexperimental class had increased more significanly. The difference between theincrease in learning results of the control class and the experimental class was 6,22higher than control class.
Hybrid Model for Concurrent Execution of Lexical Analyzer on Multi-Core Systems using Dynamic Task Allocation Algorithm and Auto Keyword Detection Method

The processing power of machines will continue to accelerate massively. Modern eras of computing are driven by elevated parallel processing by the revolution of multi-core processors. This continuing trend toward parallel architectural paradigms facilitates parallel processing on a single machine and necessitates parallel programming in order to utilize the machine's enormous processing power. As a consequence, scanner generator applications will eventually need to be parallelized in order to fully leverage the throughput benefits of multi-core processors.This article discusses the way of processing the tasks in parallel during the scanning stage of lexical analysis. This is done by recognizing tokens in different lines of the source program in parallel along with auto detection of keyword in a character stream. Tasks are allocated line-by-line to the multiple instance of the lexical analyzer program. Then, each of the instances is run in parallel to detect tokens on different cores that are not yet engaged. Theoretical and practical results indicate that the suggested methodology outperforms the sequential strategy in terms of tokenization consistently. It significantly decreases the amount of time spent on lexical analysis during the compilation process.
Pre-evaluation Strategy on Algorithms for Mining Top – k High Utility Item Sets

A rising trend in data mining is a High utility item sets (HUIs) mining.It aims to find all item sets which have an utility which meets a client determined least utility edge min_util.But , for clients, it is an issue to set a min_util efficiently.So, it is not proper procedure for clients to find a least utility edge by experimentation.An excessive number of HUIs will be produced, in the case that min_util is set very low.Due to this the mining procedure may result wasteful.It is also possible that no HUIs be found, if min_util is set very high.So for addressing the above issues, we redefine the problem of high utility item sets (HUIs) mining by top-k high utility item sets ( top-k HUI ) mining.Here, desired number of HUIs to be mined is k.Two different algorithms which are named as TKU and TKO (mining Top-K Utility item sets in two stages , mining Top-K utility item sets in one stage, respectively) are proposed for mining the item sets without setting the value of min_util.We apply pre-evaluation strategy to algorithms to improve the performance.
Software Evolution Understanding: Automatic Extraction of Software Identifiers Map for Object-Oriented Software Systems

Software companies usually develop a set of product variants within the same family that share certain functions and differ in others. Variations across software variants occur to meet different customer requirements. Thus, software product variants evolve overtime to cope with new requirements. A software engineer who deals with this family may find it difficult to understand the evolution scenarios that have taken place over time. In addition, software identifier names are important resources to understand the evolution scenarios in this family. This paper introduces an automatic approach called Juana's approach to detect the evolution scenario across two product variants at the source code level and identifies the common and unique software identifier names across software variants source code. Juana's approach refers to common and unique identifier names as a software identifiers map and computes it by comparing software variants to each other. Juana considers all software identifier names such as package, class, attribute, and method. The novelty of this approach is that it exploits common and unique identifier names across the source code of software variants, to understand the evolution scenarios across software family in an efficient way. For validity, Juana was applied on ArgoUML and Mobile Media software variants. The results of this evaluation validate the relevance and the performance of the approach as all evolution scenarios were correctly detected via a software identifiers map.
TATA LAKSANA RADIOTERAPI KANKER OTAK DENGAN TEKNIK 3D-CRT PESAWAT LINEAR ACCELERATOR DI SUB DEPARTEMEN RADIOTERAPI RUMKITAL Dr. RAMELAN SURABAYA

Brain cancer is an expansive lesion forming a mass in an intra cranial space. One of the treatment of brain cancer is by radiotherapy, that is measurable radiation dose on the target volume to kill cancer cells as much as possible by minimizing the effects of radiation on healthy tissue around the tumor. The purpose of this study is to know the radiotherapy of brain cancer procedure and the reasons for using manual verification.The type of this research is qualitative research with case study approach. Data retrieval was conducted from September to November 2017 at Sub.Departement of Radiotherapy of Navy Hospital dr. Ramelan Surabaya with participant observation method, documentation and structured interview to radiation oncologist physician, medical physicist, radiotherapy radiograph. Data obtained analyzed by intractive model, making transcript of interview, then reduced and processed in open coding form, presented in the form of quotation and then can be taken conclusion.The results showed that the treatment of Brain Cancer radiation using 3D CRT Technique in Sub Departement of Radiotherapy of Navy Hospital dr. Ramelan Surabaya includes, Consultation doctor, determination, CT-Simulator, planning treatment planning system, verification and treatment measures with LINAC tool. Manual verification application with ct simulator done because it does not have a verification tool that EPID on LINAC device.Keywords:3D CRT Technique, Brain Cancer, LINAC device, manual verification with CT-Simulator
ALGORITHM FOR MERGING AND INTERPOLATING CLUSTERS IN OVERLAPPING IMAGES

An image overlapping algorithm, taking into account certain properties of objects identified in the images (average intensity, movement speed, etc) is proposed. The algorithm minimizes both memory and time complexity and it can be used in various applications, especially in medical imaging analysis. The idea behind the proposed algorithm is surface merging and interpolation.
Cloud Native Architecture Principles

Cloud-native principles define the underlying general rules and guidelines for the use and architecture of your system. They reflect a level of consensus for the various elements of your system and enterprise and offer a basis for making future decisions. The principles are typically created at the same time as the architecture is defined.
Complaint & Compliment System (CCS UTP)

Nowadays, students tend to post their emotions on social media such as Twitter, Instagram and Facebook. They do not see the after effect of their post towards other people or organization. Thus, this create the idea of having a complaint and compliment system like social media based for Universiti Teknologi PETRONAS (UTP) students. The system will fully utilize the Microsoft tools that the UTP students can access using their UTP email account. The system is a free for the use of students to make complaint in it rather than making complaint in other media social based that will make worldwide users know the negativity of the university system and students. This system also can make students post the compliment for the staffs to show their gratitude towards the hard work of UTP staffs or lecturers. The tools used are PowerApps and Power BI which can integrate a beautiful and user-friendly system for the UTP community to use as their platform to communicate within UTP community. Furthermore, the database for this system will be keep in Microsoft SharePoint which is also one of Microsoft product that is free for UTP community.
ITC's strategic plan for Open Science 2021-2025: towards an open future

Open Science (OS) is an umbrella term comprising principles to increase the transparency of research. Besides Open Access to scientific articles, these principles contain public availability of reusable methods (e.g., code and tools), data, and educational materials. This document outlines a plan to achieve the transition towards OS. ITC’s Strategic Plan for OS 2021-2025 - Towards an Open Future contains five initiatives: 1.OS at ITC aims to provide guidelines and OS capacity development to address the obstacles ITC researchers encounter when doing OS. 2.The ITC Knowledge Hub will provide services and tools to access, create, and publish open research, including scientific results based on qualitative/quantitative analyses using computational workflows. 3.Open Educational Resources will be addressed by exploring options to realise Open Educational Resources at ITC and providing lecturers with guidelines and support to create them. 4.The OS Community Twente serves as an inter-disciplinary, bottom-up community to promote, learn, share, and discuss OS practices. 5.Research & Funding aims to address challenges in OS through innovative developments and user studies. A further output is to generate funding to realise the ambitious aims presented in the plan. For a successful OS transition, the initiatives aim to address the Rewards & Recognition system, valorise Sharing & Collaboration, develop OS Knowledge & Skills, and foster Cultural change & Societal impact.
Empirical Analysis of Pharmaceutical Companies

The paper focuses on empirical/technical analysis of 17 global level competing pharmaceutical companies. The main classification used to categorize the companies was the type of drugs they make i.e., branded, generic and oncogenic. Within this broad classification, the companies have been chosen on the basis of their leading revenue numbers. The paper takes the methods of Stochastic, Simple Average and Beta Calculation into consideration to draw conclusions which will help determine which stock is moving high in the market, in the concerned industry, based on both macroeconomic and microeconomic factors. These methods can also be used to calculate investment prospects for other sectors.
From Statistical to Deep Learning Models: A Comparative Sentiment Analysis Over Commodity News

The sentiment analysis of news and social media posts is a growing research area with advancements in natural language processing and deep learning techniques. Although various studies addressing the extraction of the sentiment score from news and other resources for specified stocks or a stock index, still there is a lack of an analysis of the sentiment in more specialized topics such as commodity news. In this paper, several natural language processing techniques with a varying range from statistical methods to deep learning-based methods were applied on the commodity news. Firstly, the dictionary-based methods were investigated with the most common dictionaries in financial sentiment analysis such as Loughran & McDonald and Harvard dictionaries. Then, statistical models have been applied to the commodity news with count vectorizer and TF-IDF. The compression-based NCD has been also included to test on the labeled data. To improve the results of the sentiment extraction, the news data was processed by deep learning-based state-of-art models such as ULMFit, Flair, Word2Vec, XLNet, and BERT. A comprehensive analysis of all tested models was held. The final analysis indicated the performance difference between the deep learning-based and statistical models for the sentiment analysis task on the commodity news. BERT has achieved superior performance among the deep learning models for the given data.
Enhancing Problem-Solving Skills with Smart Personal Assistant Technology

Smart Personal Assistants (SPAs; such as Amazon’s Alexa or Google’s Assistant) let users interact with computers in a more natural and sophisticated way that was not possible before. Although there exists an increasing amount of research of SPA technology in education, empirical evidence of its ability to offer dynamic scaffolding to enhance students problem-solving skills is still scarce. To fill this gap, the aim of this paper is to find out whether interactions with scaffolding-based SPA technology enable students to internalize and apply problem-solving steps on their own in a 10th grade high school and a vocational business school class. Students in the experiment classes completed their assignments using Smart Personal Assistants, whereas students in the control classes completed the same assignments using traditional methods. This study used a mixed-method approach consisting of two field quasi-experiments and one post-experiment focus group discussion. The empirical results revealed that students in the experiment classes acquired significantly more problem-solving skills than those in the control classes (Study 1: p = 0.0396, study 2: p < 0.001), and also uncovered several changes in students’ learning processes. The findings provide first empirical evidence for the value of using SPA technology on skill development in general, and on problem-solving skill development in particular.
Using Contrastive Learning and Pseudolabels to Learn Representations for Retail Product Image Classification

Retail product Image classification problems are often few shot classification problems, given retail product classes cannot have the type of variations across images like a cat or dog or tree could have. Previous works have shown different methods to finetune Convolutional Neural Networks to achieve better classification accuracy on such datasets. In this work, we try to address the problem statement : Can we pretrain a Convolutional Neural Network backbone which yields good enough representations for retail product images, so that training a simple logistic regression on these representations gives us good classifiers ? We use contrastive learning and pseudolabel based noisy student training to learn representations that get accuracy in order of finetuning the entire Convnet backbone for retail product image classification.
Fraud Detection and Challenges in Digital Transactions using Bayesian Optimized Random Forest Classifier

With technological and economic advances, which facilitated the communication process and increased purchasing power, credit card transactions have become the main payment method in national and international retail. In this regard, the increase in the number of credit card transactions is crucial for generating more opportunities for fraudsters to produce new forms of fraud, which results in great losses for the financial system. This fact arouses the interest of fraudsters. The card market sees fraud as operating costs, which are passed on to consumers and society in general. Still, the high volume of transactions and the need to fight fraud open space for the application of artificial intelligence and machine learning techniques. This research work highlights the importance and challenges faced while using auto monitoring to detect credit card fraud to prevent different risks to our assets. Automated learning techniques have proven to be the solution to supervised learning. This work identifies techniques such as random forests and Bayesian optimized random forest classifiers as the best techniques according to related works. Binary particle swarm optimization algorithm is used for selection of features from the open dataset provided by Kaggle. Sensitivity, precision, f-score and accuracy are used as a performance evaluation tool to find the best parameterization of both the techniques.
Writing to Increase Complexity: Spanish L2 Learners' Complexity Gains in a College Composition Class

This study examines the linguistic complexity of Spanish as a second language (L2) in learners' essays across proficiency levels at two timelines of a composition class during a college semester. Data comes from 22 L2 learners of Spanish enrolled in two sections of a third-year composition class at the college level, who were assigned nine compositions (150–250 words each) at different times throughout one semester. Data was prepared using a natural language processing (NLP) pipeline, including UDPipe, an NLP tool that allows annotation, part-of-speech (POS) tagging, lemmatization, and dependency parsing based on Universal Dependencies (UD) treebanks. In addition, the NLTK package and several Python scripts were used on the annotated model to process and extract syntactic and lexical information from the datasets. Results showed that selected predictors of syntactic complexity increased at different stages in the semester and the effect seems more robust in beginner learners. Moreover, the use of lower frequency lexicon appears to be integrated thorough out the semester in both groups of learners. These findings indicate the pedagogical benefits of Spanish composition courses and the specific indices of L2 writing development obtained during a semester of classes across two groups of language proficiency.