Reviewerly/exHarmony huggingface dataset:
FNAC In Diagnosis Of Breast Lesion

INTRODUCTION: Breast carcinoma is the leading most common malignant tumor and leading cause of carcinoma death with more than 1000000 cases occurring world wide annually 1. FNAC (Fine Needle Aspiration Cytology) has become widely accepted as a reliable diagnostic tool for diagnosis of breast masses3.The aim of the study was to classify Breast lesions & correlate with histopathology Report.MATERIALS & METHOD: This was a retrospective observational study done over a period of one year (Jan. 17- Dec. 17) in Cytology Division of Department of Pathology of AMC MET Medical College at L.G.Hospital Campus ,a tertiary Health Care located in Maninagar Ahmedabad .Of total 1104 FNAC were done in the department among these 215 were Breast Lesions. Breast lesions were categorized into Inflammatory, Benign with No Risk, Benign with moderate Risk, Suspicious & Malignant.RESULTS: The Maximum number of cases was in the age group of 18-30 in Benign Breast Lesion. Malignant Breast Lesion was found in the age group of above 41.CONCLUSION: FNAC is minimally invasive,produces speedy results and is inexpensive.
Video Action Understanding.

Many believe that the successes of deep learning on image understanding problems can be replicated in the realm of video understanding. However, due to the scale and temporal nature of video, the span of video understanding problems and the set of proposed deep learning solutions is arguably wider and more diverse than those of their 2D image siblings. Finding, identifying, and predicting actions are a few of the most salient tasks in this emerging and rapidly evolving field. With a pedagogical emphasis, this tutorial introduces and systematizes fundamental topics, basic concepts, and notable examples in supervised video action understanding. Specifically, we clarify a taxonomy of action problems, catalog and highlight video datasets, describe common video data preparation methods, present the building blocks of state-of-the art deep learning model architectures, and formalize domain-specific metrics to baseline proposed solutions. This tutorial is intended to be accessible to a general computer science audience and assumes a conceptual understanding of supervised learning.
Every picture in its place - A spatial analysis of rock art in Tjust using GIS

Rock art is the closest we have to a written language from the Bronze age. The different motifs make us wonder what stories they tell us, what they are representing and why they were even made in the first place. The focus of this thesis is on the placement of rock art motifs on the panel and by using digital techniques like photogrammetry and GIS, the spatial relation between different motifs is examined. Do certain motifs have a predetermined place on the panel, and how do the panel, cosmological beliefs, and people, who interacted with rock art then, affected the placement of motifs? To study this, ten rock art panels were chosen from Gamleby parish in Tjust in eastern Smaland. Tjust is well-known for being a landscape filled with remnants from the Bronze age, and it constitutes the rock art richest area in Smaland. Spatiality and digital technology were the theories and practices used for studying the placement of rock art motifs. By treating the panel as a topographical landscape and look at density, distance, slope and topography, it’s possible to study rock art in relation to the rock panel. What this study shows is that motifs are concentrated in certain parts of the panels, with small differentiations between individual motifs, and that the panels’ texture, including cracks and striations, could possibly have been affecting the way motifs were placed on the panels. (Less)
A PACTful Agenda for Cloud Programming Research

We have witnessed two decades of cloud computing research. Yet, programming the cloud remains a tedious task for both the application and cloud infrastructure developers: application developers need to consider various cloud deployment aspects as they write code, while infrastructure developers must determine how to execute and optimize code that mingles application semantics, fault tolerance, and hardware constraints, etc. all into a single program.
A Qualitative Study of Influences Affecting EFL Learners’ Self-Efficacy Beliefs In Preparatory Year

With the growing importance of English proficiency in educational settings in non-English speaking countries, a great deal of research emphasizes the need to consider learners' beliefs as a crucial element to further understand learners' language proficiency.At the heart of these beliefs come learners' self-efficacy beliefs, as previous research has denoted a strong relationship between these beliefs and learners' performance.This study adopts a qualitative mode of inquiry and was designed to further understand EFL learners' self-efficacy beliefs and performance by looking at influences affecting these beliefs in an EFL context i.e., Saudi Arabia.This study comprises an in-depth analysis of interviews with 16 participants studying English as a foreign language in preparatory year in a Saudi university.The participants were selected deploying maximum variation and included both successful and less successful learners belonging to different performance levels.Data analysis revealed that besides the sources of self-efficacy, students' interpretations of their English attainments, others' expectations, academic and emotional support available to students were found to be related to their EFL self-efficacy beliefs.This study and its utilization of qualitative methods illuminate our understanding of EFL learners' self-efficacy beliefs as it sheds light on contextual and cultural influences on learners' self-efficacy beliefs.
Post-hoc Models for Performance Estimation of Machine Learning Inference

Estimating how well a machine learning model performs during inference is critical in a variety of scenarios (for example, to quantify uncertainty, or to choose from a library of available models). However, the standard accuracy estimate of softmax confidence is not versatile and cannot reliably predict different performance metrics (e.g., F1-score, recall) or the performance in different application scenarios or input domains. In this work, we systematically generalize performance estimation to a diverse set of metrics and scenarios and discuss generalized notions of uncertainty calibration. We propose the use of post-hoc models to accomplish this goal and investigate design parameters, including the model type, feature engineering, and performance metric, to achieve the best estimation quality. Emphasis is given to object detection problems and, unlike prior work, our approach enables the estimation of per-image metrics such as recall and F1-score. Through extensive experiments with computer vision models and datasets in three use cases -- mobile edge offloading, model selection, and dataset shift -- we find that proposed post-hoc models consistently outperform the standard calibrated confidence baselines. To the best of our knowledge, this is the first work to develop a unified framework to address different performance estimation problems for machine learning inference.
Expert System to Diagnose Human Heart Disease Using Web-Based Forward Chaining Method: Expert System to Diagnose Human Heart Disease Using Web-Based Forward Chaining Method

People do not realize that the heart is an organ that plays an important role in the circulatory system, and are very dangerous if the heart is experiencing problems or disturbances. But there are still many people who do not know the symptoms caused when the heart is having problems. In fact one of the causes of high mortality in Indonesia is due to heart disease. The purpose of this study is done to provide an update to the user to know the symptoms of heart disease as well as solutions. The method used in this study, is to use the Forward Chaining method. Expert systems will be created using PHP and MySQL database driven website.
Continual Learning for Image-Based Camera Localization

For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component. Directly regressing camera pose/3D scene coordinates from the input image using deep neural networks has shown great potential. However, such methods assume a stationary data distribution with all scenes simultaneously available during training. In this paper, we approach the problem of visual localization in a continual learning setup -- whereby the model is trained on scenes in an incremental manner. Our results show that similar to the classification domain, non-stationary data induces catastrophic forgetting in deep networks for visual localization. To address this issue, a strong baseline based on storing and replaying images from a fixed buffer is proposed. Furthermore, we propose a new sampling method based on coverage score (Buff-CS) that adapts the existing sampling strategies in the buffering process to the problem of visual localization. Results demonstrate consistent improvements over standard buffering methods on two challenging datasets -- 7Scenes, 12Scenes, and also 19Scenes by combining the former scenes.
The Role of Smart (Mobile) Phones in Learning and Improving English

The use of smart phones makes it essential for educationists to discover methods for using this new pattern in teaching-learning. A number of studies in integrating the use of mobile phones in teaching English indicate that the use of mobile phones has improved the capability of the students to improve their outcomes in language learning. Mobile phones simplify the teaching and learning process; they endeavor to recognize the mobile phone applications used for teaching and learning, research the sorts of learning exercises/activities enabled through mobile phones and evaluate the general confines of learning.
How does the User's Knowledge of the Recommender Influence their Behavior?

Recommender systems have become a ubiquitous part of modern web applications. They help users discover new and relevant items. Today's users, through years of interaction with these systems have developed an inherent understanding of how recommender systems function, what their objectives are, and how the user might manipulate them. We describe this understanding as the Theory of the Recommender. In this study, we conducted semi-structured interviews with forty recommender system users to empirically explore the relevant factors influencing user behavior. Our findings, based on a rigorous thematic analysis of the collected data, suggest that users possess an intuitive and sophisticated understanding of the recommender system's behavior. We also found that users, based upon their understanding, attitude, and intentions change their interactions to evoke desired recommender behavior. Finally, we discuss the potential implications of such user behavior on recommendation performance.
Digital inequalities in rural schools in South Africa

It has been argued that advancements in digitalisation and enhancement of digital development could be beneficial for rural schools. However, digital inequalities persist and affect rural schools since little attention has been paid to the specific conditions rural schools find themselves in when facing continuous digital developments. This paper aims at revealing the facts related to the status of the digital inequalities in rural schools in South Africa. Data was collected by means of the desk research, whereby researchers reviewed existing literature pertaining to digital inequalities in rural schools in South Africa, in Namibia and in some developed countries for cross referencing and triangulation purposes. After review of data from secondary sources, the study found out that lack of digital literacy on the side of the rural teachers can lead to accessibility and knowledge differences among learners and furthermore it was found that the Information and Communications Technology (ICT) equipment and digital literacy training in rural schools are very much dependent on luck: pupils depend on the socioeconomic status of their respective rural school. Considering the prevailing circumstances, through this paper it is recommended that there is a need for the state to invest in necessary infrastructure namely the ICT sector to provide for the driving of digital transformation and facilitate skills development programmes for teachers to ensure that they have the needed skills to utilise modern technology more effectively.
Online education models’ comprehensive analysis

Nowadays online learning is among the most progressive and most popular educational practices in the world. Both European and United States universities try to implement this way of learning into the educational process. Several models of online education are used in the world's practice. The article aims to analyze online learning models to find a way for the best implementation in higher education based on Ukrainian educational system during COVID 19 pandemic in 2020.
A dynamic data encryption method based on addressing the data importance in the internet of things

The rapid growth of Internet of Things (IoT) in multiple areas brings research challenges closely linked to the nature of IoT technology. Therefore, there has been a need to secure the collected data from IoT sensors in an efficient and dynamic way taking into consideration the nature of collected data due to its importance. So in this paper, a dynamic algorithm has been developed to distinguish the importance of data collected and apply the suitable security approach for each type of data collected. This was done by using hybrid system that combines block cipher and stream cipher systems. After data classification using machine learning classifiers the less important data are encrypted using SC that use Rivest Cipher 4 algorithm, and more important data encrypted using BC that use Advanced Encryption Standard algorithm. By applying a performance evaluation using simulation, the proposed method guarantees that it encrypts the data with less CPU time with improvement in the security over the data by using the proposed Hybrid system.
PENGARUH MOTIVASI DAN HASIL BELAJAR MAHASISWA TERHADAP PEMBELAJARAN DARING (STUDI KASUS MAHASISWA AKUNTANSI UNIVERSITAS BOSOWA)

SINTYA PUSPITASARI.2021.Skripsi. The Impact of Student Motivation and Learning Outcomes (Case Study of Accounting Students at University of Bosowa) supervised by Dr. H. A. Arifuddin Mane, SE., SH., M.Si., MH and Indah Syamsuddin, SE., M.Ak.The purpose of the study was to determine the effect of student motivation and learning outcomes on online learning (a case study of accounting students at the University of Bosowa. The object of the research is Accounting Students class 2018, 2019 and 2020, Faculty of Economics and Business, University of Bosowa. The analytical tool used is multiple linear regression analysis.The results showed that student motivation and learning outcomes had a positive and simultaneous effect on online learning by looking at the value of Fcount (52.562) which was greater than Ftable (3.09) and the significance level of 0.000 was less than 0.05.
Prose2Poem: The blessing of Transformer-based Language Models in translating Prose to Persian Poetry

Persian Poetry has consistently expressed its philosophy, wisdom, speech, and rationale on the basis of its couplets, making it an enigmatic language on its own to both native and non-native speakers. Nevertheless, the notice able gap between Persian prose and poem has left the two pieces of literature medium-less. Having curated a parallel corpus of prose and their equivalent poems, we introduce a novel Neural Machine Translation (NMT) approach to translate prose to ancient Persian poetry using transformer-based Language Models in an extremely low-resource setting. More specifically, we trained a Transformer model from scratch to obtain initial translations and pretrained different variations of BERT to obtain final translations. To address the challenge of using masked language modelling under poeticness criteria, we heuristically joined the two models and generated valid poems in terms of automatic and human assessments. Final results demonstrate the eligibility and creativity of our novel heuristically aided approach among Literature professionals and non-professionals in generating novel Persian poems.
DeepSCC: Source Code Classification Based on Fine-Tuned RoBERTa

In software engineering-related tasks (such as programming language tag prediction based on code snippets from Stack Overflow), the programming language classification for code snippets is a common task. In this study, we propose a novel method DeepSCC, which uses a fine-tuned RoBERTa model to classify the programming language type of the source code. In our empirical study, we choose a corpus collected from Stack Overflow, which contains 224,445 pairs of code snippets and corresponding language types. After comparing nine state-of-the-art baselines from the fields of source code classification and neural text classification in terms of four performance measures (i.e., Accuracy, Precision, Recall, and F1), we show the competitiveness of our proposed method DeepSCC
Money Grows on (Proof-)Trees: The Formal FA1.2 Ledger Standard.

Once you have invented digital money, you may need a ledger to track who owns what -- and an interface to that ledger so that users of your money can transact. On the Tezos blockchain this implies: a smart contract (distributed program), storing in its state a ledger to map owner addresses to token quantities, and standardised entrypoints to transact on accounts. A bank does a similar job -- it maps account numbers to account quantities and permits users to transact -- but in return the bank demands trust, it incurs expense to maintain a centralised server and staff, it uses a proprietary interface ... and it may speculate using your money and/or display rent-seeking behaviour. A blockchain ledger is by design decentralised, inexpensive, open, and it won't just bet your tokens on risky derivatives (unless you ask). The FA1.2 standard is an open standard for ledger-keeping smart contracts on the Tezos blockchain. Several FA1.2 implementations already exist. Or do they? Is the standard sensible and complete? Are the implementations correct? And what are they implementations \emph{of}? The FA1.2 standard is written in English, a specification language favoured by wet human brains but notorious for its incompleteness and ambiguity when rendered into dry and unforgiving code. In this paper we report on a formalisation of the FA1.2 standard as a Coq specification, and on a formal verification of three FA1.2-compliant smart contracts with respect to that specification. Errors were found and ambiguities were resolved; but also, there now exists a \emph{mathematically precise} and battle-tested specification of the FA1.2 ledger standard. We will describe FA1.2 itself, outline the structure of the Coq theories -- which in itself captures some non-trivial and novel design decisions of the development -- and review the detailed verification of the implementations.
A super-resolution face enhancement algorithm based on eigen transformation

Abstract A tensor feature transformation and enhancement algorithm are proposed in this paper based on PCA algorithm to address the challenges in face detection from low-resolution images. A 2D-PCA face super-resolution enhancement algorithm of a second-order tensor replaces the traditional 1D-PCA method for feature transformation to improve the accuracy of feature tensor solution and reduce the computational complexity. Furthermore, a coupling PCA method is applied for resolution enhancement of the combination of global information and local information of face image. Experimental results verify the stable and superior performance of the proposed algorithm.
5 Pre Filtering with Rule Mining for Context Based Recommendation System

Recommendation systems have been the integral part of web and mobile applications in the domains of e commerce, e learning, e health, e governance, social networking and search engines. The problem of spending more time in getting the required relevant information from the many options available, also called as the information overload problem is addressed by the recommendation systems. The context based recommendation systems are the types of recommendation systems which use the context information to provide the recommended items. The context is the data about the application or the surroundings or the purpose with which the user is interacting with the system which can be like time, location, type of product, user’s purpose or any situation describing the interaction. In this work the architecture of the context based recommendation system is proposed with the pre filtering method with context rules. The class and object based model of context with rules and recommendation system is proposed which can be converted into a relational model for data storage for the recommendation system. One of the actions like rating is used to predict the preference of items for the current user. The analysis of the system is carried out with the Het Rec 2011 Movielens data set. The accuracy measure MAE (Mean Absolute Error) is analysed in the proposed work and the relevance is measured in terms of precision, recall and F1 measure. The experimental result shows the influence of context on action and improvement in quality of recommendation with the proposed method.
Vernacular literacy practice by freshmen: Motivation and demotivation

In this study, we analyzed the vernacular literacy practice of 32 freshmen students from a public university in southern Mexico using a mixed method approach, with narratives or biographical experiences, and a sociocultural theoretical perspective. The results show a limited vernacular literacy practice in as much as quantity, types, genres, and modalities of texts.  Six dimensions of motivation were examined: family and family context, teachers and school context, friends, interest in writing, movies, and other media, learning and personal and cultural growth. The external motivations seem to exert great influence as they, additionally, favor the development of the internal motivations. We also found that these motivations are dynamic and can change according to age or the interaction with other sources of motivation. As sources of demotivation, we identified:  family and family context, teachers and school context, distractors, and personal problems, and reading comprehension problems. The first two have the capacity to motivate or demotivate the vernacular literacy practice and have a determining role in it. The second two sources belong to the personal sphere and have a great impact on the literacy experience.
Coaching by Guided Discovery

Coaching by Guided Discovery Style F is characterised by the logical and sequential design of questions that lead the players to a coach’s pre-determined response. In effect, Coaching by Guided Discovery is intended to develop ‘understanding’ rather than simply ‘knowing’ or ‘recalling’. When Coaching by Guided Discovery, each question posed by the coach requires a single correct answer. An important skill of the coach, when applying Coaching by Guided Discovery, is to listen to the players’ responses to interpret the significance or completeness of the response in order to know whether to re-frame the question to get a more complete or meaningful response, or to move on to the next question. One example of applying Guided Discovery in kayaking can be considered in the refining of correct paddling position regardless of the range of strokes that may be executed by paddlers, such as forward, reverse, sweep or rudder.