Reviewerly/exHarmony huggingface dataset:
ANALISIS PARAMETER CURAH HUJAN DAN SUHU UDARA DI KOTA MAKASSAR TERKAIT FENOMENA PERUBAHAN IKLIM

Climate change is a global phenomenon that can be felt locally. The potential impact of climate change is a change in rainfall patterns and an increase in air temperature. One area that is vulnerable to climate change is urban areas, people and development are the driving factors for increasing greenhouse gas emissions. For climate change conditions in Makassar City, it is necessary to analyze the parameters of rainfall and air temperature. The data used are daily rainfall data and air temperature from 1991 - 2020 obtained from UPT BMKG Kelas I Maros. Data analysis was carried out by calculating the trend of rainfall and maximum air temperature, as well as calculating the frequency of rainfall with very heavy intensity (rainfall &gt; 100 mm/day). The results of the analysis show an increase in the value for the rainfall parameter of 8.2 mm/year and the maximum air temperature of 0.0317 °C/year, but there is a decrease in heavy rainfall. Annual rainfall is increasing but the frequency of heavy rains is increasing from the occurrence of rain in Makassar City but will occur more often only with high intensity. Maximum air temperature can cause increased drought in the dry season, and increased rainfall can cause flooding in the rainy season.Keywords: rainfall, air temperature, trend
MULTI-FACTOR ANALYSIS OF THE SCALES OF EVALUATING KNOWLEDGE BASED ON A FUZZY-PROBABILITY APPROACH

Teachers always face problems when assessing the competence level of test participants (whether students, undergraduates, etc.) in both moral and psychological fields as well as methodological. This raises important questions on the creation of professional systems, which have mathematical models set forth for their vital foundational path. The current working method in the creation of mathematical models for evaluating the competency of test participants is based on a fuzzy logic approach using both the explicit and implicit knowledge of highly qualified experts. One can see the realisation of this method in the example of Englishlanguage instruction. A comparison of two common assessment scales for knowledge of the tested has been carried out – the «fivepoint » standard adopted in Russia (or required, actually) and the converted sevenpoint ECTS (European Credit Transfer and Accumulation System) rating scale which is based on percentage. This study has shown that teachers cannot effectively assess the participants’ competency, neither on individual topics nor on the course as a whole, due to the narrowness of the «fivepoint » scale.
An evaluation of image segmentation techniques for MRI scans

In this thesis, several image segmentation techniques will be tested that eventually will be applied to MRI brain scans in order to detect hydrocephalus. The meth- ods include Sobel edge detection, Canny edge detection, active contour model (also known as snakes), k-means clustering and region growing. Furthermore two exten- sions are discussed. We propose a method to complete disconnected edges and, as an extension to the k-means clustering algorithm, we suggest a manner to reassign pixels to different clusters. The focus of the first part of the research lies on explaining and illustrating these methods and extensions. Two test images are used, namely the Shepp-Logan Phan- tom and an MRI scan of a recreated Shepp-Logan Phantom. In the second part, we evaluate each method by applying the methods to two independent images, that is an MRI scan of an apple and a CT scan of a brain with hydrocephalus. We discuss whether methods are useful and behave as expected. Additionally, we investigate ways to combine methods.
Менеджмент організацій. Методичні рекомендації до виконання та захисту курсових робіт з дисципліни для здобувачів другого (магістерського) рівня вищої освіти спеціальності 073 «Менеджмент» усіх форм навчання

The guidelines are designed to help all studentsforms of education while writing a term paper on the subject Managementorganizations . They set out the procedure and methods of writing a term paper,requirements for its design, provided an indicative list of topics that will contributequalitative performance and successful protection of term papers by applicants of the second(master's) level of higher education.
Improving Span Representation for Domain-adapted Coreference Resolution

Recent work has shown fine-tuning neural coreference models can produce strong performance when adapting to different domains. However, at the same time, this can require a large amount of annotated target examples. In this work, we focus on supervised domain adaptation for clinical notes, proposing the use of concept knowledge to more efficiently adapt coreference models to a new domain. We develop methods to improve the span representations via (1) a retrofitting loss to incentivize span representations to satisfy a knowledge-based distance function and (2) a scaffolding loss to guide the recovery of knowledge from the span representation. By integrating these losses, our model is able to improve our baseline precision and F-1 score. In particular, we show that incorporating knowledge with end-to-end coreference models results in better performance on the most challenging, domain-specific spans.
BERT Has Uncommon Sense: Similarity Ranking for Word Sense BERTology

An important question concerning contextualized word embedding (CWE) models like BERT is how well they can represent different word senses, especially those in the long tail of uncommon senses. Rather than build a WSD system as in previous work, we investigate contextualized embedding neighborhoods directly, formulating a query-by-example nearest neighbor retrieval task and examining ranking performance for words and senses in different frequency bands. In an evaluation on two English sense-annotated corpora, we find that several popular CWE models all outperform a random baseline even for proportionally rare senses, without explicit sense supervision. However, performance varies considerably even among models with similar architectures and pretraining regimes, with especially large differences for rare word senses, revealing that CWE models are not all created equal when it comes to approximating word senses in their native representations.
AN INSIGHT INTO CLOUD COMPUTING TECHNOLOGY

Cloud computing is a rapidly developing and excellent promising technology. It has aroused the concern of the computer society of whole world. Cloud computing is Internet-based computing, whereby shared information, resources, and software, are provided to terminals and portable devices on-demand, like the energy grid. Cloud computing is the product of the combination of grid computing, distributed computing, parallel computing, and ubiquitous computing. It aims to build and forecast sophisticated service environment with powerful computing capabilities through an array of relatively low-cost computing entity, and using the advanced deployment models like SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service), HaaS (Hardware as a Service) to distribute the powerful computing capacity to end-users. This paper will explore the background and service models and also presents the existing research issues and implications in cloud computing such as security, reliability, privacy, and so on.
Optimizing the Information of Sport Graphics in the Major League Baseball

Baseball statistics are one of the biggest concerns of baseball fans. This information allows viewers to look at the game from a different perspective. With the development of broadcast technology, complex statistics and information are increasing. The purpose of this study was to investigate the effects of baseball mirror information on audience enjoyment and cognitive load, as well as users’ preference and evaluation of the information. This study analyzed 11 regional broadcast networks of Major League Baseball (MLB) and selected the three most-watched networks (AT&T, Fox, and NBC) as experimental samples. Each sample assigned to 10 subjects, and a total were 30 subjects joined this study. The task consisted of three parts: watching baseball clips on different screens, questionnaires, and semi-structured interviews. After viewing the clips, we asked the subjects about their perceptions of the clip. Completed the 7-point Likert Scale questionnaire, which was composed of NASA-TLX and the Measure of Enjoyment and Meaningfulness. Another scale was to examined involvement with baseball fans. Subjects with low-involved baseball spectators (2.86) had a low understanding of the content than people with high-involved baseball spectators (5.06). Enjoyment was positively correlated with meaningfulness and com-prehension (P = .003). 86% of respondents said that visual information helps to facilitate the viewing experience. This study found that users’ preferences for viewing information could be a reference for interface design of future broadcast events and improve the spectator's watching experience.
Quantum Noise Imaging

We image an object solely illuminated with squeezed vacuum by analyzing the quantum noise statistics and show how the quality of the image is linked to the squeezing mode size and detection area.
The Impact of Vigilantism and Crime Control in Contemporary Nigeria: A Case Study of Gezawa Local Government Area, Kano State, Nigeria (2010-2015)

The inability of the Nigerian Police Force to control the increasing wave of crime has led to proliferation of vigilante groups in contemporary Nigeria. This study was initiated to investigate the impact of vigilante groups on crime control and management in contemporary Nigeria, particularly in GEZAWA L.G.A of KANO STATE. One hundred and twenty (120) respondents were sampled from GEZAWA LOCAL GOVERNMENT Area of KANO STATE. Multi-stage, cluster and simple random sampling methods were used as the sampling techniques. Questionnaires were administered to respondents by two (2) research assistants whereas interviews were conducted with various stakeholder (2) traditional rulers, chairman of Gezawa L.G.A or his representatives, 2 Commandants of vigilante groups and 5 vigilante group members from various units. The result indicated that Nigeria Police is ineffective with regard to crime control and vigilante groups in its current form sprang up to make up for this ineffectiveness and fill the gap of insecurity at grassroots level . It also showed that vigilante groups are gaining prominence as an agents of crime control in contemporary Nigeria, particularly, Gezawa L.G.A of KANO STATE. The result equally revealed that vigilante group members are recruited and monitored through their communities and that they are effective in crime control and management. Also, the result indicated that the best way of solving the problem of crime in Nigeria is by increasing the activities of vigilante groups through training and support from local communities and Government. However, lack of working tools, poor funding, and lack of patrol vehicles are the major problems confronting vigilante groups. More so, based on the major findings, it was recommended that vigilante group members should operate within the frame work of law, Vigilante groups needed to have support from both local communities and other relevant stake holders in the process of discharging their responsibilities security at grassroots level. Finally, security committees should be established at the local government level and these committees should meet periodically to discuss security matters in their areas and report back any issues of insecurity to the relevant security agencies for proper management and control of crime in the country.
Solar Power Forecast Using Artificial Neural Network Techniques

Solar energy is a clean, available and renewable source of energy. Deployment of photovoltaic panels is trending to take advantage of solar energy for electrical power generation. Intermittent nature of solar energy results in variable generated power. This uncertainty could be alleviated by taking advantage of energy storage systems and accurate solar power forecast. This thesis aims to implement a solar forecast module to take part in an optimized Energy Management System (EMS). Different solar power prediction methods are studied including statistical methods, sky imagers, satellite imaging and Numerical Weather Prediction (NWP). Artificial neural networks (ANNs) which are a subset of statistical methods are chosen as prediction method to satisfy requirements imposed by EMS. The requirements include precise forecast in short-term prediction horizon for proper functionality of the EMS. Prediction horizon is amount of time in future for which prediction is needed. Multiple ANN architectures suitable for time-series prediction are investigated and compared, including Long Short-term Memory (LSTM), Long- and Short-term Time Series Network (LSTNet) and Temporal Convolutional Network (TCN). Although all the aforementioned methods have acceptable performance, TCN architecture shows more promising results. In order to further improve the prediction accuracy, effect of clustering the dataset into sunny and cloudy sub-datasets and using a dedicated prediction module for each subdataset is studied. Results show that prediction accuracy is improved by clustering the dataset for all the models. Moreover, simulation results on datasets of multiple geographical locations with different climate conditions show that prediction accuracy is higher in locations having more stable weather and sunny days. A Graphical User Interface (GUI) is implemented to simplify working with the forecast module. Service is made available to users through exposing REST APIs on a remote server to facilitate user interactions with the forecast service.
Online Variational Filtering and Parameter Learning

We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the log evidence with respect to both model parameters and a variational approximation of the states' posterior distribution. However, unlike existing approaches, our method is able to operate in an entirely online manner, such that historic observations do not require revisitation after being incorporated and the cost of updates at each time step remains constant, despite the growing dimensionality of the joint posterior distribution of the states. This is achieved by utilizing backward decompositions of this joint posterior distribution and of its variational approximation, combined with Bellman-type recursions for the evidence lower bound and its gradients. We demonstrate the performance of this methodology across several examples, including high-dimensional SSMs and sequential Variational Auto-Encoders.
Probing Language Models for Understanding of Temporal Expressions

We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions. More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units. We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions.
Neural Machine Translation Quality and Post-Editing Performance.

We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.
Lineamientos pedagógicos y organizativos para la transformación de la práctica docente hacia la promoción de la comprensión lectora

The collective design of pedagogical and organizational guidelines for the transformation of pedagogical practice aimed at promoting reading comprehension in students at the Universidad del Atlantico in Colombia, the central objective of this article, was framed in the Socio-Critical paradigm, with a focus mixed and the use of Likert scale questionnaires, semi-structured interview and focus group, applied to 100 teachers and 240 students of the first semesters of the programs of this higher education house. It was determined that transmission and repetition with evaluation strategies oriented to cognitive conflict prevail in the classrooms as an educational activity, as well as the organization of the students in a group way, mediated participation and a reflective pedagogical practice from a constructivist approach. On the other hand, it was detected that the self-regulation of learning is considered in a moderate way together with an inferential level in the reading comprehension of the students of different careers. In this sense, it is recognized that the transformation of teaching practice from organizational and pedagogical actions could reinforce the self-regulation of learning in reading comprehension, the comprehensiveness of reflection in all areas of knowledge within the UA from conciliation and agreements in around teaching strategies that promote reading in university spaces, being one of the main skills for any profession being studied
A Scheme of Pairwise Feature Combinations to Improve Sentiment Classification Using Book Review Dataset

Information Retrieval has been in existence since the 1940s and is impossible to do without. However, current information retrieval systems are known to be ineffective. The reason that seems to encompass this ineffectiveness is design-focused. Designers and developers of Information Retrieval Systems are known to be system focused rather than user-focused. They provide the same information to users even when they are in different contexts and have diverse preferences. Using a restaurant use case, we propose a conceptual user-centric multi-context hybrid reasoning Information Retrieval model to improve the accuracy of retrieved results.
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation

QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions. Its adaptation to Data-to-Text tasks is not straightforward, as it requires multimodal Question Generation and Answering systems on the considered tasks, which are seldom available. To this purpose, we propose a method to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric. The resulting metric is reference-less and multimodal; it obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks. We make data-QuestEval’s code and models available for reproducibility purpose, as part of the QuestEval project.
Boosted CVaR Classification

Many modern machine learning tasks require models with high tail performance, i.e. high performance over the worst-off samples in the dataset. This problem has been widely studied in fields such as algorithmic fairness, class imbalance, and risk-sensitive decision making. A popular approach to maximize the model's tail performance is to minimize the CVaR (Conditional Value at Risk) loss, which computes the average risk over the tails of the loss. However, for classification tasks where models are evaluated by the zero-one loss, we show that if the classifiers are deterministic, then the minimizer of the average zero-one loss also minimizes the CVaR zero-one loss, suggesting that CVaR loss minimization is not helpful without additional assumptions. We circumvent this negative result by minimizing the CVaR loss over randomized classifiers, for which the minimizers of the average zero-one loss and the CVaR zero-one loss are no longer the same, so minimizing the latter can lead to better tail performance. To learn such randomized classifiers, we propose the Boosted CVaR Classification framework which is motivated by a direct relationship between CVaR and a classical boosting algorithm called LPBoost. Based on this framework, we design an algorithm called $\alpha$-AdaLPBoost. We empirically evaluate our proposed algorithm on four benchmark datasets and show that it achieves higher tail performance than deterministic model training methods.
Assessment of Service Departments in the Governorates Through Social Media Comments

Social media have been increasing obviously and widely due to the fact that it is a media for users who express their emotions using reviews and comments on a variety of areas in life. In the present study, a modest model has been suggested for the assessment of service departments with the use of reviews and comments in social media pages of those departments from various governorates. The utilization of the text mining for the sentiment classification has been used through collecting Iraqi dialect reviews on service department pages on Facebook to be analyzed with the use of the sentiment analysis to track the emotions from the comments and posts. Those have been classified after that to positive, neutral or negative comment with the use of the algorithms of Naive Bayesian, Rough Set Theory, and K-Nearest Neighbors. Out of 13 Iraqi capital (Baghdad) service departments have been tackled, it has been found that 11% of those departments had very good assessment, 18% from these service departments have good assessment, 21% from these service departments have medium assessment, 24% from these service departments have acceptance assessment and 26% from these service departments have bad assessment. The results of the evaluation showed the poor services provided by service departments in the capital Baghdad. Experimental results were helpful for the service departments in improving their work and programs had responded quickly and sufficiently to the customer demands. Index Terms—Facebook Data, Rough Set Theory, Naïve Bayesian, Sentiment Analysis, K-NN, Text Mining.
Research on Image Segmentation based on Multibranch Convolution Model

Image semantic segmentation is a very important research field in computer vision task. In view of the discontinuity of segmentation in existing semantic segmentation models, poor segmentation effect on small target objects and large amount of computation, a module similar to Iception is designed, which is called Multi-Branch Convolution (MBC) in this paper to decompose the convolution layer of the atrous space pyramid pooling (ASPP). The experimental results show that the improved network model achieves segmentation results of 71.3%(MIoU) and 90.9%(FWIoU).