Reviewerly/exHarmony huggingface dataset:
Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading

Timely cancer reporting data are required in order to understand the impact of cancer, inform public health resource planning and implement cancer policy especially in Sub Saharan Africa where the reporting lag is behind world averages. Unstructured pathology reports, which contain tumor specific data, are the main source of information collected by cancer registries. Due to manual processing and labelling of pathology reports using the International Classification of Disease for oncology (ICD-O) codes, by human coders employed by cancer registries, has led to a considerable lag in cancer reporting. We present a hierarchical deep learning classification method that employs convolutional neural network models to automate the classification of 1813 anonymized breast cancer pathology reports with applicable ICD-O morphology codes across 9 classes. We demonstrate that the hierarchical deep learning classification method improves on performance in comparison to a flat multiclass CNN model for ICD-O morphology classification of the same reports.
Aplicación móvil para envío masivo de mensajes. Notitec

El presente articulo muestra los resultados orientados a la mejora de la informacion y comunicacion de mensajes a traves de una plataforma de envio masivos de informacion denominada NotiTec, la cual es una aplicacion de software (APP) para dispositivos moviles que gestiona y envia mensajes masivos a diferentes usuarios registrados en la aplicacion. Para el desarrollo de esta aplicacion se utilizan herramientas para desarrollo Web y Movil, asimismo se analiza el proceso de comunicacion de la institucion para el envio de notificaciones y mensajes, si bien en el mercado existen aplicaciones para el envio de mensajes SMS, estos solo cumplen de manera parcial las necesidades de la institucion. Entre los resultados alcanzados estan la notificacion masiva sobre eventos, noticias y avisos realizados en la institucion y dirigidos a diferentes usuarios. Permitiendo la integracion de mensajes de texto a traves de una APP se mejora en la efectividad y el tiempo de entrega de estas notificaciones.Abstract: This article shows the results aimed at improving information and message communication through a platform for sending mass information called NotiTec, which is an software application (APP) for mobile devices that manages and sends mass messages to different users registered in the application. For the development of this application, tools for Web and Mobile development are used, the institution's communication process for sending notifications and messages is also analyzed, although there are applications in the market for sending SMS messages, they only comply partially the needs of the institution. Among the results achieved are the mass notification of events, news and notices made in the institution and addressed to different users. Allowing the integration of text messages through an APP improves the effectiveness and delivery time of these notifications.
LibGuides: Communications: Home

This guide is intended as a starting place for research in the communications field of study.
Simulation of the New Proposed Method by CAPES for the Qualis 2017-2020 Classification of the Brazilian Archives of Biology and Technology

This article aimed to simulate the new method proposed by the Brazilian Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES) for the classification of Qualis 2017-2020 by tracing the path that led to the B3 stratification attributed to the journal Brazilian Archives of Biology and Technology (BABT).Data collection was based on the evaluation of articles published between 2017 and 2018 to obtain the Lattes curricula of the author(s).Other goals were to verify their participation in stricto sensu postgraduate programs recognized by CAPES; to identify the program's evaluation area according to CAPES; and to establish the association of authors/coauthors with their respective countries.Overall 199 articles were identified, with 897 authors/co-authors, 359 of which are associated with Brazilian institutions.A participation of at least one Brazilian author/co-author could be verified in 69 articles, out of which 60 had at least one professor affiliated to a stricto sensu postgraduate program recognized by CAPES.There was professors linked to 26 of the 49 CAPES evaluation areas, whereby the highest number of publications were Food Science and Agrarian Sciences I, both with 16 HIGHLIGHTS  CAPES' proposed method for the Qualis classification of BABT was simulated. 359 authors/co-authors affiliated with Brazilian institutions and 538 with foreign ones.  Agrarian Sciences I and Food
Through a Random Route to the Goal: Theoretical Background and Application of the Method in Tourism Surveying in Poland

Abstract Classic survey methods are ineffective when surveying a small or rare population. Several methods have been developed to address this issue, but often without providing a full mathematical justification. In this paper we propose estimators of parameters relating to Random Route Sampling and explore their basic properties. A formula for the Horvitz-Thompson estimator weights is presented. Finally, a case of a tourism-related survey conducted in Poland is discussed.
Application of Data Mining to Predict Procurement of Office Writing in Al-Ikhwan Middle School using Naïve Bayes Method

Office stationery (ATK) is now a necessity for almost everyone, especially for corporate instances or educational institutions. The need for office stationery is often an unexpected need to buy, this is what makes some educational institutions overwhelmed in doing their work, when they find out their office stationery is out of stock, so it is not uncommon to make work in company or institutional instances education is not completed on time, one of the ways to be more efficient is by implementing data mining to predict the purchase of office stationery (ATK) at the company or educational institution's intents, especially at the AL IKHWAN Middle School in Tanjung Morawa. The Naive Bayes method is used to analyze data in pattern recognition and predict purchase of office stationery (ATK) at AL IKHWAN Middle School in Tanjung Morawa. The data needed is the data for the purchase of office stationery (ATK) last month as test data, calculated from the date of the first purchase until the expiry date of office stationery (ATK) at AL IKHWAN Middle School in Tanjung Morawa. The results of this study are to be able to predict whether the office stationery (ATK) at AL IKHWAN Middle School in Tanjung Morawa can be bought back, or it can still be used for a long time, and if more than four types of stationery at the AL IKHWAN Middle School in Tanjung Morawa the lack of writing instruments from the two then the purchase of new stationery is feasible to do, from the amount of data occurring out of stationery
Facies Classification: Combining Domain Knowledge with Machine Learning Solutions

Summary Automated facies identification workflows which use Machine Learning (ML) are publicly available but perform sub-optimally (accuracy in the order of 60%) due to a lack of integration with geological domain knowledge. Existing tools consider well log values mostly on a depth-by-depth basis, using only very basic feature engineering. Our solution aims to integrate ML with well-established geoscience principles (also referred to as geo-rules) such as sequence stratigraphy, proximal-distal trends, and log-trend patterns. Geological knowledge is incorporated into ML to improve the quality and robustness of facies prediction and is captured as additional geologically-inspired features added to the dataset. These features include the mean value and other derived properties of intervals, density-neutron separation, segmentation and wavelet transform. All ML algorithms tested with this augmented set of features show significant improvement in performance metrics as compared to solutions with basic logs only.
Edge-variational Graph Convolutional Networks for Uncertainty-aware Disease Prediction

There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we mathematically prove that it is optimizable in conjunction with graph convolutional neural networks. To estimate the predictive uncertainty related to the graph topology, we propose the novel concept of Monte-Carlo edge dropout. Experimental results on four databases show that our method can consistently and significantly improve the diagnostic accuracy for Autism spectrum disorder, Alzheimer's disease, and ocular diseases, indicating its generalizability in leveraging multimodal data for computer-aided diagnosis.
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition

People can manipulate emotion expressions when interacting with others. For example, acted anger can be expressed when stimuli is not genuinely angry with an aim to manipulate the observer. In this paper, we aim to examine if the veracity of anger can be recognized with observers' pupillary data with computational approaches. We use Genetic-based Feature Selection (GFS) methods to select time-series pupillary features of of observers who observe acted and genuine anger of the video stimuli. We then use the selected features to train a simple fully connected neural work and a two-stream neural network. Our results show that the two-stream architecture is able to achieve a promising recognition result with an accuracy of 93.58% when the pupillary responses from both eyes are available. It also shows that genetic algorithm based feature selection method can effectively improve the classification accuracy by 3.07%. We hope our work could help daily research such as human machine interaction and psychology studies that require emotion recognition .
Bacterial foraging optimization algorithm with mutationto solve constrained problems

A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineeringdesign optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm
The Moderating Effect of Organisational Culture on the Relationship Between Workplace Learning and Employees’ Performances in the United Arab Emirates

The purpose of the current study was to elaborate the moderating effect of culture on workplace learning and employees’ performances in the United Arab Emirates. The study had a purpose to contribute new knowledge to the existing literature available on the workplace learning and job performances. It also highlighted the contemporary literature on the topic specifically formal and information learning, as well as, performance related to tasks and contextual. The research explained and highlighted the role of training and development on employees’ performances to improve the quality of task process. An empirical study was conducted and data was collected through questionnaire to obtain the results. The present study aimed to contribute to new knowledge to the existing literature on workplace learning and job performance. Particularly, the study analysed contemporary literature on workplace learning and job performances, specifically formal and informal learning as well as employee task performance and contextual performance. The study hypothesized that informal, incidental and formal workplace learning had direct positive significant relationships with employee task and contextual performance. Findings of the study developed that adopting effective techniques of workplace learning, and techniques can improve employees’ performances. The study, further, showed that the Informal, formal and incidental workplace learning had direct and positive impact on employee task and contextual performance. The study showed that there is a significant positive relationship between workplace learning including formal, informal and incidental learning, and job performance including task performance, contextual performance and counterproductive work behaviour. It was also found that there was a positive relationship between result-oriented cultures with two types of job performance but there was a significant relationship between team orient culture and task performance. Furthermore, the moderating effect of innovation, communication and people-oriented culture on the relationship between incidental workplace learning and contextual performance was significant.
Sistem Pakar Menggunakan Metode Certainty Factor Dalam Akurasi Identifikasi Penyakit Panleukopenia Pada Kucing

Feline Panleukopenia is a disorder caused by inflammation of Feline Parvovirus. This viral inflammation can trigger inflammation of the digestive system in cats and can also attack the cat's bone marrow, and the total white blood cells in the cat's body will decrease. At this time, many people do not know how to accurately identify the disease Feline Panleukopenia in cats and as a result of that cats who suffer from it experience illness and even death. This study aims to determine the level of accuracy in identifying Panleukopenia in cats. There are several types of symptoms that refer to Feline Panleukopenia. Furthermore, the data is processed manually with the Certainty Factor method and continued by using a website-based expert system software. The processing stages are rule solving, determining the weight value of each symptom and calculating the Certainty Factor value with the rule formula. The results of the data processing are continued with the calculation of the level of accuracy. The result of testing this method is that there are 100% of the 5 test data. Based on the accuracy of the identification results of this system, this study is very precise in the level of identifying the level of accuracy of feline panleukopenia. Expert testing systems have been able to specifically identify Feline Panleukopenia. Through the Certainty Factor method, the level of accuracy that can be obtained is quite accurate and can help veterinarians improve their accuracy to identify Feline Panleukopenia in cats.
A CLARIAH Environment for Linguistic Research

With the rise of digital humanities and research infrastructures many tools and services have become available for linguistic research. However, often these tools and services come with their own dedicated environments, e.g., a specific programming or user interface. In the CLARIAH project (CLARIAH-NL, 2019) a Virtual Research Environment (VRE) is constructed, which unifies the access to these dedicated environments and thus lowers the barier for linguists to apply the new methods coming available in the field of Digital Humanities to their own research. This VRE stands in a tradition to provide researchers with an environment that brings together different services and tools necessary for a researcher to perform her work. An important discussion platform is the RDA VRE Interest Group (RDA, 2018). The main features of this linguistics VRE will include: easy up- and download of a researcher’s own resources; automatic analysis for known resource types; automatic indexing of linguistic resources; profile matching of services and resources; hide invocation details of different types of services; storing the provenance track of the resources; gradually eliciting the resource’s metadata; persist a resource into a repository.
ECOC as a Method of Constructing Deep Convolutional Neural Network Ensembles.

Deep neural networks have enhanced the performance of decision making systems in many applications including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the constituent networks is very high or the performance gain obtained is not very significant. In this paper, we analyse error correcting output coding (ECOC) framework to be used as an ensemble technique for deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a combinatory technique which is shown to reveal the highest classification performance amongst all.
CSUSM Library Guides: GEO 102 Library Resources : Starting your background research

This guide includes library resources and assignments for students enrolled in GEO 102: General Education - Oral Communications Tutorial that will walk you through the process of locating background information on your speech topic.
A fitness dependent salp swarm algorithm

Salp Swarm Algorithm (SSA) is a novel swarm technique using to optimize design problems. SSA is inspired by the swarming behavior of salp observed in the deep area of sea. In spite of its application versatility, SSA suffers from mediocre convergence rate and limited exploratory capabilities. In this paper, a Fitness Dependent Salp Swarm Algorithm (FDSSA) is proposed. The novelties of the proposed approach are the definition of a fitness coefficient able to enhance the exploration, the introduction of a novel mathematical model to describe the trajectory of salps and a mutation mechanism to increase the convergence speed. The designed algorithm is tested on unimodal and multimodal benchmark functions and then compared with well-known heuristic algorithms. The results show the superiority of FDSSA with respect to the comparison algorithms in terms of optimization and convergence performances according to their computational complexity.
Reflective Practices To Explore Postgraduate Students Learning Experience In Mobile Learning Platform

Mobile Learning (M-learning) has great potential to be an effective learning tool in creating meaningful learning experiences. The purpose of this study is to explore postgraduate students M-learning experiences in Massive Open Online Courses (MOOCs) platform. Reflective practice is used to measure the students M-learning experience based on mobile-based task-centered activity. This study is qualitative in nature employed thematic analysis method. Therefore, 34 postgraduate students from a public university in Malaysia were selected for this purposive sampling. The postgraduate students were initially introduced to M-learning course content in FutureLearn platform followed by reflective practices. Pre-reflective practice happened before students are enlisted into the M-learning process, During-reflective process in FutureLearn platform and Post-reflective upon completion of task-centered activity. The findings revealed that majority of these students were unsure of M-learning platform during the pre-reflective stage. This is due to lack of prior knowledge and experience in M-learning platform using mobile devices as real time hindered the students from exploring. However, most of them progress to gained positive M-learning experiences as their able to monitoring and self-evaluate their own learning development throughout this learning process and completed the task-centered activity successfully. Therefore, the reflective approach practice has given students insight on their meaningful learning experience and their own learning capabilities.
Unconstrained Thermal Hand Segmentation

Robust segmentation of thermal hand images, a trivial task in controlled environments, can be difficult for unconstrained acquisition with a thermal camera, when the temperature of the object differs little from this of a background. This paper implements a method for segmenting hand images collected in the thermal spectrum involving a pre-trained, off-the-shelf data-driven model, fine-tuned with multiple databases of thermal hand images and corresponding, manually annotated ground truth masks. This allows superior performance for difficult samples, compared against conventional methods such as Otsu's thresholding and Gaussian Mixture Modeling. The segmentation accuracy for good quality samples is comparable with these traditional methods, while at the same time being more unconstrained acquisition-resistant. An improvement of up to 20% in accuracy measured by intersection over union is observed for difficult samples, such as hands that were partially colder than the background, as well as those with wristwatches and jewelry. This robust segmentation in various acquisition scenarios allows not only the correct localization of regions of interest for feature extraction, but also for extracting accurate hand geometry information. Together with the results of experiments, involving multiple training and testing scenarios on three different databases, we provide codes, model weights, and ground truth masks to ensure reproducibility and facilitate further research.
Exponential-wrapped distributions on symmetric spaces

In many applications, the curvature of the space supporting the data makes the statistical modelling challenging. In this paper we discuss the construction and use of probability distributions wrapped around manifolds using exponential maps. These distributions have already been used on specific manifolds. We describe their construction in the unifying framework of affine locally symmetric spaces. Affine locally symmetric spaces are a broad class of manifolds containing many manifolds encountered in data sciences. We show that on these spaces, exponential-wrapped distributions enjoy interesting properties for practical use. We provide the generic expression of the Jacobian appearing in these distributions and compute it on two particular examples: Grassmannians and pseudo-hyperboloids. We illustrate the interest of such distributions in a classification experiment on simulated data.
Reverse-engineering Bar Charts Using Neural Networks

Reverse-engineering bar charts extracts textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder-decoder framework that integrates convolutional and recurrent neural networks to extract numeric information. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts.