Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Detect Malaria using Deep Learning

Mosquitoes, though seeming to be tiny and fragile, transmit numerous diseases and are responsible for threatening the health of billions of people around the world. Combating these insects is becoming increasingly difficult as they are developing resistance to available insecticides. Malaria, one of the diseases transmitted through infected mosquitoes, is life-threatening in various parts of the developing world. However, early detection of this disease is propitious for proper diagnosis and effective cure. Automating the task of detecting malarial parasite from cell images using Mathematical techniques, like Machine learning algorithms or Neural Networks for deep learning, can help with the accurate diagnosis as well as funnelling resources on other important aspects of the treatment in resource-scarce areas.
Empirical Strategy for Stretching Probability Distribution in Neural-network-based Regression

In regression analysis under artificial neural networks, the prediction performance depends on determining the appropriate weights between layers. As randomly initialized weights are updated during back-propagation using the gradient descent procedure under a given loss function, the loss function structure can affect the performance significantly. In this study, we considered the distribution error, i.e., the inconsistency of two distributions (those of the predicted values and label), as the prediction error, and proposed weighted empirical stretching (WES) as a novel loss function to increase the overlap area of the two distributions. The function depends on the distribution of a given label, thus, it is applicable to any distribution shape. Moreover, it contains a scaling hyperparameter such that the appropriate parameter value maximizes the common section of the two distributions. To test the function capability, we generated ideal distributed curves (unimodal, skewed unimodal, bimodal, and skewed bimodal) as the labels, and used the Fourier-extracted input data from the curves under a feedforward neural network. In general, WES outperformed loss functions in wide use, and the performance was robust to the various noise levels. The improved results in RMSE for the extreme domain (i.e., both tail regions of the distribution) are expected to be utilized for prediction of abnormal events in non-linear complex systems such as natural disaster and financial crisis.
Inventory Support System for Retail Shop

Nowadays having a traditional way recording inventory were very troublesome. Inventory stock is very important when someone run a retail business. If they are not managing inventory well, they will be experiencing losses as if they supply too much, it will risk damaging the stocks and may spending too much storage costs, and vice versa, if the supplies are not sufficient then the customer needs cannot be fulfilled and they will lose profits. Therefore, demand for next period should be estimated, so that customer demand can still be met and they are not bearing the cost of having too many stocks. Shop XYZ is a retail business that sells household daily needs, and they don’t know exact number of items in the warehouse. With these problems, this research aims to create procedures and how to forecast reorder quantity of each selected items.
Meningkatkan Prestasi Belajar Siswa dengan Menerapkan Metode Model Jigsaw Siswa kelas II Sekolah Dasar

The purpose of this study was to improve student learning achievement by applying the Jigsaw Model Method for Class II Semester II students of Public Elementary School 04 Pao, Tanete Rilau District, Baru Regency, 2016/2017 Academic Year. This research was conducted in Class IV SDN No. 04 Paodengan implementation stages include (1) Planning, (2) Implementation, (3) Observation and evaluation, (4) reflection. The results of the analysis of this study obtained an overview of the test results in cycle 1 (one) of learning by applying the jigsaw model not yet showing maximum results. This shows that the results of the first cycle test were 62.88%, while the second cycle test results showed a satisfactory result of 74.62%. Whereas the cycle has shown good learning outcomes with test results of 83.46%. These results indicate that applying the jigsaw model in thematic learning of grade II SDN Pao students can improve student achievement.
Effect of Major Flow Experience on College Life Adjustment and Intention of School Dropout by Chinese Students in Korea

In this study, we examined the impact of major flow experience on college life adjustment, and examined the impact of major flow experience on the intention of school dropout. As a result, the following results were derived.BR First of all, according to the results of an examination of the impact of major flow experience on college life adjustment and the intention of school dropout, the higher the level of perception of major flow experience, the higher the level of awareness of college life adjustment, and the lower the intention of school dropout. Therefore, the university will have to learn to immerse students in their major classes. To be more specific, it is necessary to provide a high level of perceptual education on intrinsic values among job values, to provide support for bilingual classes other than major classes and Korean supplementary classes by individual level, to provide systematic support for academic studies and to provide customized service and to provide institutional procedures to prevent students from dropping out of school.
Disjunctive Delimited Control

Delimited control is a powerful mechanism for programming language extension which has been recently proposed for Prolog (and implemented in SWI-Prolog). By manipulating the control flow of a program from inside the language, it enables the implementation of powerful features, such as tabling, without modifying the internals of the Prolog engine. However, its current formulation is inadequate: it does not capture Prolog’s unique non-deterministic nature which allows multiple ways to satisfy a goal. This paper fully embraces Prolog’s non-determinism with a novel interface for disjunctive delimited control, which gives the programmer not only control over the sequential (conjunctive) control flow, but also over the non-deterministic control flow. We provide a meta-interpreter that conservatively extends Prolog with delimited control and show that it enables a range of typical Prolog features and extensions, now at the library level: findall, cut, branch-and-bound optimisation, probabilistic programming, ...
Statistical Descriptors-Based Image Classification of Textural Images

Texture investigation is a broad field of study with applications extending from remote sensing, satellite communication and autonomous systems to advanced systems such as robotics and machine learning. Textural images define the pattern of pixels spatially arranged, where a specific order can be used to identify and classify images. Human level analysis and classification of textural images have been challenged by current technical advancements with similar level of accuracy. Machines learn better when the images are more distorted and can even spot minute differences in parameters. Efficient classification of images is ensured if the parameter governing the decision is robust and exclusive. Statistical features extracted from textural images are learned through support vector machines, and the learned database is used with testing images to obtain the accuracy of image classification.
Image Encryption Based on Pseudo Hadamard Transformation and Gingerbreadman Chaotic Substitution

In this chapter, a combined Pseudo Hadamard transformation and Gingerbreadman chaotic substitution based algorithm proposed for image encryption. Due to the intrinsic properties of images, such as high inter-pixel redundancy and bulk data capacity, encryption is done in two stages per each round. The correlation between the adjacent elements in the host image is effectively reduced by subjecting it to Pseudo Hadamard transformation and Gingerbreadman chaotic substitution. The initial conditions for Gingerbreadman chaotic generator are considered from 128 bits secrete key. This sequence is introduced in the substitution stage of the encryption process to diffuse the pixel values of cipher image after transformation. The cipher images are subjected to various security analysis and the results obtained are better compared to many existing techniques.
Data Management for Healthcare with a Focus on Privacy and Security for Cancer Patients

Background: Despite the technological advancements in the medical field and patient care, a key area that is lacking for the healthcare sector is on patient privacy and security of the infrastructure enabling and managing patient data in digital format.Numerous security incidents such as ransomware and gross violations of patient security were observed in the healthcare sector, patient privacy must receive more attention from the medical sector.Furthermore, as the severity of the illness increases, it becomes paramount for the patient's privacy to be protected as there are socio-economic impacts on a patient's lifestyle.One such disease that is receiving greater attention and funding is cancer.With cancer-killing, some 8 million patients on an annual basis further research and diagnostics measures can leverage on various data management techniques to improve results accuracy and gain critical insights into the disease.Methods: As such the cervical cancer dataset from "Hospital Universitario de Caracas" in Caracas, Venezuela is used to explore various data cleaning techniques for filling missing values such as global constants, proportion-based filling of missing values and using central tendency measures.Furthermore, as most data in this form of research tends to be skewed, data transformation techniques are also discussed to normalise the data.Another transformation which is applied extensively in this study is the discretisation methods that is used to bin continuous variables to qualitative groupings that are then used for machine learning techniques.Results: As medical data can be extremely large, the Apache Hadoop framework is used to upload the dataset and Optimised Row-Column (ORC) is the most optimal way to store and read data is also demonstrated.Several hypotheses were developed and tested to gain some preliminary insights into cervical cancer.
El uso de las redes sociales de la comunidad académica: Análisis de un servicio creado por una biblioteca universitaria

espanolSe realiza un estudio de las redes sociales de la Biblioteca de la Facultad de Ciencias Sociales (Universidad de la Republica, Uruguay), luego de 6 anos de existencia y publicaciones ininterrumpidas. El objetivo es mejorar la presencia y comunicacion de la Biblioteca en las redes sociales, ademas de transmitir la experiencia a otros servicios de informacion universitarios que puedan encontrar un modelo de trabajo.El estudio se enfoca sobre dos factores: los usuarios, y las publicaciones realizadas. EnglishA study of the social networks of the Library of the Faculty of Social Sciences (University of the Republic, Uruguay) is carried out, after 6 years of existence and uninterrupted publications. The objective is to improve the presence and communication of the Library in social networks, in addition to transmitting the experience to other university information services that can find a working model.The study focuses on two factors: the users and the posts made.
How covid-19 lockdown time was predicted: implications of "collective intelligence" in educational research

espanolLa Covid19 ha supuesto una de las mayores amenazas mundiales desde la segunda guerra mundial. Los modelos formales para estimar su evolucion han sido multiples, pero no se ha encontrado ninguno que estimase el tiempo de confinamiento, lo que podria aportar un dato para gestionar la ansiedad de la ciudadania ante la incertidumbre. Este estudio se planteo hacer una estimacionde los dias de confinamiento en Espana, utilizando la Inteligencia Colectiva. Otros dos objetivos fueron analizar la precision de la estimacion y debatir el potencial del procedimiento para la investigacion en Educacion. Se conto con una participacion de 203 sujetos. Se realizo un analisis descriptivo, estimacion de parametros y un grupo de discusion. Los resultados muestran una estimacion relativamente precisa, con diferencia por edad y genero. Finalmente se debate sobre la capacidad de usar la inteligencia colectiva como recursos docentes, asi como para la investigacion e innovacion educativa. Se comentan sus limitaciones y la necesidad de estudiarse con mas profundidad por parte de la Pedagogia. EnglishCovid19 is one of the biggest global threats since the Second World War. The mathematical models regarding its evolution have been many, although none was found estimating how long confinement would be in Spain. This study aim was to estimate the days of confinement in Spain, using Collective Intelligence. Other both objectives were set out, to analyze the estimation accuracy and to debate about possibilities of Collective Intelligence in Education. A non-probability sample of 203 people was recruited. It was conducted a descriptive analysis, estimation of parameters and a focus group too. The results show a good estimation, with differences by age and gender. Finally, the capacity of Collective Intelligence as teaching resources as well as to research in education is debated.
Алгоритм формирования планов действий по ликвидации чрезвычайных ситуаций

The formation of combat action plans during emergency rescue operations (ASR) is carried out on the basis of an algorithm that combines ontological modeling of the subject area with an algorithm for generating combat action plans.
LibGuides: Library of Things: Tools

A guide to the non-traditional items available for checking out at the Milton Public Library
Logika Fuzzy dengan Metode Mamdani dalam Menentukan Tingkat Peminatan Tipe Motor Honda

Honda motorcycles are in demand by the public as a cheap means of land transportation. CV Hayati is the main motorcycle dealer company in Padang. In carrying out its activities, CV Hayati needs to consider several factors when selling motorcycles that are in demand by consumers. However, CV Hayati still uses manual means in looking at the interest in the motor that will be purchased by consumers. To solve the problem, a system is needed that can help with decision-making by consumers in purchasing motors according to their interests. In this study, the decision to buy a motor that consumers were interested in was done using the fuzzy logic of mamdani method. With the decision-making system in motor interest, it is expected to help and facilitate consumers in determining the motor they are interested in buying. The results of this study can be viewed using the PHP programming language and MySQL database, with the fuzzy logic of the mamdani method. Where in the fuzzyfication process consider several input variables namely: price, oil fuel tank capacity, engine speed, baggage capacity and vehicle weight. So that by defuzzification can be determined the recomedation of motors that are in demand by consumers.
Shifting borders: Invisible, but very real

Today’s borders are no longer necessarily made of bricks and barbed wire. They are increasingly becoming moving barriers that rely on cutting-edge technologies and complex regulations to impose travel restrictions on citizens. The COVID-19 pandemic has further accentuated this phenomenon.
Machine learning approaches for detection of urinary tract infections

Urinary tract infections (UTI) are a common bacterial infection. Diagnosing UTI can be done through urine culturing. While precise, culturing is both time and money consuming. Flow cytometry analys ...
UNDERSTANDING STUDENT EXPECTATION AND SATISFACTION TOWARS ONLINE LEARNING IN THE OPEN UNIVERSITY: A STUDY IN ARCHIPELAGIC AREA IN INDONESIA

The online tutorial is one of the learning methods used by students at the Open University (OP). One main obstacle in delivering online tutorial in OP is the accessibility of the internet, especially for the students living in archipelagic areas in Indonesia. This study aims to analyze the level of student satisfaction towards online tutorial activities at OP, especially for the Distance Learning Unit of OP Ternate that located in an archipelagic area with limited internet facilities. The population was the second-semester students come from various regions in Ternate. Research data were collected by distributing online questionnaires. The sample consisted of 24 respondents. Results showed that, in general, students were satisfied with online tutorial services. The level of student satisfaction towards internet access in the online tutorial was 96.04%, and for the aspect of presenting the learning materials, the satisfaction level was up to 100.77%. These findings indicated that student satisfaction exceeds their expectations. Furthermore, student satisfaction level towards the interaction aspect in the online tutorial was 89.92%. For the class assignment, the student satisfaction level was 97.05%.&#x0D; Keywords: access, assignment, learning material, online tutorial, student satisfaction
Analysis of Blockchain Based Prototype for Electronic Payments

Technological development offers more and more opportunities for people to make electronic payments. From payments via credit / debit cards, then payments via mobile or IoT devices to payments via blockchain technology, they are constantly evolving and becoming simpler, faster, more convenient and more secure. This paper aims to address a number of issues such as the benefits and challenges of payments through blockchain technology. Also, based on the analysis of a developed prototype, the paper will present the sequence of blockchain money transfer operations, the possible states of a transaction, as well as the information that will be processed and stored so that the transfer is successfully completed. The research methodology is qualitative; In this sense, we conducted a research of the resources available at this time and addressing the subject of electronic payments based on blockchain technology and, in parallel, the paper will follow the analysis of the prototype developed so that the results are as objective and consistent with practical use.
Analysis of Rician Noise Restoration Using Fuzzy Membership Function with Median and Trilateral Filter in MRI

Magnetic Resonance (MR) images are exaggerated with noise leads to the limitations in achieving better restoration results. So there is a need for developing efficient restoration algorithms in the medical images to avoid crucial problems for diagnosis and treatment of diseases. In this present work, a trilateral based fuzzy filter is developed for better restoration results. To preserve the structural information, edges trilateral based fuzzy trapezoidal membership function along with median filter has been used. The weights are obtained for the trilateral filter and median filter using fuzzy trapezoidal membership function. The weights are convoluted with the filtered image of the trilateral and median filter. The results obtained using this method are improved and it is analyzed with the simulated images and real images. The results are compared with the median, wiener, trilateral, fuzzy NLM with trapezoidal.
Multi-Authority Ciphertext-Policy Attribute Based Encryption With Accountability

Attribute-based encryption (ABE) is a promising tool for implementing fine-grained access control.To solve the matters of security in single authority, access policy public, not traceable of malicious user,we proposed a scheme of multi-authority. Moreover, multi-authority may bring about the collusion of different authorities.In order to solve these problem,we proposed a scheme of access tree structure with policy hidden and access complex.Once the private key is leaked, our scheme can extract the user ID and find it.If the authorities share their information with each other,the scheme avoid them to combine together to compute the key information and decrypt the ciphertext.Finally,the scheme proved to be secure under selective-set of IND-CPA.
LibGuides: AAS Historical Periodicals Collection: Home

The AAS Historical Periodicals Collection contains thousands of historical periodical titles dating from 1693 to the early Twentieth Century.
METHODOLOGY ASPECTS OF SCIENCE TEACHERS' ENTREPRENEURIAL COMPETENCY DEVELOPMENT: EUROPEAN AND UKRAINIAN PERSPECTIVE

The methodical aspects of the formation of entrepreneurial competence of teachers of Science teachers in the institutions of in-service teacher education are defined in the article. The analysis of theoretical views of Ukrainian and European scientists to the definition of critical concepts are held: “teaching methods”, “traditional methods”, “interactive methods”, “blended learning”, “project method”.Based on the conducted diagnostic the level of awareness of Science teachers related to the development of the entrepreneurship key competency is identified, and educational process of the institutions of in-service teacher education is updated by disseminating the European experience.Methodological bases of teacher training in European educational environ-ment are revealed, and national perspectives, which are based on the principles of variability and interrelation of traditional and interactive methods, which provide the formation of entrepreneurship competency of Science teachers in the system of in-service teacher education are justified. The practical orientation of the use of interactive methods in the professional training of teachers is substantiated, which contributes to the positive dynamics of the formation of entrepreneurial competence of Science teachers.
LibGuides: Graduate Students: Getting Started @ UVic Libraries: Starting your research?

This guide highlights services, resources and tools of interest to graduate students and are available through UVic Libraries Tools, services and resources to support your research journey
Practical exploration of applying mathematical experiment to probability and statistics course

probability and statistics; mathematical experiment; quantile; analysis of variance
High-Frequency Trading

In recent years, concerns have been raised over erratic behavior in increasingly high-speed automated markets. Especially the act of spoofing has been an issue of regulatory concern. Regulators read the incentives of single traders and look for motivations in order to judge whether a crime was committed. High-frequency traders are being charged with accusations of intentionally manipulating the markets to act in their favor. With the rise of high-frequency trading (HFT), financial trades are executed by interacting algorithms acting and responding to one another at a time scale that far surpasses human perception. This chapter develops a sociological approach to HFT taking the social interaction between algorithms into consideration. Based on interviews and ethnographic observations among high-frequency traders, the chapter argues that the use of high-speed algorithms and their interactional features creates a situation where spoofing becomes a fundamental mode of operation and not an immoral act that regulators can attribute to one single individual. This calls for a new critical agenda, which has consequences for how we might understand the regulation of algorithmic finance and the possibility of holding non-intentional actors such as interacting algorithms accountable for their actions.