Reviewerly/exHarmony huggingface dataset:
Colour texture image classification using colour completed local binary pattern (CCLBP)

Local Binary Pattern (LBP) descriptor is being used successfully for the classification of textures. Also, it is been used for other tasks such as facial expression, face recognition and texture segmentation. On the other hand, these descriptors are barely used for image categorization due to their calculations which are depend on the gray image and they are invariant to monotonic light variations on the gray level. Despite the key role in distinctive the objects of these descriptors, they ignore color information. In this project, Completed Local Binary Pattern (CLBP) will be enhanced and two colour CLBP descriptors are proposed which RGB_CCLBP and HSV_CCLBP. Moreover, the datasets that have been used in this project are KTH-TIPS, KTH-TIPS 2A and Outex_TC_00013 datasets. The proposed method shows promising results despite the limitations of it.
A Comparative Study on Crime Analysis Techniques

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Crime analytics and prediction have long been studied among research communities. In the daily life, crime keeps increasing and threatens the lives of the people in public. In this paper, various existing techniques like k-means algorithm, k-nearest neighbor algorithm, Naive Bayes’ theorem, decision trees, random forest, Apriori algorithm, logistic regression, linear regression, SVM and neural networks are summarized.
Young Future Economists’ Perception of Cryptocurrencies and the Motives for their Purchase

Cryptocurrencies represent one of the most innovative financial assets in recent years. The attitude to cryptocurrencies can vary, as well as the motives for their purchase. The aim of the paper is to find out and analyse the perception of young future economists of cryptocurrencies and their motives for purchasing them. For this purpose, an online questionnaire survey was conducted among economists aged 19-35 years in May 2019. A total of 269 responses were obtained and evaluated using descriptive statistics and statistical methods of linear regression and analysis of variance (ANOVA). The results show that 44.98% of respondents perceive cryptocurrencies as speculative assets, 23.79% as a medium of exchange, 21.93% as investment assets and 8.92% as an informal economy instrument. Out of all respondents, 31.22% purchased or intends to purchase cryptocurrencies – most of them for the reason of cryptocurrency value and its future development.
Observations on the Utility of Square Waves and Their Applicability in Generative Melodic Applications

This paper discusses a generative compositional approach within a modular synthesiser system using square waves. With a passing similarity to Schillinger's 'millimetrization' procedure that influenced the composition of Villa-Lobos' New York Skyline, this live presentation will illustrate some potential outputs, suggested adaptations for expressivity and dynamics for realtime performance within this novel form of application. Using square waves as source material offers a number of advantages over other waveforms, chief being fast transition between states. For composers this introduces an aspect of creative randomisation with controlling elements provided by additional modules such as LFOs for smoothing, attenuators, offset generators and quantiser(s) used to constrain voltages to specific pitch sets or tessitura. Processing the summed audio output via a VCA under the control of an alternative CV source acts as a potential 'glueing' factor that also introduces the potential use of Audio Rate Modulation as a part of the available sonics. Using this systems based approach it is possible to generate quite rich and potentially expressive melodies from simple sources. A number of approaches will be demonstrated using a modular synthesiser and Ableton Live. The aim will be to accompany generated melodies with a number of ground bass examples, treating the bass as a secondary melody, as per Schoenberg's observation. The range of examples should enable discussion of the relative merits of this form of application and utility in generatively performed compositions, introducing a sense of creative 'randomness' within a more democratised music composition approach using modular synthesis.
Research Guides: Getting Started at the Library : Welcome!

Learning the basic skills you need to get the most of everything WKU Libraries has to offer!
Evaluation of Brest Cancer Detection Algorithm

In this paper is presented the evaluation of an algorithm for detection of microcalcifications in breast images, allowing us early breast cancer prevention. The Crème Filter is an algorithm with only one parameter “n” that permits to observe the texture dependence when the parameter is changed. The evaluation is done by a cancer expert medical doctor. This algorithm is a new tool for better detecting microcalcifications characteristic of breast cancer.
Research Guides: Frequently Asked Questions: FAQs

How to log on to a library database both on-and off-campus, and some troubleshooting tips if you run into problems.
Design and Construction of a Prototype Device, for Measurement of Environmental Variables in a Unit of Critical Patients, in Health Institutions

The article makes an analysis of how environmental conditions have been studied, and how they affect patients in Critical Patient Units (UPC), in order to determine the importance of these physical variables in health care. A general device model is established as the basis for choosing suitable sensors for the system to be created. And the device for measuring environmental conditions for UPC rooms with the chosen sensors is developed; which contemplates an interface using the Android platform with Bluetooth protocol. For tests, the UPC environment is simulated in a Laboratory to carry out the measurements. The results of the measurements are obtained, and compared with the reference values to determine the accuracy of the device. The project responded to the requirements raised, and will serve as the basis for generating a new feasible marketing team.
PENERAPAN METODE SINGLE MOVING AVERAGE UNTUK PERAMALAN PENJUALAN MAINAN ANAK

Kids toys will never die, but as a shop owner must be observant to see market share soas not to suffer losses, there will be a loss if the rotation of goods is not balanced between thestock of goods with the amount of goods sold. This not only requires reliability and instinct. Itwill be very helpful for shopkeepers if forecasting is done in buying goods for stock, so the stockwill not accumulate much but only buy a few items whose number of turn over is not muchotherwise it will buy lots of goods whose turnover is very fast and many. Forecasting method usedis Single Moving Average by taking orange puzzle sales data from January 2018 to June 2019with a value of 6,7,8 and 9 which will be used as calculation data in the SMA method, determinethe results of historical data processing using the SMA method, calculate errors from the resultsof predictions using the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE(mean absolute percentage error) methods, the final results are obtained after going through theprocess of calculating predictions and calculating errors. The results of this study obtained avalue of movement 9 which has good accuracy (the smallest error rate). Namely MAD of 4.23457,MAPE of 4.2638 and MSE 30.166 with the results of forecasting 96 pcs of orange puzzle sales inJuly 2019.Keywords—Kids Toys, Forecasting, SMA, MAD, MSE, MAPE
Path Planning Study Considering Multicopter Dynamics and Weighted Cost Function

This paper proposes path planning of multicopter. Path planning algorithms include turning radius limitations considering the maneuver of the multicopter and collision determination with obstacles. The turning radius of the multicopter was limited by using a simulator that reflects the dynamics of the multicopter, and the collision decision was determined by the occupancy of the cluster considering the size of the aircraft. In addition, this paper proposes a cost calculation method considering energy consumption and travel distance of multicopter. The algorithm was verified by performing path planning algorithms on virtual maps and maps obtained from actual experiments.
DEVELOPING TRANSLATION QUALITY THROUGH SYNTAX MASTERY OF STUDENTS

This research was aimed at realizing the theory of syntax in improving the student’s competency in translating text from English into Bahasa Indonesia or vice versa. This research focuses on syntactic structure of sentences in the translation of the text. The text that is used to be the source of the data is the text of Asahan Profile 2018, text which is the most currently published by BadanPusatStatistik (BPS) Asahan Regency. the analysis presented in this study is strictly confined to data randomly derived from each of the chapters and the translation assessed by the English students whose performance is good, very good and excellent. The students are of Semester VI FakultasKeguruandanIlmuPendidikan  UNA. Therefore, this paper primarily explores and highlights major issues that explicitly exhibit aspects of syntactic structure. Noticeably, these strategies turn to be fruitful in many cases where the SL system and the TL system slightly diverge while they prove to be fully ungrammatical, odd and even absurd. systems. Key words : Improving, Translation quality, syntax, competency, text
Application of Natural Language Processing Techniques for Classification of Web Published News in Spanish

Web published news written in the Spanish language, were analyzed by using categories that are related to its content, such as: 'Culture', 'Sports' and 'Finances', or they are classified very generally as is the case of 'National' or 'International'.The large content of documents generated the need to provide the user with an analysis of such documents, particularly in circumstances where in search engines are involved.First of all, a pre-process was applied to allow the mining of texts, which includes the lemmatization, homologation of synonyms and representation of documents with a Boolean method.This pre-process also includes a dimensional reduction of the obtained matrix.Secondly, different classification methods were applied to compare their performance in order to find the one that best assigns the category to the news.
What Is Java EE?

At its core, the Java Enterprise Edition (Java EE), formerly known as J2EE, is a collection of abstract, standardized specifications that prescribe solutions to commonly faced challenges in software development.
Regulating Initial Coin Offerings and Cryptocurrencies : A Comparison of Different Approaches in Nine Jurisdictions Worldwide

Initial Coin Offerings (ICOs) and cryptocurrencies are applications of blockchain technology that offer many benefits. ICOs are increasingly used by companies for crowdfunding, allowing start-ups to find investors. Cryptocurrencies allow cheap, fast and straightforward international money transfers. However, along with such benefits also come risks, like volatility of cryptocurrency rates, abuse by (cyber)criminals, and other risks and uncertainties for investors. Governments across the globe are struggling with the question whether and how to regulate cryptocurrencies and ICOs. The technologies and applications are similar in different jurisdictions, but the responses of legislators, regulators and supervisory authorities widely differ. In this article, we investigate the regulatory responses to cryptocurrencies and ICOs in nine jurisdictions worldwide. The aim of investigating different approaches towards regulating cryptocurrencies and ICOs is to identify different approaches, to make a comparison between jurisdictions, and to identify potential good or best practices. The nine jurisdictions that are compared in this paper are Australia, Belgium, China, Estonia, Japan, Switzerland, The Netherlands, the United States, and the European Union. It is concluded that all the jurisdictions investigated do have legislation that is applicable to ICOs and cryptocurrencies. However, big differences exist in the extent to which the legislation applies and is regulated by the national supervising authorities. Generally speaking, most legislation of the investigated jurisdictions consists of financial markets legislation (including that of securities), anti-money laundering legislation, and consumer law. The approaches of the countries investigated differ from a negative, forbidding approach (such as in China, which has launched an ICO-ban and is obstructing trade in cryptocurrencies) to a positive and facilitating approach (such as in Australia and Switzerland, where the aim is to promote innovation). Although this paper does not assess which method of regulation of ICOs and cryptocurrencies can ultimately be qualified as best strategy, we conclude that a positive and facilitating approach offers more opportunities for investors and innovative companies. However, this approach requires a clear and detailed legislative and regulatory framework for all parties involved in the establishment, issuing, storing or trading of cryptocurrencies and ICOs. Such a framework should at least provide boundaries with regard to money laundering and other common forms of cybercrime. Moreover, it should provide some sort of consumer/investor protection and clarity when it comes to tax liability. A legislative and regulatory framework that provides all these aspects will prevent abuse and may enable governments to intervene when issues occur.
INJECT: Algorithms to Discover Creative Angles on News

INJECT is a new digitaltool tosupport journalists to think more creativelywhendiscoveringnewangles on stories under devel-opment. It deliversinteractiveand intelligentsupport embeddedin the text editorsthat journalists work with regularly. This support is generated bycombiningcomplex creative searchesofmillionsof related news storiespublished in multiplelanguageswith entityextraction algorithms and interactive creative guidance tailored to news. This paper reportsthetool’sarchitecture, some itsalgo-rithms, and the design decisions made to delivera reliable and us-able tool for journalistsin different newsroomsand work contexts.
Automatic Alert Generation against Pre-defined Rules-set for Perimetric Security of Sensitive Premises using YOLOv3

Adaptation of artificial intelligence (AI) based solutions at sensitive locations is growing rapidly. Use of these techniques along with surveillance cameras has become a primary requirement of smart cities to convert data into intelligible information. Consequently, these solutions are minimizing the effort of training and reliance on human resource. In this research paper, we have devised use cases of object detection with focus on human and luggage detection in real-time using a Convolutional Neural Network (CNN) technique YOLOv3. This technique, trained on MS-COCO dataset, has not been able to produce desirable results when tested on images from subcontinent region containing luggage, human or both. As a case study, we enhanced MS-COCO dataset by incorporating our own collection of realistic images. The study is carried out on a commodity hardware to strengthen our claim to use technology over humans. The proposed solution is developed for analysing video streams in real time against a predefined rules-set. Idea to automate the process of surveillance at strategic locations without human intervention opens a new window of research for literary community to develop cost effective solutions.
An Association Rule Mining Algorithm for Gas Monitoring Database

A new association rule mining algorithm is studied, which is based on gas monitoring database. In order to calculate itemsets support, this paper puts forward the concept of database characteristic matrix and characteristic vector, and leads to algorithm for mining association rules based on the characteristic matrix. This algorithm needs to traverse the database one time only, and the database operation has been reduced. Based on the characteristic vector inner product to get an Item set support, the efficiency of the algorithm has been improved. It improves the efficiency of association rules mining in mass gas databases.
Continuous User Understanding in Software Evolution

This dissertation introduces CuuSE, a framework for continuous user understanding to extract and utilize tacit knowledge of users. CuuSE collects user feedback through kits and maps the feedback to features using a new concept called feature crumbs. A reference platform for user feedback visualization demonstrates the feasibility and extensibility of CuuSE. The results of three empirical validation cycles indicate that CuuSE enables developers to understand tacit knowledge of users.
A Comparative Study On Apriori And Reverse Apriori In Generation Of Frequent Item Set

Weblog Mining is also termed as Web Usage Mining, and it is considered as a branch of Data Mining. Web usage mining includes the approach of extracting usage behaviour of the customers in Web Applications. The obtained data can be utilized in many ways such as, checking of fraudulent elements, improvement of the application, etc. The presented research work, focuses on Web Usage Mining (WUM), and it mainly consist of three steps pre- processing of the information, knowledge exploration, and finally Pattern analysis, task involves Association Rule Mining, Statistical Analysis, Clustering, Classification and identification of Sequential Patterns. Apriori is most commonly used algorithm in terms of data mining for extracting usage behaviour. The proposed algorithm, Reverse Apriori is the combination of FP-tree structure and Apriori algorithm. Comparative study is made by considering the running time in the generation of frequent set.
Hegemonic and Inclusive Masculinities in American and CzechYouTube Vlogs

This paper examines discursive means of the construction ofhegemonic and inclusive masculinities in American and CzechYouTube vlogs. The vlog is a relatively young new media genre,which has developed primarily in the context of YouTube (thesecond most visited website and the second highest rankingsocial media platform in the world) and is a curious object ofstudy as a platform of identity construction. The two types ofmasculine identities are analyzed in terms of two-levelindexicality realized by means of positioning of self andothers in the context of YouTube vlogs. The author outlines themost salient strategies of performing the two types ofmasculinities and shows how differently they are employed byyoung male Americans and Czechs. The extent and means ofconstructing these gender identities by Czech and AmericanYouTubers diverge: both masculinities were constructed moreprominently in American vlogs, whereas Czech YouTubersexhibited more neutral patterns. Moreover, American vloggersproved to resort to inclusive masculinity considerably morefrequently that their Czech counterparts and tended toco-construct the two masculinities in such a way that theyappear blended (for instance, by clustering affectionatehomosociality with address terms indexical of cool solidarity).In Czech YouTube vlogs, on the other hand, the instances ofinclusive masculinity were significantly less abundant and moreisolated, which is in line with a less intensive constructionof masculine identity in Czech vlogs overall.
UTILIZATION OF EDMODO AS PART OF OPTIMIZATION LEARNING IN NETWORK

Edmodo is a form of learning application used by academics in order to carry out online learning. This research was conducted with the aim of informing more widely about Edmodo and describing how to use it to be able to optimize online learning. The research method used is a qualitative research method with a type of literature study. The results of the research conducted are that Edmodo has provided complete facilities in the context of implementing virtual learning. Intended as virtual learning because the learning process is carried out in the network. Educators (teachers / lecturers) have breadth in the implementation of classroom management because students can be made classes as classes in conventional learning. Educators can optimize the dissemination of information as well as in applications that have provided many features to make tests with various types of tests offered, educators can also provide assignments with predetermined collection times so as to optimize classroom management, educators can provide a direct assessment of the assignments students, giving awards with various types of awards. The advantage of using Edmodo is that there is no limit on space and time in learning because at any time it can take part in learning, it does not depend on the presence of educators in front of the class because wherever educators can still provide optimal learning.
Embedded Implementation of Human Detection Using Only Color Features on the NVIDIA Xavier

The authors are developing a novel vital sensing system for real-time exercises using image-assisted routing (IAR), which requires the accurate detection of humans wearing sensor nodes in aerial images captured by a camera mounted on an UAV for sensor localization. To achieve real-time human detection for IAR, this paper proposes the embedded implementation of an accurate human detection scheme, which is constructed with informed filters using only color features and can achieve higher accuracy than deep learning for sports scenes. The experimental results for the actual aerial images of a sports scene revealed that our implementation on the NVIDIA Jetson Xavier board could process a 3840×2160 imaae in annroximatelv 44.93 ms.
Image Quilting for Histological Image Synthesis

Applications in digital histopathology often require costly expert labels to train modern machine learning algorithms. We introduce an adaptation of the Image Quilting algorithm for texture synthesis that is utilized to virtually multiply the tissues and labels. Potential applications are augmentation in neural network training and quality control in intra-rater experiments. We evaluate this method in a subjective expert trial and a quantitative augmented learning scenario.
Model-based learning of co-sparse representations for image processing applications

In this thesis, approaches for unsupervised learning of image representations are developed with the goal of reducing training complexity over fully learned and improving accuracy over expert-crafted models. To that end, application-specific knowledge of image formation is incorporated into sparsity-based, unsupervised learning models for low-level image representations. The designed numerical algorithms achieve state-of-the-art results in uni- and multi-modal image reconstruction, alignment, and segmentation benchmarks.
Neural Network for Analyzing Prostate Cancer Tissue Microarrays

Prostate cancer (PCa) is the dominating malignant tumor for men worldwide and across all ethnic groups. If a carcinoma is being suspected, e.g. due to blood levels, trans-rectal punch biopsies of the prostate will be accomplished, while in case of higher stages of the disease the complete prostate is being surgically removed (radical prostatectomy). In both cases prostate tissue will be prepared into histological sections on glass microscope slides according to certain laboratory protocols, and is finally microscopically inspected by a trained histopathologist. Even though this method is well established, it can lead to various problems because of objectivity deficiencies. In this paper, we present a proof of concept of using Artificial Neural Networks (ANN) for automatically analyzing prostate cancer tissue and rating its malignancy using tissue microarrays (TMAs) of sampled benign and malignant tissue.