Reviewerly/exHarmony huggingface dataset:
IFOA: an improved forest algorithm for continuous nonlinear optimisation

Forest optimisation algorithm (FOA) is a new evolutionary optimisation algorithm which is inspired by seed dispersal procedure in the forests, suitable for continuous nonlinear optimisation problems. In this paper, an improved forest optimisation algorithm (IFOA) is introduced to improve convergence speed and the accuracy of FOA, and four improvement strategies which include the greedy strategy, waveform step, preferential treatment of best tree and new-type global seeding are proposed to solve continuous nonlinear optimisation problems better. The capability of IFOA has been investigated through the performance of several experiments on well-known test problems and the results prove that IFOA is able to perform global optimisation effectively with high accuracy and convergence speed.
Character Relationship Extraction Method Based on BiLSTM

In the traditional character relationship extraction methods, there are problems about feature selection such as difficulty and incompleteness. In order to solve this problem, this paper proposes a character relationship extraction method based on BiLSTM (CRE-BiLSTM) to improve the accuracy of the classification of character relationship. Firstly, use Sogou Lab’s Chinese news corpus to train the Word2vec model and complete conversion of interactive encyclopedia dataset into word vectors. Then, more complete text context information is obtained through the bidirectional LSTM neural network which extract the depth feature word vectors. Finally, the Softmax function classifier is used to classify the character relationship and complete the extraction of five common character relationships. The experimental results show that compared with the traditional character relationship extraction methods, the CRE-BiLSTM model performed much better in relation extraction.
A Look at my Blogging in 2018

Inspired by Chris Aldrich's post on his 2018 review of blogging I decided to take a quick peak and see what my productivity looked like
Governance of Social Networks in the educational context analyzed through Power BI: Case Study Students belonging to the Pio Jaramillo Alvarado Institution

This investigation aims to study the skills, creativity, interactivity, work team, individual and colaborative learning, and also to distinguish distractions, additions, data and information vulnerability among others itens. Social networks currently play a leading role; through its use it allows the interaction between family members, acquaintances and friends, becoming one of the strongest pillars when it comes to communication. Likewise it should be considered that its excessive use leads to problems such as: addiction, involuntary loss of control, family separation, unemployment, eating disorders, low school grades in the case of adolescents, among others, therefore facing these concerns is meritorious through this research to know through a structural analysis the influence and governance of social media in the adolescents of Pio Jaramillo Alvarado; With the results obtained, short, medium and long term solutions are proposed to raise awareness of the appropriate use; the research procedure is governed by four sequential scenarios; It begins with: the collection of information to identify central problems of attention, then surveys are applied to students and parents; Subsequently, the tabulation of data is performed and based on the results, the ideal solutions for its use are proposed. The research contribution is subject to the use of tools and techniques of data analysis, considering the first the use of surveys and secondly, the analysis of data through Power BI software.
Design of Reversible Hardware BinDCT

Recently, reversible logic computation has attracted researchers’ attention for implementing low-power digital logic designs. In fact, no information is wasted in this approach, i.e., it performs a bijective function. This chapter introduces a hardware design of reversible BinDCT. It is a new proposal in reversible approach. In this study, we dealt with a variety of sub-modules, which have a better performance in terms of constant inputs (CIs), garbage output (GO), power and quantum cost (QC) as well as the delay than that of existing designs. This work can offer a vital step in the design of reversible designs for in the field of image processing. It could also be present as an essential step in this area since the image processing systems are known to be the biggest energy consumers.
Evaluation of image comparison methods for complex textures

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Evaluation of image comparison methods for complex texturesAuthors: Rodrigo S. FerreiraJulia NoceMarco FerrazMatheus OliveiraEmilio Vita BrazilSérgio CersosimoRenato CerqueiraRodrigo S. FerreiraIBM ResearchSearch for more papers by this author, Julia NoceIBM ResearchSearch for more papers by this author, Marco FerrazGalpSearch for more papers by this author, Matheus OliveiraIBM ResearchSearch for more papers by this author, Emilio Vita BrazilIBM ResearchSearch for more papers by this author, Sérgio CersosimoGalpSearch for more papers by this author, and Renato CerqueiraIBM ResearchSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216292.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn the O&G industry, interpreters may be assisted by image retrieval techniques when searching for relevant geological features in a seismic dataset, such as image patterns and structures. This can greatly accelerate the interpretation process by enabling the interpreter to relate the observed features with potential analogs from other interpretation projects. In this paper we evaluate the performance of different image comparison methods for seismic image retrieval. Most methodologies proposed in the literature assume that query and database images have comparable sizes. Our analysis includes methods that are able to compare images with arbitrarily different sizes which not only makes the retrieval application more general but also allows to answer queries not addressed by usual methods such as within, contains and resembles. The results in a public seismic dataset show a gain of up to 180% for [email protected] in comparison to traditional methods.Presentation Date: Wednesday, September 18, 2019Session Start Time: 1:50 PMPresentation Time: 3:55 PMLocation: Poster Station 3Presentation Type: PosterKeywords: machine learning, algorithm, interpretationPermalink: https://doi.org/10.1190/segam2019-3216292.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Rodrigo S. Ferreira, Julia Noce, Marco Ferraz, Matheus Oliveira, Emilio Vita Brazil, Sérgio Cersosimo, and Renato Cerqueira, (2019), "Evaluation of image comparison methods for complex textures," SEG Technical Program Expanded Abstracts : 2639-2643. https://doi.org/10.1190/segam2019-3216292.1 Plain-Language Summary Keywordsmachine learningalgorithminterpretationPDF DownloadLoading ...
A Brief Conceptual View on Classification Using Support Vector Machine

Abstract Classifier is an important element for a classification process to classify features or samples of a dataset successfully. The existence of classifier is important, especially involved multiple class dataset with huge number of features or samples. Support Vector Machine (SVM) classifier catches more attention over other classifiers as it is widely used in various fields. Therefore, this paper aims to give a brief conceptual view of SVM in classification. The important elements and principle of SVM are also described. Along with that an appropriate evaluation and validation for SVM performance are also included. As a result, SVM concept in solving real world classification problem is identified for future reference.
Travel Your Desk? An Office Desk Substitution and its Effects on Cybersickness, Presence and Performance in an HMD-based Exploratory Analysis Task

In this work, we evaluate the feasibility of an office desk substitution in the context of a visual data analysis task involving travel. We measure the impact on cybersickness as well as the general task performance and presence. In the conducted user study $(n=52)$ , surprisingly, and partially in contradiction to existing work, we found no significant differences for those core measures between the control condition without a virtual table and the condition containing a virtual table.
SOM Library: M-I Students: M-I Students Home

This guide provides links to e-books, study skills resources, and Step 1 resources from the School of Medicine Library and beyond.
On Berlekamp–Massey and Berlekamp–Massey–Sakata Algorithms

The Berlekamp–Massey and Berlekamp–Massey–Sakata algorithms compute a minimal polynomial or polynomial set of a linearly recurring sequence or multi-dimensional array. In this paper some underlying properties of and connections between these two algorithms are clarified theoretically: a unified flow chart for both algorithms is proposed to reveal their connections; the polynomials these two algorithms maintain at each iteration are proved to be reciprocal when both algorithms are applied to the same sequence; and the uniqueness of the choices of polynomials from two critical polynomial sets in the Berlekamp–Massey–Sakata algorithm is investigated.
The Teacher’s Method in Teaching Vocabulary: a Case Study in Second Grade of SMP Muhammadiyah 1 Kartasura

This research aims to describe the teacher methods used by the English teacher in teaching vocabulary, the implementation teacher method in the classroom, and identify the students responses toward the teacher method in teaching vocabulary. The method used in this research is descriptive qualitative research, the technique for collecting data through observation, interview, and documentation. This research used theory from Richards and Rodgers (1986) as reference on finding and analyzing the methods in teaching English vocabulary.The subject of this research is teacher and students. Based on the data analysis, the result of this shows that the teacher use 3 methods in teaching vocabulary, that is Grammar Translation Method, Direct Method, and Total Physical Response. In the implementation in teaching learning process, the steps are identifying the words in the text, Memorizing Vocabulary, Forming sentences with the new word, Practicing Fill-in-the-blank exercise, and Writing compositions from a given topic. The other ones are Reading Aloud, Question and Answer Exercise, Getting Students to Self-Correct, Conversation Practice, and Paragraph Writing. And the last is Action Song. The students responses toward implementation the method that teacher used is good and positively, because during the teaching learning process, the students can enjoy and understand fluently.
The Fast Digital Algorithm for Measuring the Parameters of the Random Processes

In this paper, there are considered the digital algorithm and the device for measuring the first and second one-dimensional moments of the random processes. One of the fundamental advantages of our approach is its simple hardware implementation that is possible, for example, by means of the modern FPGAs and requires minimal number of the arithmetic operations. Another is a high accuracy that is provides in the measurements of the mean value and the variance of the random process in real time that are performed by the observed discrete realization from the output of the analog-to-digital converter. By means of the statistical simulation methods, the estimates of the measured parameters of the Gaussian and Nakagami processes are obtained and the high efficiency of the introduced measurer is established.
Fundamentals of Collection Development and Management (4th ed.) / Peggy Johnson [reseña bibliográfica]

Review of the book Fundamentals of Collection Development and Management (4th ed.) de Peggy Johnson.
SOM Library: M-II Students: M-II Students Home

This guide provides links to e-books, study skills resources, and Step 1 resources from the School of Medicine Library and beyond.
Machine Reading of Clinical Notes for Automated ICD Coding

The International Classification of Diseases (ICD) is a healthcare classification system maintained by the World Health Organization. It provides a set of diagnostic codes for classifying diseases (including a wide range of signs, symptoms and external causes of injury or disease) and surgical procedures. ICD codes are used for a number of different tasks, such as reporting health conditions and carrying out medical billing. The standard coding procedure consists in assigning one or more ICD codes to a patient's hospital visit: this operation is performed by medical coders, who read and review the clinical notes written by physicians and then assign the appropriate ICD codes according to the coding guidelines. This process can be time consuming and error-prone. With the rising popularity of electronic health records (EHRs) systems for the automated reading of the clinical notes and codes assignment have been proposed in the recent years. In the present work we review different supervised learning approaches for automated ICD coding and we propose our model, based on a convolutional neural network with attention layers, which achieves state of the art results. The experiments are performed on the publicly available MIMIC-III dataset, which contains de-identified EHRs of 58,976 patient visits at the Beth Israel Deaconess Medical Center from 2001 to 2012.
Artificial Intelligence to Assess Hormonal Status of Breast Cancer Patients

The pathological review of tumor samples, even for common molecular biomarkers such as estrogen receptor (ER), progesterone receptor (PR), and human
Validating multilingual hybrid automatic term extraction for search engine optimisation : the use case of EBM-GUIDELINES

Tools that automatically extract terms and their equivalents in other languages from parallel corpora can contribute to multilingual professional communication in more than one way. By means of a use case with data from a medical web site with point of care evidence summaries (Ebpracticenet), we illustrate how hybrid multilingual automatic term extraction from parallel corpora works and how it can be used in a practical application such as search engine optimisation. The original aim was to use the result of the extraction to improve the recall of a search engine by allowing automated multilingual searches. Two additional possible applications were found while considering the data: searching via related forms and searching via strongly semantically related words. The second stage of this research was to find the most suitable format for the required manual validation of the raw extraction results and to compare the validation process when performed by a domain expert versus a terminologist.
Improvements to the Massive Unsupervised Outlier Detection (MUOD) Algorithm

We present improvements to the Massive Unsupervised Outlier Detection (MUOD) algorithm, a scalable and unsupervised outlier detection method, especially useful for identifying outliers for functional data. MUOD identifies different types of outliers in samples of curves including shape, magnitude and amplitude outliers. This is done by computing for each curve three indices, which measure outlyingness in terms of shape, magnitude and amplitude relative to the other curves. These indices are then sorted and observations with extremely high indices are labelled as outliers. To further improve the scalability MUOD, we introduce ``fastMUOD, a fast implementation of MUOD which uses the component-wise or the $L_1-$median in the computation of the indices instead of using the whole observation. We also present ``semi-fastMUOD, which uses a sample of the observations in the computation of the indices. As further improvements to MUOD, we discuss a new method for identifying extreme indices which entails the use of a classical boxplot or its adjusted version for skewed distributions. We analyse the performance of the proposed improvements using real and simulated data, and show that outlier detection accuracy is not compromised even with the gains in scalability.
Simplicity, quality and visibility as key drivers to build SINUS system

Universities and research institutes in Poland use digital libraries, repositories, bibliographic databases or Current Research Information Systems to promote or give access to their scientific achievements. In 2015, the Poznan Supercomputing and Networking Center started cooperation with the Library of the Poznan University of Technology to optimise the process of managing the scientific achievements of the Technical University. The envisioned solution was to replace four legacy systems used at the Technical University, provide comprehensive information for end users, automate the work of collecting and sharing resources, and support flexible data export. In course of the work we executed in-depth analysis of existing software solutions and as a result decided to develop a new system adjusted to the needs of the Technical University. Our approach in the design & development work was to deliver system which is simple to use, has built-in features to improve/ensure quality of provided data and has good visibility in search engines. After several years of work we have strong believe that detailed analysis of the needs, flexible solution as well as simplicity, quality and visibility are the most important factors of success when building comprehensive solution for promotion and access to academic research information/outcomes.
Can Machines Learn to Pick Horizons in Post Stack Data?

Abstract Summary The presented method applies a supervised deep learning (DL) method to detect the horizons throughout a seismic dataset with high detection accuracy.
Design of B2B E-commerce Platform Based on SOA Architecture

Abstract With the rapid development of information technology and computer networks, internnet-based e-commerce has also emerged, and has achieved tremendous development in recent years. Most enterprise systems are developed based on heterogeneous platforms and different information standards. The interaction and integration between enterprises is very difficult, and the traditional distributed architecture can not solve the current needs. Service-oriented architecture is a new generation of distributed software architecture that solves the above problems. As a new generation of distributed software architecture, it not only improves the reusability of software, but also achieves good scalability. This paper uses the SOA architecture to design the e-commerce platform, and designs the interface service to realize the service composition of the e-commerce platform.
ENGAGING UNIVERSITY STUDENTS IN READING PROJECTS TO ENHANCE THEIR LEARNING

In Higher Education students encounter large reading materials which require them to be independent readers. Therefore, this makes reading an important academic activity at higher education institutions. Students in this sector engage with different texts and books in their academic years, primarily to acquire information and knowledge. However, they appear not to read other materials to gain insight and for pleasure. Engaging students in reading activities is of importance, hence, reading projects are essential to enhance their reading habits. The research was conducted at University of Technology, to investigate the role of reading projects at tertiary level regarding students learning and reading ability. The study used a qualitative method design to explore the students’ engagement in reading projects as to enhance their learning at the Central University of Technology, Free State. Focus group interviews were conducted with Reading Club members involved in reading projects. The findings of this study revealed that the reading projects made reading a shared pleasure, team effort and encouraged metacognitive focus upon learning. The study further provided insight into the role reading projects play in enhancing learning and promoting academic success.
MultiPrime cloud-RSA: a fast homomorphic encryption scheme for data confidentiality protection in clouds

Concerns over data confidentiality are amongst the main barriers limiting the widespread adoption of cloud solutions. Indeed, scientists have invented a new promising form of encryption, homomorphic encryption (HE), which can be considered as a powerful tool to get over these concerns. Since cloud environments are more vulnerable to attacks and since cloud consumers frequently access to cloud computing services using light-weight devices, the HE schemes must be promoted, in terms of security level and running time, to operate efficiently. In El Makkaoui et al. (2017), we boosted the normal RSA cryptosystem at security level, cloud-RSA. In this paper, we suggest a new fast variant of the Cloud-RSA scheme, MultiPime cloud-RSA, to accelerate its decryption process. The variant uses a modulus of the form n = Πk i=1 pi for k ≥ 2 and employs the Chinese remaindering to decrypt. Theoretical and simulation results show that the new variant offers a large decryption speed-up over the cloud-RSA scheme whilst maintaining a recommended security level.
The Future of Cataloging (for the Rest of Us): Forecasting for Production-Level Cataloging

For decades, catalogers have lived in a place of change. We’ve been in a nearly continual shift ever since library catalogs moved to digital environments, and before that as concepts and new formats emerged. In the past, many of these changes have taken years or decades to implement locally, let alone implement on a national or global scale. Our era in cataloging is no different. The implementation of RDA, the shift away from the MARC record, and increasing interest in linked data (among myriad other factors) are beginning to coalesce into a discernible future. This is an exceptional time when we can begin to imagine a practical future for the catalogers, copy catalogers, and staff-who-occasionally-catalog who might not always be privy to the inner workings of committee meetings, task forces, or academic conferences.
Future Frame Prediction with Generative Adversarial Networks

This report is about using generative adversarial networks with predictive coding networks for future frame prediction. Model selection choices for the components of the network are explored by training different models and testing their performance on next frame prediction in digital video from driving scenarios. Benefits and issues of using adversarial loss for future frame prediction as well as different choices for the model are discussed.