Reviewerly/exHarmony huggingface dataset:
Blockchain and Cryptocurrency: International Legal and Regulatory Challenges – Book Review

Book review on publication of 2019 book Blockchain and Cryptocurrency: International Legal and Regulatory Challenges
Alfabetización informacional y el rol del bibliotecario transformativo para enfrentar la desinformación en tiempos de crisis

espanolEn este articulo se analizan los fenomenos de infodemia y desinformacion en el contexto de la actual crisis sanitaria. Ademas, se visibilizan los riesgos de la posverdad y las noticias falsas en el area de la salud y la perdida de valor que ha sufrido la ciencia dentro de la sociedad debido a las mismas. Se posiciona a los bibliotecarios como profesionales indispensables para contrarrestar los peligros de la desinformacion en la sociedad actual y a los programas de alfabetizacion informacional como herramientas perfectas en la ensenanza y desarrollo del pensamiento critico. A su vez, se rescata la importancia del trabajo interdisciplinario para lograr con exito los objetivos planteados. EnglishThe article analyses phenomena related to infodemic and misinformation in the context of the current health crisis. In addition, the article raises awareness of the risks caused by post-truth and fake news in the healthcare area and the constant loss of value that science has experienced in our society due to these ills. The article considers librarians as indispensable professionals to counteract the perils raised by misinformation in our current society and deems information literacy programmes as ideal tools for teaching and developing critical thinking. At the same time, the article emphasises the significance of interdisciplinary work in order to achieve the goals proposed in it.
Augmenting analytics : Situated Data Visualization towards decision-making for EMC testing

The present work proposes the use of information visualization techniques allied to an Augmented Reality user interface to provide in-formation that helps professionals to analyse data, however spa ...
Blasphemy as transgressive speech, a natural history

This chapter examines the concept of blasphemy as transgressive speech, which involves human attempts to control an informational ecosystem, in terms of both speech acts and communications. Blasphemy must be understood in the broader context of human beings as biological organisms using evolved communicative systems.
Estimation of the energy contributions of a hydroelectric power station using space parameter exploration

Abstract From the study of nonlinear dynamics, the study of chaotic dynamics in deterministic systems has been developed. If a system is deterministic, its behavior would be easy to predict. However, there are systems in which their behavior seems to be unpredictable due to the complexity of the dynamics of the system, which is reflected in an aperiodic behavior. This article presents the application of parameter exploration techniques for the study of aperiodic deterministic systems using time constraint estimation through the calculation of the Lyapunov exponent in order to estimate future system values. This technique is developed on a part of the energy matrix of the Colombian system, especially with hydroelectric production.
Application of text analytics for identification of respiratory diseases in medical claims

<b>Introduction:</b> Natural language processing algorithms are helping in unraveling valuable clinical data in health records. Such analytics has immense potential to aid in respiratory clinical services. <b>Aims &amp; objectives:</b> To determine whether text analytics in unstructured hospital admission records extracts useful health information in respiratory diseases (RD). <b>Methods:</b> Text analytics were applied on large, unstructured medical claim records of patients between 50-90 years in a health scheme to identify RD. Disease categories and respiratory subcategories were created by extensive data wrangling, building custom dictionaries, using regular expressions (Regex) base package of R Language, tm &amp; Rweka libraries and unigram &amp; bigram tokenizations. <b>Results:</b> Among 179,624 claims of 5 years 60,519 indoor admissions were identified. RD appeared in 6776 (11.19%) admissions, which include COPD (1877, 27.7%), lower respiratory tract infections(LRTI) (1938, 28.6%), asthma (838, 12%), lung cancer (745, 11%), pneumonia (429, 6%), interstitial lung disease(ILD) (241, 3.6%), tuberculosis (187, 2.8%), pulmonary hypertension(72, 1.3%). RD accounted for 13.4% of overall expenditure. <b>Conclusion:</b> Text analytics can reveal unknown patterns and extract vital information in RD such as burden of lung disease. Policy makers can utilize these insights to devise targeted preventive &amp; therapeutic interventions and optimize resources towards better management of RD.
The Linked Data Enterprise as Enabler for both Intra- and Inter-organizational Business Data Integration and Usage

Information silos and conceptual heterogeneity within organizations remain to be main problems towards an effective utilization of available information resources. Since decades, proper data and knowledge-integration and extraction remain to be giant tasks in the information systems arena. This paper briefly introduces the Linked-Data Concept as a possible approach for easing this problem. Linked Data can be utilized on the one hand within organizations (“intra-enterprise”) for the integration of possible existing heterogeneous systems and on the other hand as enabler of cross-organizational data integration (“interenterprise”) in a networked economy. The “Linked Data Enterprise” unifies both concepts of intra- and inter-enterprise networks as a very flexible and extendible instrument of data integration. Special emphasis is laid on the applicability of the FAIR Guiding Principles for data management and stewardship that was originally introduced for scientific data.
LibGuides: Researching Majors and Careers - Winter Park Campus: Home

A guide designed to help SLS students on the Winter Park Campus research careers, majors, and 4-year colleges.
Supply Chain Powered by AI and Blockchain

Abstract It is a common knowledge that the main goal of companies is to supply goods and services to buyers at maximum price for minimum cost. For companies such as energy producers, with complex and dynamic supply chains, this often causes cost control issues. Previously, business models based on VI (Vertical Integration) or outsourcing could mitigate cost increases, but these models have limits. A new paradigm is proposed: use of distributed ledger technology in the form of a blockchain-based supply chain integration solution. By permitting participants in a supply chain to integrate information flows in a secure, quick and automated levels of efficiency that are achieved and cannot be reached by other technologies and do not require participants to change their IT platforms. The main purpose for the creation and management of the "supply chain" is for the seller to provide the buyer the maximum value – whether of a product or service – at the lowest cost. Here is a depiction of the "links" in the general supply chain for energy production.
Which Minimizer Does My Neural Network Converge To

The loss surface of an overparameterized neural network (NN) possesses many global minima of zero training error. We explain how common variants of the standard NN training procedure change the minimizer obtained. First, we make explicit how the size of the initialization of a strongly overparameterized NN affects the minimizer and can deteriorate its final test performance. We propose a strategy to limit this effect. Then, we demonstrate that for adaptive optimization such as AdaGrad, the obtained minimizer generally differs from the gradient descent (GD) minimizer. This adaptive minimizer is changed further by stochastic mini-batch training, even though in the non-adaptive case GD and stochastic GD result in essentially the same minimizer. Lastly, we explain that these effects remain relevant for less overparameterized NNs. While overparameterization has its benefits, our work highlights that it induces sources of error absent from underparameterized models, some of which can be challenging to control.
فعالیة استخدام الرموز الثابتة والمتحرکة فی القصص الرقمیة ثنائیة اللغـة على تحسـین الفهم القرائی الحرفی المباشر لدى الطالبات الصم The Effectiveness of Using Static and Dynamic Symbols in digital bilingual storybook reading app to Improve the literal Reading Comprehension of Deaf Students

يناضل العاملون في ميدان تعليم الصم للتغلب على التحديات التي تواجههم في تعليم مهارات القراءة للتلاميذ الصم؛ نظرا لما يسببه فقدانهم السمع من تأثير سلبي على نموهم اللغوي، فجاءت هذه الدراسة للتعرف على فعالية استخدام استراتيجية الرموز الثابتة والمتحرکة في القصص الرقمية ثنائية اللغة على تحسين الفهم القرائي الحرفي المباشر على الطالبات الصم، استخدمت الباحثة أحد أشکال تصميم الحالة الواحدة (SCD) وهو تصميم التقصي المتعدد مع ثلاث طالبات، تم جمع البيانات حول سلوک الإجابة عن أسئلة اختبار الفهم القرائي باستخدام أداتي (الاختبار والاستبانة)، عبر ثلاث مراحل وهي: مرحلة الخط القاعدي، التدخل، التعميم.وشملت عينة الدراسة ثلاث طالبات تتراوح أعمارهن ما بين (12-10)سنة، ويبلغ متوسط درجة ذکائهن ما بين (95-90)، وجميعهن من أباء سامعين، وجميعهن يستخدمن السماعات الطبية، ويستخدمن لغة الإشارة کطريقة أساسية للتواصل، ويتلقين الخدمات التربوية في نفس المعهد -معهد الأمل للبنات الصم- بمدينة الدمام، وأشارت النتائج إلى تحسن في مهارات الفهم القرائي لدى أفراد العينة. کما أشارت نتائج الصدق الاجتماعي إلى تقبل عال للتدخل المقدم، وخلصت الدراسة إلى أن استخدام الرموز الثابتة والمتحرکة في القصص الرقمية ثنائية اللغة ذات فعالية في زيادة الفهم القرائي للتلاميذ الصم.It’s obvious that workers in the field of deaf learning are struggling to overcome challenges facing them in teaching reading skills to deaf students, because of the negative impact of hearing loss has on their language development. This study aimed to recognize the effectiveness of using the strategy of static and dynamic symbols in bilingual digital stories on improving the direct literal reading comprehension of deaf students. The researcher used one type of single case design tool (SCD) which is the multiple probe design across participants on three students. Data on behavior of answers to the Reading Comprehension Test questions were collected using two tools (test and questionnaire), through three phases: baseline, intervention, and generalization.The study sample involved three students with ages between (12-10) years, their intelligence degree average ranges between (90-95), all of them are born to hearing parents, and all of them use hearing aids and use sign language as a primary method of communication, and receive their educational services from the same institute -Al-Amal institute for Deaf Girls- in Dammam. The results of the study reveal an improvement in the reading comprehension skills of the sample members, the results of social validity also indicated a high acceptance of the intervention presented. The study concluded that the use of static and dynamic symbols in digital bilingual stories is effective in increasing the reading comprehension of deaf students.
Survey of Digital Tools for the Generation of Ideas

Abstract During the concept phase of the industrial design process drawings are used to represent designer’s ideas. More specifically, the designer’s goal is to put the characteristics of ideas on paper so that they can later act as pivotal points in the development of a project. Sketching is also the ideal tool to continue developing an idea: because it is imprecise, the sketch guarantees a high degree of freedom, allowing for changes to made and new ideas to be added. Another possibility is to translate ideas into sketches on computer tools. This approach can allow the designer to use the created 3D model as the basis for further developing ideas. At the present moment, however, this type of solution is not extensively used by designers during the concept phase. Some researchers have identified technical problems as the reason why these instruments have been unsuccessful on the market, while for others this is related to systems still too rigid to be adapted to the often-diverse needs of designers. The research presented in this position paper aims at analyzing what has so far been understood with respect to the process of generating ideas, their initial representation in the concept phase and the tools that have been developed so far to support this phase. Consequently, a discussion on these themes and some hypotheses from which develop new research lines will be presented.
THE INSTRUCTIONAL MATERIAL OF TEACHING ENGLISH IN MI PSM GAMBYOK: A STUDY ON THE INSTRUCTIONAL DESIGN

AbstractTeaching English for the students in the elementary level should be meaningful and fun. By learning in a great atmosphere, the students are expected to understand well the materials. Most of the elementary schools in east java put English as the lesson, start from the first grade. A good decision but in the application, most English teachers don‘t use the proper method. This condition makes the learning process in vain. Condition stated is also occurs in MI PSM Gambyok. The research describes this condition using the instructional design point of view in the descriptive research. The school has problem with the teaching method which shows by the score in daily test are good (up to 7) but centered-test like middle test or final test are bad (below 5). It shows that the trouble is in the teaching method. This research finds that the teacher do the same activities trough the same instructions with the different materials. The problem wouldn‘t be solved, if the teacher still used the same instructions.Key words: Instructional materials, learning process, method
Produktionens Progression : En självstudie i musikmakarens förtrollade resa

In this independent work, an exploration of a learning process takes place in the creation and developing of three different musical compositions. The study originates in the design theoretical per ...
Experimental Investigation on Adaptability of SE Module to DenseNet

Recently, layer stack approach for CNN (Convolutional Neural Network) has achieved high image recognition performance. However, as the number of stacked layers is increased, this leads to increasing number of parameters. Therefore, a high-speck machine is required for calculation. To solve this problem, in this research, we focus on SE module, which has an attention mechanism that adaptively trains the relationship among filters and achieves the improvement of recognition performance with a slight increase in the number of parameters. Although this module has achieved high parameter efficiency compared with the layer stack approach, the adaptability of the SE module is discussed insufficiently. Therefore, in this paper, we evaluate its adaptability by utilizing DenseNet and ResDenseNet, which have higher parameter efficiency compared with ResNet and both have DenseBlock modules. Unfortunately, a simple combination of SE module and such CNNs generally adds the SE module to end of each CNN module, which leads increasing a large number of parameters since the SE module requires parameters depending on the number of filters in DenseBlock. To solve this problem, we propose a new combination of SE and DenseBlock modules, that is, we add the SE module to each branching function. From the empirical evaluation with CIFAR10 and CIFAR100, our proposed method improved recognition performance compared with DenseNet/ResDenseNet without SE modules.
Research on the Parallel Calculation Method of Real-time Financial Index Market Based on Computer FPGA

Abstract Social and economic prosperity, the total volume continues to increase, emerging industries are springing up everywhere, success and failure are full of variables, financial index prices are constantly optimized but also full of variables, People who judge the economy and guide investment by index industries are required to control the rapidly changing financial industry and create the future. By analyzing the financial situation in the FPGA environment, this paper puts forward the parallel computing method based on computer, and designs and verifies the feasibility of the calculation bill.
ODArchive – Creating an Archive for Structured Data from Open Data Portals

We present ODArchive, a large corpus of structured data collected from over 260 Open Data portals worldwide, alongside with curated, integrated metadata. Furthermore we enrich the harvested datasets by heuristic annotations using the type hierarchies in existing Knowledge Graphs. We both (i) present the underlying distributed architecture to scale up regular harvesting and monitoring changes on these portals, and (ii) make the corpus available via different APIs. Moreover, we (iii) analyse the characteristics of tabular data within the corpus. Our APIs can be used to regularly run such analyses or to reproduce experiments from the literature that have worked on static, not publicly available corpora.
End-to-End 3D Face Model Reconstruction Method Based on Fusion-CNN

How to reconstruct robust 3D face models quickly from a single image is a hotspot in the field of computer vision which is widely used in wireless mobile network apps. Previous reconstruction methods relied on accurate landmark localization, which may increase the error possibility and limit the suitability. An end-to-end method is proposed in this paper based on Fusion-CNN to extract face parameters directly from a single image to achieve 3D face reconstruction. Multi-task loss function and fusion neural network are applied to convolutional neural network to enhance the reconstruction effect. As for the semantic information of the whole loss, both face parameters regressing and camera pose regressing are adopted to reduce the error of the neural network. Experiments demonstrate that the proposed method is able to reconstruct 3D face model from real pictures successfully regardless of lighting conditions and extreme expression.
IMPLEMENTASI TEORI NAIVE BAYES DALAM KLASIFIKASI CALON MAHASISWA BARU STMIK KHARISMA MAKASSAR

STMIK KHARISMA Makassar has graduated thousands of alumni since it was founded. However, the number of students registering is uncertain every year, although from 2016 to 2019 there has been an increase in the number of registrations. The problem is the percentage of the number of prospective students registering has actually decreased significantly. The purpose of this research is to implement the Naive Bayes theory in classification of STMIK KHARISMA Makassar prospective students. This research basically uses the Naive Bayes theory as a classifier, and is made using the Python programming language. At the classifier design stage, there were a total of 499 data collected from 2016 to 2019. The data was divided by a ratio of 80:20 for training data and test data. The result from the research indicate the level of accuracy of the classifier reaches 73%.
The Difference in Student Engagement in Latent Groups according to the Perceived Competencies and Learning Expectations of University Freshmen : Focusing on S University in Gwangju

이 연구는 대학 신입생을 대상으로 그들이 지각하는 역량과 학습기대에 따라 유사한 특성을 갖는 잠재집단이 도출되는지 탐색하고, 잠재집단 간 학습참여의 차이가 나타나는지를 확인하고자 수행되었다. 이를 위해 한국교육개발원의 대학생 교수학습 과정 조사(NASEL) 자료를 활용하여, 2019년에 광주 지역 S 대학에 입학한 남녀 신입생 497명을 분석대상으로 잠재프로파일분석을 실시하였다. 연구결과는 다음과 같다. 첫째, S대학의 신입생은 3개의 잠재집단이 도출되었으며 첫째 집단은 역량-학습기대 높은 집단, 둘째 집단은 역량-학습기대 보통 집단, 셋째 집단은 비인지적 역량-학습기대 낮은 집단으로 명명하였다. 둘째, 잠재집단 간 학습참여는 통계적으로 유의한 차이가 나타났다. 역량에 대한 지각과 학습기대가 높은 신입생 집단일수록 인지적 참여(능동적&#8901;협력적 학습활동), 정서적 참여(수업만족도, 교육지원서비스만족도), 행동적 참여(수업참여, 교수/동료와의 상호작용, 교육지원서비스 활용)가 높았으며, 특히 비인지적 역량과 학습기대가 낮은 신입생 집단은 학습참여가 모두 낮은 것으로 나타났다. 이러한 연구결과에 근거하여 대학 신입생의 학습참여 신장을 위한 교육적 시사점을 논의하였다.This study was conducted to identify the groups with similar responses using the perceived competencies and learning expectations of university freshmen, and to examine the difference in student engagement for each groups. For these purpose, 497 male and female freshmen enrolled in 2019 at S university in Gwangju participated in a NASEL(National Assessment Student Engagement in Learning), and the data was analyzed using latent profile analysis. The results of the study were as follows. First, the data was divided into three groups. The first group was called ‘perceived competencies - learning expectation high group’ with high competencies and learning expectations, the second group was called ‘perceived competencies- learning expectation average group’, and the third group was called ‘perceived nonacademic competencies - learning expectation low group’. Second, it was found that the total scores of student engagement were significantly different among the three groups, and that the higher the perceived competencies and learning expectations, the higher the engagement in learning(all with respect to cognitive, emotional, and behavioral engagement). Based on the results of this study, the educational implications to increase student engagement and educational learning outcomes were discussed.
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks.

The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cases is catastrophic forgetting, i.e., the tendency of neural networks to underfit past data when new ones are ingested. A first group of approaches tackles forgetting by increasing deep model capacity to accommodate new knowledge. A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model. While the first type of algorithms were compared thoroughly, this is not the case for methods which exploit a fixed size model. Here, we focus on the latter, place them in a common conceptual and experimental framework and propose the following contributions: (1) define six desirable properties of incremental learning algorithms and analyze them according to these properties, (2) introduce a unified formalization of the class-incremental learning problem, (3) propose a common evaluation framework which is more thorough than existing ones in terms of number of datasets, size of datasets, size of bounded memory and number of incremental states, (4) investigate the usefulness of herding for past exemplars selection, (5) provide experimental evidence that it is possible to obtain competitive performance without the use of knowledge distillation to tackle catastrophic forgetting and (6) facilitate reproducibility by integrating all tested methods in a common open-source repository. The main experimental finding is that none of the existing algorithms achieves the best results in all evaluated settings. Important differences arise notably if a bounded memory of past classes is allowed or not.
2D PCA for automatic segmentation of the prostate in ultrasound images

Accurate automatic segmentation of the prostate in ultrasound images is still a challenging research problem. In this work, we propose the use of gray level images, constructed with a sample of gray level profiles perpendicular to the contour of the prostate. A two dimensional principal component analysis (2D PCA) was performed on a set of training contour images. The reconstruction error from the 2D PCA was used as an objective function for automatic adjustment of a point distribution model of the prostate. Our method was validated on 9 ultrasound images of the prostate and compared to the optimization of an objective function based on the mean Mahalanobis distance of a sampled gray level profile to the corresponding statistical profile model. Our new method based on a 2D PCA shows improved prostate segmentation results.