Reviewerly/exHarmony huggingface dataset:
The development of libmosaic-sound: a library for sound design and an extension for the Mosaicode Programming Environment

Music has been influenced by digital technology over the last few decades. With the computer, the musical composition could trespass the use of acoustic instruments demanding to musicians and composers a sort of computer programming skills for the development of musical applications. In order to simplify the development of musical applications, several tools and musical programming languages arose bringing some facilities to lay-musicians on computer programming to use the computer to make music. This work presents the development of a Visual Programming Language (VPL) for audio applications in the Mosaicode programming environment, simplifying sound design and making the synthesis and manipulation of audio more accessible to digital artists. It is also presented the implementation of libmosaic-sound library for the specific domain of Music Computing, which supported the VPL development.
Finding Malicious One in Group Data Sharing

Cloud computing is one of the fastest developing in the internet, it provides user demanding resources and services with low cost.Users can easily use cloud resource to do their works without any interruptions like doing personal works, social networks, video editing and business works like many more.Group data sharing grabbed people attention to share their works and thoughts in cloud computing.In this concept multiple users can access same data or documents to do works for developed of projects.With this concept users get lots of benefits because they can do work any where as they like with their resources because they don't need to buy new hardware or software to their works.Whenever group of people working on some important work then owner can't find malicious user in it.Its big challenge in cloud computing.Group members can share information anonymously without revealing their identity but this became big challenge in computing because when user used fake id's then it will be threat to document because they might misuse it.Dynamic changes of the group member, exploiting the key understanding and efficient get to control, the computational multifaceted nature and correspondence intricacy for refreshing the normal meeting key and the encryption information are generally low and its time taking process to share data in groups and consume high memory.Proposing user defined encryption for data sharing to secure communication.It can share the data to users in secure way and efficiently and tracing malicious user in group data sharing.It requires low amount to memory and low time to process encryption.
Message from the Program Chair - SmartCloud 2019

The SmartCloud 2019 is the next edition of a series of successful academic events, including SmartCloud 2016 (New York, USA), SmartCloud 2017 (New York, USA), and SmartCloud 2018 (New York, USA). The conference has involved a variety of topics focusing on smart computing researches mainly in cloud computing scenarios. The recent research and improvement have brought a plurality of interests and insights to our picturesque meeting venue. We sincerely hope that all attendees and contributors of this conference can take advantage of this opportunity through all activities held by this event, from presentations to keynote lectures. Moreover, it is our great honor to introduce the program for the SmartCloud 2019. We received over 177 research papers for this conference out of which fewer than 37 were accepted as full papers. The acceptance rate of SmartCloud 2019 is 20.9%. We have put together a few tracks over the period of the conference.
Development of a New Image-to-text Conversion System for Pashto, Farsi and Traditional Chinese

We report upon the results of a research and prototype building project \emph{Worldly~OCR} dedicated to developing new, more accurate image-to-text conversion software for several languages and writing systems. These include the cursive scripts Farsi and Pashto, and Latin cursive scripts. We also describe approaches geared towards Traditional Chinese, which is non-cursive, but features an extremely large character set of 65,000 characters. Our methodology is based on Machine Learning, especially Deep Learning, and Data Science, and is directed towards vast quantities of original documents, exceeding a billion pages. The target audience of this paper is a general audience with interest in Digital Humanities or in retrieval of accurate full-text and metadata from digital images.
Initial ideals of Pfaffian ideals

We explore the relationship between secant ideals and initial ideals of I 2 , n , the ideal of the Grassmannian, Gr ( 2 , ℂ n ) . The ( r − 1 ) -secant of I 2 , n is the ideal generated by the 2 r × 2 r subpfaffians of a generic n × n skew-symmetric matrix. It has been conjectured that for a weight vector ω in the tropical Grassmannian, the secant of the initial ideal of I 2 , n with respect to ω is equal to the initial ideal of the secant. We show that this conjecture is not true in general. Using the correspondence between weight vectors in the tropical Grassmannian and binary leaf-labeled trees, we also give necessary and sufficient conditions for the conjecture to hold in terms of the topology of the tree associated to ω . In the course of proving this result, we show that the ideal in ω ( I 2 , n { r } ) is always prime, thus giving a new class of prime initial ideals of the Pfaffian ideals.
LibGuides: ENL for Elementary School Library: Home

This LibGuide is intended to provide guidance and resources for students, parents and classroom teachers in supporting ENL student in grades K-5.
Computer-aided Pathological Annotation Framework: A Deep Learning-Based Diagnostic Algorithm of Lung Cancer

Whole-slide image (WSI) annotation is fundamental to computational pathology. However, pixel-level manual annotation of large-scale histopathology dataset is impractical due to the combination of required labor-intensitive tasks and the budget limitation in pathologists' supports. In this paper, we develop a framework for computer-aided annotation of cancerous region on WSI with pathologically conformed lung cancers aiming to significantly reduce the workload of pathologists in annotation on the WSIs. We used deep convolutional neural network (DCNN) to automate the labeling of the area of cancerous lesions. Our method was able to identify malignant area on tested WSIs with a mean dice of 0.84, in comparison to pathologists' manual annotation.
COMPARISON OF QUICKSORT AND MERGESORT METHOD FOR USER INTERFACE AND USER EXPERIENCE ASSESSMENT IN ADISUTJIPTO COLLEGE OF TECHNOLOGY

This study is intended to assess the user interface and user experience on the Adisutjipto College of Technology website using the mergesort method and compared with the quicksort method in finding the highest value of several variables or elements that are categorized as good or good in order to improve the quality of the Adisutjipto College of Technology website. The results of the study show that the quicksort method is faster in the process of calculating questionnaire data than the merge method with an average time record of 0.476667 seconds for the quicksort method and 0.576667 seconds for the mergesort method. The results of the comparison of the two methods on the questionnaire website to assess the accuracy in terms of user interface and user experience on the Adisutjipto College of Technology website, that the mergesort method is more accurate than the quicksort method, the Usability category scores the highest, while the Expressive aesthentics category has the lowest value
LibGuides: Alumni Resources: Home

Michigan State alumni have access to the University's vast campus print collections, as well as on-campus use of the Libraries' databases.
Similarity-The Enhancement of Fingerprint Images using Gabor Filter

Hasil check similarity dengan turnitin pada artikel prosiding dengan judul The Enhancement of Fingerprint Images using Gabor Filter. Hasil turnitin 19%
Using remote sensing for monitoring the dynamic of soil organic carbon concentration in Lake Valencia basin, Venezuela, based on Landsat 8 data

Maintaining or increasing SOC concentration is fundamental for reducing the effects of global warming and increasing soil productivity. In this paper, a method based on Landsat 8 OLI products was developed for qualitatively monitoring in the Lake Valencia basin (Venezuela) the dynamic of SOC concentration between the years 2013 to 2018. The developed method uses the Green (B3), NIR (B5) and SW1 (B6) bands of Landsat 8 OLI sensor for detecting changes in the spectral signatures of bare soils that indicate possible variations in their concentrations of SOC. It was found that for the study period, the Lake Valencia basin soils do not present spectral features of significant variation in SOC concentration. An area of 8.61Km 2 (0.3% of the study area) was identified as a zone with a possible reduction of SOC concentration. In case of insufficient data for developing remote sensing based predictive models, the proposed method allows qualitatively monitoring and categorizing the dynamic of SOC concentration and identifying areas with spectral features of a possible variation in SOC concentration.
Forecasting Omset Printing of Printing Sales in CV Sembilan Jaya with Neural Network Method

Forecasting is a process for estimating several needs in the future which includes needs in order to meet thedemand for goods and services. Neural Network Backpropagation Method is a time series forecasting method. Thepurpose of this study is to predict the turnover results in the next period obtained by CV. Nine Jaya every week. Thisstudy uses sales data obtained from the printing of food boxes, shoe boxes, watch boxes from January 2014 toDecember 2018. The results of this forecasting are done using the Neural Network method, the smallest MSE valueobtained is 0.004211 with 1000 times iteration and learning rate 0.2. The MSE value obtained meets the condition orcondition value as a good forecasting method because it is able to meet the MSE value requirement &lt;0.1.
LibGuides: Computer Networks: Journals

This guide is for the subject of Computer Networks. It contains resources available in the NCU Library.
PENERAPAN METODE NAÏVE BAYES DALAM PEMERIKSAAN KESEHATAN

Maintaining the safety and health of its workers by conducting daily health checks. The medical examination is carried out by a doctor or nurse at work. Workers who were declared unhealthy were not allowed to work on that day, in several cities, so that inspection standards were maintained, a web-based system was implemented that implemented a rule-based expert system. Naive Bayes algorithm which is included as an algorithm for classification based on data mining and machine learning activities. The initial process of Naive Bayes begins by studying existing data (complete with labels) through the training process. The results of this training are actually the probability of an outcome based on the variables used. Naive Bayes was chosen because it can study the results of past examinations and use them to make the next decision. This is felt to be a solution to the problems of previous system deficiencies that require the experience and expertise of health workers who are not necessarily equal.
New Pathways in Coevolutionary Computation

The simultaneous evolution of two or more species with coupled fitness—coevolution—has been put to good use in the field of evolutionary computation. Herein, we present two new forms of coevolutionary algorithms, which we have recently designed and applied with success. OMNIREP is a cooperative coevolutionary algorithm that discovers both a representation and an encoding for solving a particular problem of interest. SAFE is a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions needed to measure solution quality during evolution.
ART SPACE: AN EXPERIMENTAL DIGITAL ART GAME

The article examines the discourse concerning modern game theory and suggests a new method of research and knowledge transfer in the field of digital art game creation. The method is embodied in the new game Art Space that utilizes current research results in the field of contemporary aesthetics. Art Space is an experimental digital game that is being created in collaboration between researcher, Dr.art. Ieva Gintere (Vidzeme University of Applied Sciences, Latvia) and the game artist, Mag.art. Kristaps Biters (Liepāja University, Latvia) within the framework of a post-doctoral project. The concept of this new art game arises from the historical heritage of modern art. The aim of the game is knowledge transfer: the author has been carrying out research into contemporary digital games in order to transfer the results of the research to develop an appreciation and understanding of aesthetics in Art Game’s players. The game links aesthetics to art games by identifying modern trends such as pixel art, glitch, noise, and others. Due to the dearth of written information on the subject of modern art heritage in digital games, the study presents an innovative approach to art gaming explaining modern art’s cultural backgrounds. The methods used are audio-visual and stylistic analyses of games as well as studies of the existing literature. The project hopes to raise the interest of the wider public concerning contemporary art and music, point out the newest creative tendencies in art, and suggest potential changes in the language of art in the near future. This paper continues previously published research that helped to create the concept and design of Art Space, and focuses on the trends of photorealism and futurism.
MODERN EDUCATIONAL STRATEGY: SKILL TRAINING IN EFFECTIVE THINKING

The situation of crisis characteristic of education actualizes the consideration of a modern educational strategy in order to identify its methodological foundations. The educational strategies developed in the period of Antiquity, the Middle Ages, the New Age are ana-lyzed. As part of the traditional educational strategy shows the subject-objective nature of the interaction of the teacher and the student. The failure of the traditional paradigm in the knowledge society is demonstrated. The post-industrial society proclaiming informati-zation creates a cultural environment, with a value system corresponding to it, where ed-ucation occupies a special place, but its essential characteristics are such as “creative”, “reflexive”, “critical”. The concept of an educational space that is in development, contrib-uting to the isolation of the “image” of individuality in man is investigated. It is concluded that the emerging modern educational strategy is dependent, in particular, on the devel-opment of information technologies, which is reflected in the organization of the educa-tional process and the educational system as a whole.
The impact of success factors on the strategic management in an educational complex

The most important topics that have attracted attention in recent decades have been changes and transformations in education. The purpose of this study is an analytical analysis of the factors of strategic management in the field of education in the Customs Union countries, using the example of Russia and Kazakhstan. Over the past two decades, the education system of the Customs Union countries has been reorganized from education management to strategic management that focuses on the future. The authors prove that an integrated and dynamic global market in the context of a pandemic is transforming the field of education. The article analyzes the interacting elements of the educational system, since they have a cumulative effect. The work has the character of a description, the methodological aspects of the study are determined by the aspects of institutional trust in social institutions, social management, the relevance and importance of institutional trust for the effective functioning of the social institution of education, the factors that significantly affect the formation of institutional trust are considered. The authors propose to consider strategic management as the driving force of innovative programs in education with an emphasis on human resources. The authors prove on extensive material that strategic management reduces the risks of educational organizations and transforms them into new fields of opportunities.
ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes

Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand deep learning holds promise to positively impact clinical practice by leveraging medical data to assist diagnosis and prediction, including mortality prediction. However, as the question of whether powerful Deep Learning models attend correlations backed by sound medical knowledge when generating predictions remains open, additional interpretability tools are needed to foster trust and encourage the use of AI by clinicians. In this work we show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance. Our model reaches a ROC of 0.8629 (+/-0.0058), outperforming the traditional SAPS-II score and providing enhanced interpretability when compared with similar Deep Learning approaches.
VerifyTL: Secure and Verifiable Collaborative Transfer Learning

Getting access to labelled datasets in certain sensitive application domains can be challenging. Hence, one often resorts to transfer learning to transfer knowledge learned from a source domain with sufficient labelled data to a target domain with limited labelled data. However, most existing transfer learning techniques only focus on one-way transfer which brings no benefit to the source domain. In addition, there is the risk of a covert adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper we construct a secure and Verifiable collaborative Transfer Learning scheme, VerifyTL, to support two-way transfer learning over potentially untrusted datasets by improving knowledge transfer from a target domain to a source domain. Further, we equip VerifyTL with a cross transfer unit and a weave transfer unit employing SPDZ computation to provide privacy guarantee and verification in the two-domain setting and the multi-domain setting, respectively. Thus, VerifyTL is secure against covert adversary that can compromise up to n-1 out of n data domains. We analyze the security of VerifyTL and evaluate its performance over two real-world datasets. Experimental results show that VerifyTL achieves significant performance gains over existing secure learning schemes.
PENGEMBANGAN PAKET PELATIHAN MENULIS EKSPRESIF SEBAGAI MEDIA KATARSIS KECEMASAN SISWA INTROVERT DI SMA

Abstract : The purpose of this study was to develop a training package expressive writing as catharsis media introverted high school student anxiety. The research model used adapts from Borg and Gall. The product has been through expert test of BK, media, language and test of product user candidate and operational test. Data were collected using an anxiety scale questionnaire, introverted questionnaires, interviews and documentation studies. Data analysis techniques use descriptive statistics. The distribution of anxiety scale questionnaires and introverted scale is done in SMA 1 Pasuruan, distributed to 70 students in grade 10. From the division of questionnaires proved some students have high levels of anxiety and tend to introvert. Results of the assessment expert testing and test prospective users stating the product has met the eligibility criteria, accuracy, usefulness, the attractiveness and convenience. The results of product operational tests show that there are differences in anxiety levels of students before and after being treated using expressive writing training packages. For the introverted scale there are differences in introverted levels of students before and after being treated.&#x0D; Keywords : Training Packages, Expressive Writing, Catharsis, Anxiety, Introverts.
Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model

Studies on grammatical error correction (GEC) have reported the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining for GEC because of the size of the pseudodata. In this study, we explore the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC. Our implementations are publicly available at GitHub (https://github.com/Katsumata420/generic-pretrained-GEC).
A Method for Selecting Learning Data in the Prediction of Time Series with Explanatory Variables using Neural Networks

In the prediction of time series using multilayer feedforward neural networks, there are two practical methods for selecting learning data: the moving window data learning method and the similar data selective learning method with the correlation coefficient based similar data selection method which we proposed in a previous paper. In this paper, for time series data with explanatory variables, the predictive performance by the two methods was investigated by numerical simulations. With the time series whose nature is choppy, that by the latter was considerably better than that by the former. With the time series whose nature is smooth, that by the former was slightly better than that by the latter. According to these results, it is conjectured that the latter is effective for a time series whose nature is choppy.
Why Small Business Security Matters for Your Large Corporation

Today’s hackers are becoming more sophisticated and are finding new ways to infiltrate a company’s computer networks and servers. From stealing ...
End-To-End Rotational Motion Deblurring Method Combining with Motion Information

Abstract We propose an end-to-end rotational motion deblurring method based on conditional generation adversarial networks. The proposed method calculates the blur path value of each pixel on the rotational motion blurred image to provide a priori information of its blur degree, and then connects it to the blurred image as the input of the network. In addition, a rotational motion blurred image dataset is produced, which contains different degrees of rotational motion blurred images, as an evaluation dataset for the method to the effect of rotational motion deblurring. Experiments show that the proposed method is superior to existing end-to-end deblurring methods in both qualitative and quantitative analysis when dealing with different degrees of rotational motion blur.