Reviewerly/exHarmony huggingface dataset:
Performance and Power Evaluation of AI Accelerators for Training Deep Learning Models

Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time, and much energy. Nowadays, many-core AI accelerators (e.g., GPUs and TPUs) are designed to improve the performance of AI training. However, processors from different vendors perform dissimilarly in terms of performance and energy consumption. To investigate the differences among several popular off-the-shelf processors (i.e., Intel CPU, NVIDIA GPU, AMD GPU, and Google TPU) in training DNNs, we carry out a comprehensive empirical study on the performance and energy efficiency of these processors by benchmarking a representative set of deep learning workloads, including computation-intensive operations, classical convolutional neural networks (CNNs), recurrent neural networks (LSTM), Deep Speech 2, and Transformer. Different from the existing end-to-end benchmarks which only present the training time, We try to investigate the impact of hardware, vendor's software library, and deep learning framework on the performance and energy consumption of AI training. Our evaluation methods and results not only provide an informative guide for end-users to select proper AI accelerators, but also expose some opportunities for the hardware vendors to improve their software library.
A New Frame Work for Content Based Image Retrieval Based on Rule Based Motifs on Full Texton Images

This paper derives new framework for content based image retrieval (CBIR) by deriving color features and texture features.This paper initially transforms the RGB color plane image into HSV color plane and derives the individual histograms on H, S and V planes.This paper derives rule based dynamic motif matrix (RDMM) on full texton index (FTi) index images of V-plane.The FTi overcomes the disadvantages and ambiguity issues in indexing textons of earlier methods.The RDMM overcomes the ambiguity issues arise especially when two or more pixels of the 2x2 grid exhibit the similar intensity levels and this reduces overall retrieval rate.This paper integrated the co-occurrence features derived on RDMM-FTi with histogram features of pure color model.The proposed framework is tested on popular color databases and also compared with state of art models of texton and motif based methods based on average precision rate (APR) and average recall rate(ARR).The experimental results indicate the efficacy of the proposed method over the existing methods.
Strategy of Oyster Mushroom CultivationThrough Women Empowermentin Mushroom House at Urban Village of Siumbut Baru

The problem in this research is how the management strategy of oyster mushroom cultivation in Mushroom House, Urban Village of SiumbutBaru, sub-district of East Kisaran, District of Asahan.The purpose of this research is to find out the management strategy of oyster mushroom cultivation in Mushroom House, Urban Village of SiumbutBaru, sub-district of East Kisaran, District of Asahan.This is a descriptive qualitative research; the informant is the manager and two employeesof the Mushroom House.The data were collected by interview and documentation, and analyzed through: data collection, data reduction, data display, and conclusion.The results showed that the manager can develop and improve the quality of his business systematicallyin order to have a good management system in accordance with the management function, he can make good preparation for his bussiness, he always chooses the superior oyster mushroom seeds, he takes care of the oyster mushroom that have been cultivated, and analyzed the oyster mushroom business opportunities and renewing the types of food that will be served.The interview revealed that the management strategy in improving the motivation of employees to build their own business by giving them knowledge with sincerity, give them strategy to to improve the quality of business and never give up.
A Secure and Privacy-Aware Framework for Future Smart Cities

Smart cities has developed in urban areas and are interconnecting every part of the city and offering enormous smart services to make the human life easier and convenient.The smart services are a result of data streams connecting the smart cities including citizen information, smart activities of the individual facilitate smart transportation, surroundings and also access to their information through local government portals for their own data.These huge amounts of data gathering and processing increases privacy preservation and security limitations and challenges at individual and social levels.Therefore it is required to ensure data security and privacy preservation to the stored citizens' data.But these issues are not only restricted to citizens' or stakeholders alone rather pervasive to service providers (SPs) and governments with their own databases.This research paper is an attempt to identify the issues and challenges of the privacy and security for ensuring that proper services are provided to the participants in smart city activities.This paper presents a secure and privacy-aware framework for offering efficient services in smart cities.The proposed model of smart cities ensures the stakeholders' privacy and integrity of services such as avoiding misuse of public data malicious SPs.
PENGEMBANGAN KOSAMI (KOMIK SAKU MATEMATIKA) BAGI SISWA KELAS VII SMP NEGERI 7 TARAKAN

The development model used is the Borg and Gall model of Sugiyono modification. In the Borg and Gall model, the Sugiyono modification consists of 10 stages, namely Potential and Problems, Data Collection, Product Design, Design Validation, Design Revision, Product Testing, Product Revision, Usage Testing, Product Revision and Mass Production. The media testing phase is done through 2 stages, namely the assessment of experts and user response. In the assessment of experts validated by material experts, media experts and linguists. While the user response was tested by 30 students and 2 mathematics teachers. The results of analysis shows that the kosami product developed based on validation by material experts obtained a feasible category with a percentage of 79.68%, media experts obtained the category of Very Eligible with a percentage of 92.72% and linguists obtained the Very Eligible category with a percentage of 83.33%.While the result of research in the field by the response of teacher user obtained a percentage of 75% with a Very Practical category, responses of student users obtained by 87,33% with a Very Eligible category, and responses of student users obtained by 90,67% with a Very Practical category . Because it has fulfilled the feasible and practical requirements of a Kosami learning media consisting of aspects of material feasibility, media feasibility, feasibility of language use, and practicality of user response, the kosami can be used as a feasible and practical learning media for students of clas VII SMP Negeri 7 Tarakan on Number Material.
‘Sontoloyo' and 'Genderuwo'; Critical Discourse Analysis on Jokowi’s Utterance

This study discusses Joko Widodo’s utterance in several moments. Jokowi is the president of the Indonesian Republic. He would like to participate in the reelection of the presidential election in April 2019. Many statements of him make public perceptions appear different. In this case, there is two big points of research that can be famous dictions in this era. ‘sontoloyo’ and ‘genderuwo’ are two interesting dictions to discuss. As a popular person, Jokowi’s utterances occur as public discourses. Many perspectives are produced by many analysts in multidiscipline views. Critical Discourse Analysis (CDA) is used as a framework to analyze the utterances related to many perspectives of language. Fairclough model is a choice for analyzing in depth of the research. It states in three big points; text analysis (description), processing analysis (interpretation) andsocial analysis (explanation). The researcher uses a qualitative method. The data related with the sontoloyo and genderuwo utterance comes from news online the Jakarta Post. By using Fairclough theory, the researcher explains the text in the relationship with the situation of political moment, the speaker as government and candidate of the presidential election. The text gives many interpretation and relationship with the social condition.
Rancang Bangun Mesin Crawler di Instagram dan Pinterest untuk Kebutuhan Data pada Riset Visual

ABSTRAKMedia sosial memberikan bonus berupa data yang dapat dikelola menjadi informasi yang bermanfaat. Dalam penelitian ini, kami mengembangkan mesin crawler untuk media sosial Instagram dan Pinterest. Mesin crawler ini digunakan sebagai infrastruktur pendukung untuk mengambil data di media sosial. Data yang dihasilkan oleh mesin crawler selanjutnya digunakan sebagai bahan riset visual untuk merancang kemasan produk bagi konsumen menengah ke atas. Hasil uji coba menunjukkan bahwa penggunaan Apache MesOS dapat mempercepat proses crawling dari 30 jam menjadi 1 jam. Dalam hal seleksi data, pada Pinterest, mesin crawler ini dapat mencapai akurasi hingga 100%. Sementara itu, pada Instagram, nilai Presisi tidak stabil dan berada pada rentang 34.8% hingga 90.0%. Sedangkan nilai recall dan akurasinya konsisten di bawah 70%. Hal ini menunjukkan bahwa arsitekturr mesin crawler sudah sesuai untuk menyelesaikan permasalahan. Namun, perbaikan masih dibutuhkan dari sisi algoritma seleksi agar nilai Presisi, Recall dan Akurasi pada Instagram dapat ditingkatkan lagi.Kata kunci: Media Sosial, Riset Visual, Mesin Crawler, Infrastruktur Big DataABSTRACT Social media gives us a huge number of data to be analyzed and sends us useful knowledge. In this paper, we develop a crawler machine for Instagram and Pinterest as an infrastructure of social media based visual research. We conduct the visual research to design product package for consumers from the middle class and upper class. The crawler machine is developed using Apache Kafka, Apache Spark, and Apache MesOS The evaluation result shows us that Apache MesOS can speed up data processing from 30 hours to one hour. In term of data selection, this machine can achieve 100% accuracy on Pinterest. Meanwhile, on Instagram, the precision is unstable between 34.8% to 90.0%. On the other hand, the recall and the accuracy on Instagram are consistently below 70%. Based on the evaluation result, we conclude that the machine performs well in term of time efficiency. However, we need to improve the selection algorithm so that the precision, the recall and the accuracy on Instagram can be increased.Keywords: Social Media, Visual Research, Crawler Machine, Big Data Infrastructure
Mobile Composite Application Simulator As Efficient Learning Media

ACT The development of Information Communication and Technology (ICT) has affected on various sectors. In the field of education have been used in the development of learning media. It is not cheaper to buy the laboratory equipment’s and also the maintenance. Therefore, various laboratory equipment used in learning media with the help of ICT can be developed simulation application. Particularly in the field of mechanical engineering laboratory equipment required a tools to be used on calculations in combining or arranging the chemical properties and physical properties of material objects, thus producing an appropriate composite material. By using android developer can be developed mobile-based applications that act as a simulator in terms of composite calculation composite materials more accurate and efficient. Using this application is improving efficiency in providing laboratory equipment’s and maintenance cost.
Evaluating the portals of Iran ministries using the Standard WebQEM

The present study was done to evaluate the quality of portals in Iran ministries using "WebQEM".The research was descriptive and the method was web metrics.The research community is 13 portals of Iran Ministries.The required data was collected using a checklist designed based on a web-based quality evaluation method.Data analysis was performed using SPSS-21 software.After evaluating the main criteria using the Web quality evaluation method and considering the average scores obtained for four main indices, it is concluded that the portals examined had more weaknesses in terms of functionality and usability, which required a closer look.In the case of two indices of reliability and performance, almost all sites had a very good and high score.The designers of these portals should focus on these weaknesses and try to resolve them.
Neural Network-Based Deep Encoding for Mixed-Attribute Data Classification

This paper proposes a neural network-based deep encoding (DE) method for the mixed-attribute data classification. DE method first uses the existing one-hot encoding (OE) method to encode the discrete-attribute data. Second, DE method trains an improved neural network to classify the OE-attribute data corresponding to the discrete-attribute data. The loss function of improved neural network not only includes the training error but also considers the uncertainty of hidden-layer output matrix (i.e., DE-attribute data), where the uncertainty is calculated with the re-substitution entropy. Third, the classification task is conducted based on the combination of previous continuous-attribute data and transformed DE-attribute data. Finally, we compare DE method with OE method by training support vector machine (SVM) and deep neural network (DNN) on 4 KEEL mixed-attribute data sets. The experimental results demonstrate the feasibility and effectiveness of DE method and show that DE method can help SVM and DNN obtain the better classification accuracies than the traditional OE method.
Developing CyberSecurity Skills with Intermediate Programming Courses

For most electrical and computer engineering students intermediate programming is a required course.Typical topics taught in intermediate programming are advanced array operations, reading data from files, writing data to files, string conversions, string manipulations, pointers, searching for a text string, sorting of data into alphabetical order and numerical methods.To motivate students instructors should create learning experiences that the student can relate to for example by incorporating popular culture into the teaching materials.Judicious choice of teaching material can spell the difference between engaging or not engaging the interest of the student.One method for improving student engagement would be to provide evidence that they are learning a skill that will in the long run be useful to them in some way.Many cybersecurity concepts can be introduced and learned using techniques suitable for intermediate programming classes.
Learning Vector-valued Functions with Local Rademacher Complexity.

We consider a general family of problems of which the output space admits vector-valued structure, covering a broad family of important domains, e.g. multi-label learning and multi-class classification. By using local Rademacher complexity and unlabeled data, we derived novel data-dependent excess risk bounds for vector-valued functions in both linear space and kernel space. The proposed bounds are much sharper than existing bounds and can be applied into specific vector-valued tasks in terms of different hypotheses sets and loss functions. Theoretical analysis motivates us to devise a unified learning framework for vector-valued functions based which is solved by proximal gradient descent on the primal, achieving a much better tradeoff between accuracy and efficiency. Empirical results on several benchmark datasets show that the proposed algorithm outperforms compared methods significantly, which coincides with our theoretical analysis.
Large-Scale Machine Learning Algorithms for Biomedical Data Science

Data science is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment, and prevention of diseases. However, the unprecedented scale and complexity of large-scale biomedical data have presented critical computational bottlenecks requiring new concepts and enabling tools. To address the challenging problems in current biomedical data science, we proposed several novel large-scale machine learning models for multi-dimensional data integration, heterogeneous multi-task learning, longitudinal feature learning, etc. Meanwhile, to deal with the big data computations, we proposed new asynchronous distributed stochastic gradient and coordinate descent methods for efficiently solving convex and non-convex problems, and also parallelized the deep learning optimization algorithms with layer-wise model parallelism. We applied our new large-scale machine learning models to analyze the multi-modal and longitudinal Electronic Medical Records (EMR) for predicting the heart failure patients' readmission and drug side effects, integrate the neuroimaging and genome-wide array data to recognize the phenotypic and genotypic biomarkers, and detect the histopathological image markers and the multi-dimensional cancer genomic biomarkers in precision medicine studies.
Remote Sensing Image Registration With Adjustable Threshold and Variational Mixture Transformation

This letter proposes a remote sensing (RS) image registration method based on adjustable threshold and variational mixture transformation (VMT). The main contributions of our method are: 1) an adjustable threshold strategy proposed to guarantee sufficient inlier pairs for image spatial transformation and 2) a VMT, which achieves a coarse-to-fine process, consisting of three main steps as follows: a) a rigid transformation is employed to achieve approximate correspondence; b) a guided Gaussian mixture model is proposed to better distinguish outliers; and c) a nonrigid transformation is applied to achieve an accurate registration. We test the performance of our algorithm in feature matching and RS image registration and compare it with the six state-of-the-art methods. Our method shows the best performances in most scenarios.
Related Research on “The Belt and Road” Initiative Based on Big Data Text Mining: Taking the Domestic Area and the Korean Peninsula as an Example

As one of the core contents of China, "The Belt and Road" is very necessary to analyze the impact and significance of "The Belt and Road" on the domestic areas and Korean Peninsula areas in the context of big data.The text mining method was used to extract the core content and hot topics from the news reports of "The Belt and Road" in China and the Korean Peninsula in recent domestic and international news data.Using the unique attributes of public opinion media and news reports.Focusing on the key theme of "The Belt and Road", using clustering algorithm and TF-IDF method, combined with LDA topic model, the structure and content of the theme are analyzed in detail on the pre-built big data analysis platform.The establishment of a big data platform and the use of TF-IDF and LDA topic models have improved the efficiency of data analysis.On the domestic front, the eastward extension of "The Belt and Road" is imperative and the prospects are clearer.On the Korean Peninsula, its own security situation is the core issue, and the United States is the biggest drag factor.In the future, it is necessary to focus on the common focus and solve the problem.
Perancangan Sistem Informasi Geografis Lokasi Objek Wisata Di Kota Lubuklinggau Berbasis Android

The Problem In Finding and acces a tourism location in Lubuklinggau, be a primary problem, Especially for tourism or visitoe or tourism from other place in Indonesia. We know that the technology more being advance, but this case make we to improve our potency in Lubuklinggau.&#x0D; Hopely this application which will be made, will appliance the web technology service, in order to the information about tourism will be up to date and to solve this problem the researcher use digital map model approach or SIG based on Android by using Google Maps as the map digital. The method which used in tihs research is UML (Unifide Modelling Language), PHP Program, Java and database Mysql as the data saving geografic information system (GIS) tourism has two programs they are, administration programe for processing tourism data base on web and programe for the user, we use Android aplication. Geografic information system has a goal to give an easy and faster information about tourism in Lubuklinggau
A fast and efficient method for solving the multiple line detection problem

In this paper, we consider the multiple line detection problem on the basis of a data points set coming from a number of lines not known in advance.A new and efficient method is proposed, which is based upon center-based clustering, and it solves this problem quickly and precisely.The method has been tested on 100 randomly generated data sets.In comparison to the incremental algorithm, the method gives significantly better results.Also, in order to identify a partition with the most appropriate number of clusters, a new index has been proposed for the case of a cluster whose lines are cluster-centers.The index can also be generalized for other geometrical objects.
Numerical data of probabilistic 3D lithological map of Japanese crust

The 3D lithological distribution model presented in this data article is related to “Stochastic modeling of 3-D compositional distribution in the crust with Bayesian inference and application to geoneutrino observation in Japan” by Takeuchi et al. (2019) [1]. Our target region is set to the crust and uppermost mantle beneath Japanese main islands and their vicinity. We discretized the target region into 79,968 grid points. We defined 31 rock types; 29 major crustal rock types, plus sediment and mantle. Our lithology model represents a probabilistic distribution map inferred from a seismic tomography model and allows quantitative studies with error estimations, making it fundamentally different from previous models. To enable such quantitative applications, we provide explicit numerical data for the probabilities of the 31 rock types for each grid point. We also provide explicit values of the bulk proportion lithology model at various depths and for the bulk whole crust. Further, a figure of synthetic gravity data is presented to correct a minor error in the related paper [1].
Binary logistic regression methods for modeling broncho-pneumonia status in infants from tertiary health institutions in north central Nigeria

Acute respiratory tract infections, predominantly bronchopneumonia, are one of the leading causes of infant deaths in developing countries and around the world. This work models the effects of the significant risk factors on infants’ bronchopneumonia status and also fits some reduced models and determines the best model with minimum number of parameters. The data for this study consist of a random sample of 433 births to women seen in the obstetrics clinic of two sampled tertiary health institutions in north-central Nigeria. These include University Teaching Hospital (UTH) Abuja, and Federal Medical Center (FMC) Keffi, Nasarawa State. Binary logistic regression was used to identify and model the effects of the various risk factors while stepwise regression technique was used to fit some reduced logistic regression models. Then the best fitting model with minimum number of parameters was identified using likelihood ratio statistic. It was observed that baby’s weight at birth, baby’s weight four weeks since birth, and mother’s occupation have significant effects on infant’s bronchopneumonia status. Additionally, among the four fitted reduced models, model4 is the best predictor of infants’ bronchopneumonia status, followed by model3 and then model2. Therefore, community service like home visiting for health education, supplementation of vitamin A, etc., would be an advantage if provided for teenaged pregnant women as it would, in turn, reduce incidence of low birth weight and thereby reduce bronchopneumonia infection among these children.Keywords: Bronchopneumonia, Multiple Logistic Regression Model, Fitness, likelihood ratio test