Reviewerly/exHarmony huggingface dataset:
Stability Analysis of Denoising Autoencoders Based on Dynamical Projection System

In this study, we give a stability analysis of denoising autoencoder(DAE) from the novel perspective of dynamical systems when the input density is defined as a distribution on a manifold. We demonstrate the connection between the corrupted distribution and the learned reconstruction function of a nonlinear DAE, which motivates the use of a dynamic projection system (DPS) associated with the learned reconstruction function. Utilizing the constructed DPS, we prove that the high-density region of the corrupted data distribution asymptotically converges to the data manifold. Then, we show that the region is the attracting stable equilibrium manifold of the DPS which is completely stable. These results serve a theoretical basis of the DAE in recognizing the high-density region of the highly corrupted data with large deviations through the DPS. The effectiveness of this analysis is verified by conducting experiments on several toy examples and real image datasets with various types of noise.
Multi-modal gesture elicitation methodology for children

This course will prepare students to both analyze and conduct gesture and multi-modal elicitation studies. These skill sets are critical as emerging technologies continue to shift how we interact with systems. The course will have several sections: Introduction, Objective, and Motivation (30 minutes), Gestures (30 minutes), Gesture Taxonomies (30 minutes), Elicitation Techniques and Current Practices (60 minutes), Hands-on Elicitation Study (45 minutes) which will be done during a break out session, Discuss use-case scenario IDC/Elicitation related (45 minutes). Writing an Elicitation Paper (30 minutes), Challenges and Solutions Discussion (30 minutes), The Future of Elicitation (30 minutes).
A Pilot Benchmarking Study of Deep Neural Network Performance on Low Magnification Pathology Region-of-Interest Images

Deep Neural Networks (DNNs) have successfully demonstrated superior overall performance in many image classification and recognition tasks on Hematoxylin and Eosin stain (H&E) histology images. Reported studies typically utilize high quality (20x or 40x) Whole Slide Images (WSIs) to deliver optimal performance. However, it remains uncertain how well DNNs can perform on lower quality Region of Interest (ROI) histology images in real-life scenarios. The NCI Patient Derived Models Repository (PDMR) database hosts a catalog of low magnification (4x) ROIs of tissue histology images across a total of 60 cancer models, providing an ideal test case for evaluating DNNs performance in real-life scenarios. Using five pre-trained models, we have benchmarked the NCI PDMR database ROIs on a selected set of popular DNN classifiers. Overall, on the binary carcinoma vs. sarcoma classification test, we have reached 89.57% accuracy on 4x ROIs using our downsizing models and 84.18% accuracy on 4x ROIs using our patch-based models. On the multi-class carcinoma classification test, we have reached 72.06% top-2 accuracy on 4x ROIs using our downsizing models and 78.07% top-2 accuracy on 4x ROIs using our patch-based models. Given that pathologist accuracies hover around 85% [26] [27], our models were comparable in performance. With such accuracies, we can utilize our DNNs to perform crucial tele-pathological tasks in underdeveloped countries and rural areas, enabling scientists to take a cell phone picture and feed that image into a battery powered small computer for rapid screenings on the field.
Requirements on Dimensions for a Maturity Model for Smart Grids Based on Two Case Studies

This contribution describes two different types of requirements engineering analysis of the necessary dimensions of a possible maturity model for Smart Grids to be implemented for utilities. For the first case study, the requirements engineering for necessary dimensions for a Smart Grid maturity model was elicited using a systematic literature research. On the contrary a more agile approach is used for the second requirements engineering. For this more agile approach, interviews with energy suppliers were conducted, taking into account the analysis of the literature research. Various energy suppliers from Germany took part in the survey. The results were used to develop the basic framework for a maturity model for Smart Grids, which can still be tailored if necessary. Finally, future research activities for the application and further development of maturity models for Smart Grids in the energy industry are explained as well as the different procedural variants in the requirements analysis.
Unusual Behavior Analysis of Virtual Machine in Cloud Environment

Cloud computing is a model for enabling the resources to access over the internet, rather than managing files on local storage. Virtual machine is the abstraction of hardware of a distinct Personal Computer into numerous diverse implementing environments. Virtual machines are also known as virtual servers, full-fledged computers, but it is all virtual or simulated, not physical. They have operating systems, files and applications. Cloud service provider offers virtual machine to be used by any user. So, any user can gain control of virtual servers. Unusual behavior of virtual machine may occur in the cloud environment. The proposed system identifies the virtual machine theft attack, which discusses about Spectral Analysis and Energy Calculation which avoids the attackers to stay in the Virtual Machine for a long Period and paying less amount for the service time providers. Hence the total estimation time and Energy might be calculated to overcome the loss for actual time. We proposed and implemented using JAVA software with an API, which identifies and predicts the virtual machine theft attack by Spectral Analysis of Virtual Machine Activities and Energy Calculation.
Evaluation and Improvement of E-toll Card System at Suramadu Toll Gate

Abstract The research was conducted to the manager or operator of toll road and e-toll users to find out what problems happened in transactions with e-toll cards. The purpose of the E-Toll Card is to simplify transactions and to unravel the traffic jam at the Suramadu toll gate. The methods used were the critical incident technique (CIT) and importance performance analysis (IPA). The research result in Suramadu toll road is 7 attributes that need to be improved. Evaluation and improvement of toll road usage system resulted that queue of vehicles at Suramadu toll gate was caused by the lack of socialization, lack of signs along Suramadu road access and lack of toll gate on Suramadu toll road. The toll road management is also required to provide a place for top up e-toll cards around Suramadu access. The toll road management also opens the e-toll card complaint system to the complaints of e-toll card users with the online system and also to reduce transaction errors on toll roads.
Teaching and Develop Pronounciation Using Media Podcast in Senior High School

A wide range of English teaching and learning approaches have emerged due to information and communication technology advancement.  According to Constantine (2007), Podcast was first known in 2004, and it is defined as an internet audio blogging or internet audio publishing. Research on podcasting pedagogy suggests that podcasting helps learners boost their English language skills and support areas such as grammar, pronunciation, and vocabulary. The results of the research consist of  180 students of grade eleven at SMA 1 MAJA and consisting of 6 classes. To determine the sample of this research, the writer used Cluster Random Sampling.  The result of the highest score of the pre-test was 75 and the post-test was 90. Learning through podcasts as a learning tool that can attract students' attention and provide information, knowledge and enrich vocabulary.
Reduction in Complexity of Anti-jamming with Interference Approximation and Classification Algorithm

Interference Approximation and Classification (IAC) is a modified frequency estimation technique for narrowband interference. Improvement in the performance of an anti-jamming with ICA is presented especially for GPS L1 and NavIC L5 band under Continuous Wave (CW) and chirp jamming scenario. IAC is implemented by dual approximation of interference in which frequency and sweep period are calculated using the Adaptive Line Enhancer (ALE) and Low Pass Differentiator (LPD). However, a Pattern Enhancement Technique (PET) is used to classify interference. After classification, only the chirp type jamming signal is further refined by the Kalman filter. As Kalman filter is activated only for chirp, the execution time and so complexity is reduced. The advantage of such an algorithm is to effectively classify the interference, and remove it by adjusting the optimal width of the notch filter.
You and Your Autopilot – How Consumers Perceive Semi-autonomous Cars

While autonomous vehicle (AV) technology has gained increasing attention, literature on consumers’ adoption behavior towards AV is still limited. In this article, we explore consumers’ perceptions of a real-life, on-road experience with a SAE Level 2 semi-autonomous car and the influence on drivers’ trust beliefs and appraisal of autonomous cars. We propose that acceptance of AV technologies is based on a holistic experience consisting of cognitive absorption, cognitive appraisal, and affective appraisal.
Towards Cooperative Sustainability

The inescapable question in the ENVRIplus project is how to sustain all achievements after the end of a collaborative project. Considering that each individual research infrastructure as cooperating in the ENVRIplus project has its own legal entity with dedicated governance and management, the challenge is to agree on modes of cooperation to keep tools and services of common interest up to date and operational. This chapter starts with the views of stakeholders, more specifically the views of scientific bodies, policy bodies, and of the infrastructure managers and operators who have to keep a lot of balls in the air. The sustainability plan has to consider the influences from external developments such as the European Strategy Forum on Research Infrastructures (ESFRI), and the emergence of a European Open Science Cloud (EOSC). The chapter discusses ENVRI strategic views, position, and future challenges.
Analyzing Performance of Apache Spark MLlib with Multinode Clusters on Azure HDInsight: Spark-Perf Case Study

In this paper, we present an analysis and results of experimental research into determining the performance of solving machine learning problems via the library Apache Spark MLlib for the ecosystem Microsoft Azure HDInsight with the help of the test dataset Spark-Pref. In order to solve the defined problems, software and information support methodology have been developed based on the monitoring system SparkMeasure and Ambari. Metrics have been suggested for analyzing the performance of Apache Spark computations. These metrics use statistical characteristics of learning and testing processes when benchmark Spark-perf tests are carried out. There have been suggested formulas for determining settings for Apache Spark parameters. These formulas provide a time minimization as compared to the standard values of Spark parameter settings for executing sets of machine learning test tasks for heterogeneous and homogeneous cluster configurations of Apache Spark Azure HDInsight. In order to assess computing performance for machine learning methods in Spark-Pref a metric has been proposed, which is calculated as the ratio of the average testing time and the average training time. The results of the computational experiments have been demonstrated. They confirm the effectiveness of the proposed algorithms for Apache Spark settings relative to the standard values for heterogeneous and homogeneous clusters deployed on the platform Apache Spark Azure HDInsight, machine learning methods for a Spark-Pref test set being implemented.
Analyzing User Behavior and Sentimental in Computer Mediated Communication

Social media platforms are witnessing significant growth in both size and purpose. One specific aspect of social media platforms is sentiment analysis, by which insights into the emotions and feelings of a person can be inferred from their posted text. Twitter is a widely known social media platform in which users from different cultural and linguistic backgrounds. It is also valuable in studying a large amount of data and turning it into valuable information that can be useful in decision making. This study aims at investigating the sentiment orientation of the textual features as well as the emojis-based features of Arabic comments posted on social media, Twitter, during the World Cup 18 event. It also measures whether different results will be obtained when the text is analyzed in isolation from emojis. This paper presents the approach that involves extracting thousands of tweets, implementing the sentiment analysis, on the texts, and the emojis separately. This analysis has been conducted once via a using an artifact and once again manually, then a comparison between the results is made. This comparison shows that emojis support the sentiment orientation in the text. However, it can be relied solely neither on the text nor on emojis as they complement each other.
Is Whole Object Information Helpful for Scene Recognition?

Scene recognition is one of the visual tasks, classifying a place category on an image. Scene images may contain various objects, and these objects tend to become clues to recognize the scene of the image. Therefore, many previous approaches for scene recognition use the object information that appeared in the image to improve the performance. Here, we raise a question of whether whole object information is helpful for scene recognition. To find the answer to the question, we conduct experiments on Places365, which is the largest scene recognition dataset consist of real-world images. To find the object classes which disturbed scene recognition, we utilize the Class Conversion Matrix, which is a deep learning approach. Finally, we found that some object classes may contribute to disturbing scene recognition. It indicates that not only making good use of object information, but also dropping disturbed object information is also important.
Optimizing a Specialized Convolutional Neural Network in a Supercomputing Environment Using Tensorflow and VisIt

We describe hyper-parameter optimization and training for a novel convolutional neural network that identifies neural axons in 3D volume electron microscope data for a larval zebrafish. Training a convolutional neural network to detect image components in three dimensional space has a variety of applications. There are many parts of a network that can be tuned to work most effectively in a supercomputing environment. We optimized run parameters including batch sizes, thread counts, and epoch sizes as well as hyper-parameters such as drop-out rates. Once we had improved the speed and accuracy of the network we created a large three dimensional volume to visualize the errors of the network. We added new samples to our training set based on the larger volume and improved the accuracy of the network using those samples. Adapting the network to our supercomputing environment and adjusting the dropout layers increased the accuracy and efficiency of the network. Being able to accurately detect neurons in three dimensional space shows potential for other deep learning applications.
Clustering Pollutants Concentration from Sumatra Peat Fire using Fuzzy C-Means Algorithm

Abstract Indonesia has large peatland areas which can significantly impact both regional and global environments. The forest and land fire in 2015 produced high level of air pollutants concentration in Sumatra, especially in South Sumatra, Riau, and Jambi provinces. This study aims to cluster pollutants concentration from peat fires in Sumatra during July until November 2015 using fuzzy c-means algorithm. The dataset used in this study is concentration of pollutants including CH 4 , CO, CO 2 , NH 3 and PM 2.5 . Based on the experimental result, pollutants concentration data are grouped into 8 clusters. The result of this study indicates that the highest pollutants concentration is found in the cluster which has only one member with concentration 14,500 µg/m 3 of CH 4 , 145,980 µg/m 3 of CO, 1,183,831 µg/m 3 of CO 2 , 13,800 µg/m 3 of NH 3 and 6,290 µg/m 3 of PM 2.5 . Another cluster containing high concentration has 6 objects representing haze locations with the average concentration 8,265 µg/m 3 of CH 4 , 83,427 µg/m 3 of CO, 676,558 µg/m 3 of CO 2 , 7,913 µg/m 3 of NH 3 , and 3,597 µg/m 3 of PM 2.5 . The largest cluster with 590,816 haze locations contains the lowest concentration of pollutants. Although its average concentration levels are low, yet the maximum concentration are still in moderate or high enough levels. In general, high concentration of pollutants was distributed mostly in South Sumatra, Jambi, and Riau.
Using mobile applications as a marketing tool in agribusiness

Studies have been conducted on current problems and prospects of using mobile Apps in Ukrainian agriculture. The mobile application market for agricultural businesses is analyzed along with their main features and benefits. The main limiting factors for the use of mobile applications in the agrarian sector of Ukraine are identified. The positive changes that are expected in this area as a result of the development of a national program for the introduction of digital services in the country, the development of infrastructure and the spread of high-speed mobile communication have been noted. Mechanisms of functioning of mobile applications as a marketing tool are defined. The main marketing goals of using the Mobile App in the agricultural sector, as well as the stages of their introduction to the market are highlighted. The basic types of mobile applications for different platforms, their advantages and disadvantages, evaluation and selection criteria are described. The hybrid cross-platform development is the most optimal method, than the simultaneous native development of separate applications for Android and iOS. Key advantages include: cost-effectiveness, ease of development and use of tools, use without internet access, user-friendly user interface, quick installation. Examples of software products that are implemented as marketing tools for promoting food products, as well as products and services for agriculture that can be used to support management decisions in agribusiness. The multi-purpose nature of mobile applications be emphasized, which become a competitive advantage of the company and not only increase customer loyalty, that receive useful information and practical management tools, but also provide growth of sales and formation of stable partnerships.
Personalized group of readers' emotion traits modeling based on sentimental analysis of novels

Studies concerning sentiment analysis haven drawn attentions from a wide spectrum of researchers in the past few years. One of the most eye-catching topics within this emerging field is how emotion can be dectected and extracted properly from the given contexts. This study chooses content from novel to stimulate above mentioned long contexts, and expects analysis results can be projected to a three-dimensions space model. Two major tasks are conducted. First, specific content selected from novel is proceeded through morpheme analysis to estimate emotion status. Second, obtained emotion status is calculated through existing emotion dictionary which was built based on data from the novel. Results are then demonstrated on the three-dimensions space model. At last, two experiments, a systematic way to compare emotion status and an empirical questionnaire, are conducted to verify the performance of proposed method.
Neural network implementation in Java

Artificial neural networks are a powerful tool that engineers can use in variety of purposes.The most common tasks are classification and regression, as well as control, modeling and prediction.For more than three decades, the field of artificial neural networks has been the center of intensive research.A large number of software tools have been developed to train these types of networks, but there is still interest in implementing neural networks in different programming languages.This paper aims to present the implementation of an arbitrary neural network in the Java programming language.
Formation of professional competence in future teachers of preschool and elementary general education

The changes taking place in the modern world dictate to society new requirements for the training of specialists in the field of teacher education. Training professionals in their field is not an easy task. In order to ensure the professional competence of the future teacher meets the challenges of modern society, it is necessary to provide training at the stage of study at the university. The article discusses the formation of professional competence of future teachers of preschool and primary general education, describes the theoretical aspects of studying the essence of this problem. On the basis of the study, by means of a questionnaire survey, the main components of the successful formation of future professional competence in future teachers of preschool and primary general education are analyzed.
Development of Multifunctional Calculator Based on MCU (Microcontroller Unit)

The ordinary simple calculator can only perform some basic calculations. It will be helpless to meet some complex ones, while the multifunctional calculator can perform these calculations. This design uses the STC89C52 MCU (Microcontroller Unit) as the core of the multifunctional calculator. The input uses a matrix keyboard consisting of 8 rows and 5 columns. It can perform 19 kinds of calculation functions such as addition, subtraction, multiplication, division, square, extraction of a root, N-th power, extraction of an N-th power, sine function, cosine function, tangent function, logarithmic operation, and finally display the calculation result on LCD1602.
The potency of the efficiency and compression rate of Cirebon language script using the Binary Huffman algorithm

Abstract Each language usually has several important features that are hidden statistically and certain redundancy. These features can be utilized to perform text compression that is suitable for the optimal use of resources. This study proposes an analysis the potency of the efficiency and compression rate of Cirebon language script using the Binary Huffman algorithm. The analysis of the potency is based on the entropy of the Cirebon language script. The study begins with an analysis of the Cirebon language script to calculate the probability of each symbol. These probabilities are used to calculate the value of entropy. The results showed that the entropy of the Cirebon language script was 3.976 bits per symbol, with an expected code length of 4.02 bits per symbol. Then, the estimated efficiency of compression with the Binary Huffman Code is 98.89% and the compression rate is 0.80402. These results can be considered in the use of Cirebon language compression for the telecommunications transmission process.