Reviewerly/exHarmony huggingface dataset:
Reservoir computing with inertial manifold

We propose a spatiotemporally continuous framework of reservoir computing that has an inertial manifold. Using the theoretical results concerning the infinite-dimensional dynamical systems, we first propose a new model that guarantees the existence of a strong solution. Then, we prove that the proposed model actually has an inertial manifold, which is easier to deal with in practical situations. Furthermore, we also prove the following: given two input signals tending to different stationary states as time infinitely elapses, the corresponding stationary state of the reservoirs tend to different states.
Design of electric guitar 8 strings with combination of smartphone devices as a virtual guitar effect using reverse engineering method

Electric guitar is an instrumental instrument that is widely used in music, because with an electric guitar the music played will be more varied. That happens because of the supporting devices, namely the effect of the pedals that are used as setting the sound variations of the electric guitar being played. With the development of smartphone devices and applications on the Android and IOS platforms, the application developers made a new breakthrough by creating a virtual application of guitar effects that could be used like a pedal effect with sound results not much different from the original. then, electric guitars for now, still rely on the number of 6 strings, but some guitarists cannot reach out to the music genre that requires low and heavy tones, therefore, it is necessary to add 1 or 2 strings in order to get a low tunning sound character and increasing the number of frets on the fretboard to 30 frets, the more new tones that can be explored. Then, carried out with a reverse engineering method approach, to combine a smartphone device as a virtual effect on an electric guitar that can include several existing guitar concepts into an electric guitar unit that will be able to answer the wishes of progressive guitarists. And it was concluded that the electric guitar made meets the criteria such as low-tunning sound character, articulation and playbility aspects. then, the electric guitar manufacturing process was carried out by Luther Wira Musica, a home industry scale.
The development of Chinese grammar checker website based on Natural Language Processing

Abstract Research on artificial intelligence in learning Mandarin in Indonesia is still very rare. The research on developing a Chinese grammar checking website is aimed at helping Mandarin learners in mastering Mandarin grammar. This website can be a “learning companion” of basic-level Mandarin learners outside the classroom. This technology is still very new, so this research should be carried out immediately by researchers in Indonesia to master this technology. The method used in this research is a quantitative approach. The collection of grammar patterns that will appear on the chineseteak.net will be selected from several Chinese textbooks. Then the pattern will become Mandarin grammar patterns and will be entered the website auto-correct grammar. This website is designed using PHP and MySQL as the database. From the results of the questionnaire, we can find out that there are 7 points of Mandarin grammar that are still difficult for early learners.
Image Processing based Linear Discriminant and Quadratic Discriminant Classifier for Feature Extraction Models

An automatic method for classification of RBCs is discussed in this paper to overcome the limitations of manual analysis. This is helpful for fast, precise and efficient analysis of blood cells in order to diagnose diseases. Wavelet transform and thresholding techniques are used for feature extraction. Two classifiers and three combining rules are implemented with PR Toolbox. LDC and QDC classifiers are used separately and then combined. Highest accuracy of 85% is obtained by LDC. Meanc Productc combiner rule gives highest accuracy of 90%. Source coding is performed in MATLAB R2016a.
Authenticating Cryptography over Network in Data

The instantaneous growth for the world being digital is- "Expeditious growth of a network system and high-speed internet". This leads to both ease as well as security threats to the users. Network security issues can broadly be classified into two main categories: System security and Data security. The system security issue protects the system from unwanted attacks by malicious threats. Data security is protecting the leakage or modification of data from the unauthorized party in the network. The main goal and sole purpose of the network security are to achieve Authenticity, Integrity, Availability, and Confidentiality in the wireless network. This paper mainly instigates with all the network security issues and classify various encryption technologies, tools, and keys used in the network for ensuring Confidentiality and Integrity over the network.
An FPGA based implementation of the Conjugate Gradient Kernels

The following topics are dealt with: learning (artificial intelligence); feature extraction; support vector machines; pattern classification; medical signal processing; regression analysis; diseases; antenna radiation patterns; image classification; cancer.
PNCC for forensic automatic speaker recognition

Forensic Automatic Speaker Recognition (FASR) is a process for making judgments on whether a particular speech utterance belongs to a suspected speaker. This is a challenging task as the sample tested may be received from different channels and noise conditions or may be disguised. Robust feature extraction plays an important role in improving the performance of FASR. In this paper, a forensic automatic speaker recognition system is implemented which exploits Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) features. Performance of the system is demonstrated on a Malayalam speaker database. The speaker recognition framework is based on conventional i-vector based system. Experimental results suggest that the PNCC features provide slightly inferior performance with the MFCC features while tested under the conditions namely, masked speech, telephone speech, and Voice over Internet Protocol (VoIP). But, it is also observed that PNCC provides better performance than MFCC in noisy conditions.
Deep Vision for Navigation of Autonomous Motorcycle in Urban and Semi-Urban Environments

Deep neural networks are currently the best solution for road and traffic scene interpretation for autonomous and self-driving vehicles. Compared to the autonomous cars, motorcycles have significant flexibility and advantages in crowded traffic situations and especially in non-urban and off-road areas. Many off-road tracks especially for agriculture and environment management tasks are only traversable with motorcycles. In this paper, a deep neural network is used for design and implementation of the vision system for navigation of an autonomous motorcycle. The proposed framework is evaluated using real world scenarios captured by a real motorcycle in various complex situations. The experimental results show that the proposed framework is capable of highly accurate interpretation of various environments for autonomous navigation of a motorcycle.
Study on inertia weight decreasing strategy of particle swarm optimization based on inverse coseca

Abstract The standard particle swarm optimization (pso), which introduces inertia weight w, is an effective method to find the extreme value of the function. However, particle swarm optimization (pso) has some disadvantages. When dealing with optimization problems, pso lacks effective parameter control and is prone to fall into local optimization, which leads to low convergence accuracy. In, this paper, put forward a new improved particle swarm optimization (pso) algorithm, The nonlinear decreasing inertia weight by the CSC function strategy, at the same time to join the beta distribution on random Numbers, thus to balance the global search and local search ability of the algorithm. The learning factor is the changed asynchronously to improve the learning ability of the algorithm. By adopting Griewank, Rastrigrin, J.D. Schaffer three standard test functions to simulate experiment, at the same time and the basic particle swarm algorithm the inertia weight in a fixed value, the linear regressive LDIW and nonlinear regressive comparison. The experimental results show that the nonlinear decreasing strategy with dynamic adjustment of inverse cosecant function can improve the convergence speed and stability.
Data Driven Analysis for Web Service Selection

Since web services are among the basic cloud technologies, the problems related to their quality remains of a primary importance. An open question is how to provide services that can justifiably be trusted and at the same time acceptable and usable by the clients. The aim of the present paper is to reveal three conceptually different data-driven approaches for quality of service (QoS) assessment based on quality metrics data. A comparison analysis of the described assessments provides a solid base for reliable QoS service selection. Software tool that enables the three assessments is developed. It ensures a general overview of the compared services according to their quality. Analysis of advantages and drawbacks of the estimations is done both on theoretical perspective and through application by the tool towards real time data of two web services of weather forecasting.
The Comparison of Light Compensation Method and CycleGAN Method for Deep Learning Based Object Detection of Mobile Robot Vision Under Inconsistent Illumination Conditions in Virtual Environment

The visual system of indoor mobile robots is mainly used for object detection, recognition, tracking, positioning and navigation. However, the effect of object detection of robot is easily affected by the outside world such as: illumination inconsistent, object occlusion, and so on. In order to explore and solve the effect of illumination inconsistent on object detection algorithm(YOLOv2) of robot vision, the virtual environment was used to simulate the real indoor environment, because the conditions are not easily controlled and susceptible to external influences for object detection in the actual indoor environment. And the low confidence of object detection of robot vision was studied and verified in the dark environment. Meanwhile, the light source was added to the robot to solve relevant problems and specific method of brightness migration based on CycleGAN is found after analyzing the principle. Finally, the method based on CycleGAN is applied to robot to improve the detection effect in dark environment.
Reliable Visual Analytics, a Prerequisite for Outcome Assessment of Engineering Systems

Various evaluation approaches exist for multi-purpose visual analytics (VA) frameworks. They are based on empirical studies in information visualization or on community activities, for example, VA Science and Technology Challenge (2006-2014) created as a community evaluation resource to 'decide upon the right metrics to use, and the appropriate implementation of those metrics including datasets and evaluators'. In this paper, we propose to use evaluated VA environments for computer-based processes or systems with the main goal of aligning user plans, system models and software results. For this purpose, trust in VA outcome should be established, which can be done by following the (meta-)design principles of a human-centered verification and validation assessment and also in dependence on users' task models and interaction styles, since the possibility to work with the visualization interactively is an integral part of VA. To define reliable VA, we point out various dimensions of reliability along with their quality criteria, requirements, attributes and metrics. Several software packages are used to illustrate the concepts.
Deep Learning Approach to Face Pose Estimation for High-Speed Camera Network System

Three-dimensional face pose estimation has been vastly researched in computer vision, as the face recognition techniques can be utilized in tremendous applications not only regarding human behavior monitoring but also about human-computer interaction. In this paper, we attempted to build a deep-learning model which classifies the pan angle of human head by directly applying convolutional neural network without preliminary image processing, for low-resolution face images. In comparison with the transfer learnings based on pre-trained model, customized simple model consisting of a few convolutional layers and dropout scheme showed an enhanced accuracy in face pan angle prediction.
Joint Feature-level and Pixel-level Domain Adaption for Object Detection in the Wild

The obeject detector trained on a source dataset probablely fails to be generalized well to a target dataset due to the domian shift between different distributions. This paper aims at reducing such domain gap without any available annotation of the target domain. It is achieved through a novel unsupervised domain adaption algorithm which improves the robustness of Faster R-CNN on two levels: 1) on the feature level, a domain discriminator is appended to the feature extractor of Faster R-CNN; 2) on the pixel level, multiple feature maps from the feature extractor are connected to a generator which feeds the fused features in a form of pictures to a later domain discriminator. When used alone, either one can improve the performance in a adversarial training manner, the former acheives a high precison-rate while the latter gains a high recall-rate. While applying both components can play the role of complementary advantages. After domain adaption, the accuracy of Faster R-CNN on the publicly used Virtual KITTI dataset increases from 51.2% to 69.5%, the accuracy on the publicly used Foggy Cityscapes dataset increases from 20.2 % to 30.3 % which outperforms the state-of-the-art method by 1.3 % in the accuracy gain.
Evaluating data visualization : Broadening the measurements of success

This chapter investigates the evaluation of data visualizations using observational research in an award-winning design studio. It outlines some professional and commercial forces that are involved in the shaping of evaluative strategies and identifies differences in methods and forms of evaluation in projects with different aims and intended audiences. The research showed that alongside quantitative headline figures of consumption, such as audience reach and interaction, qualitative measures of audience experience—which consider the sociocultural context of consumption—were sometimes included in evaluation strategies, but this varied between projects depending on the level of access to, and knowledge about, the audience. This chapter highlights the importance of such measures, outlines attempts to develop them, and comments on the potential to do so.
Implementing 60-Year Curriculum Learning at the Harvard Division of Continuing Education

The mission of Harvard’s Division of Continuing Education (HDCE) is to provide open access to high-quality affordable courses, certificates, and degrees for adult learners. Implementing the 60-Year Curriculum (60YC) at HDCE has been, and will continue to be, a work in progress with both successful innovations and missteps. Thus far, it has involved successive strategic planning exercises to guide an evolution from a lecture-oriented pedagogical model with administration-centric services to supporting multiple online and hybrid pedagogical models with learner- and faculty-centric services. The technical infrastructure is changing from modifications of existing management information systems and a suite of in-house applications to a relationship-management infrastructure and open source and commercial applications. The on-going 60YC planning and development efforts involve shared work by learning engineers, IT developers, and administrators. HDCE’s 60YC infrastructure allows us to conduct experiments to test learning experiences, new infrastructure tools, and administrative processes in response to evidence of learner success, faculty satisfaction, and operational efficiency.
Bayesian Inference and Learning for Neural Networks and Deep Learning

Bayesian statistics has a lot of influence on neural networks and deep learning for artificial intelligence (AI). The inference and learning of Bayesian statistics is based on prior, likelihood and posterior. The prior is the current belief of data field and the posterior is the updated belief after learning from observed data. By repeated learning using prior and posterior distributions, Bayesian statistics provides advanced data learning for AI. In this paper, we compare the previous Bayesian inference and learning methods for AI and propose a model based on Bayesian inference and learning for neural networks and deep learning.
(Re-) Presenting an altered interactive system. On et ego, a piece for classical guitar, two piezoelectric microphones and electronics

INTRODUCTION: Starting from et ego, a piece for classical guitar and electronics, this paper aims to introduce the notion of the interactive system as a tool to understand the musical activity developed by a composer and/or performer in relationship with an instrument and a score. OBJECTIVES: T
The Innovation Development of Early Flash Flood Warning System Based on Digital Image Processing through Android Smartphone

Abstract Flooding is a phenomenon that almost always occurs every year in various regions throughout Indonesia with a variety of cycle variations. In the rainy season, high rainfall and narrowed watersheds and silting up are no longer able to accommodate the flow of water, causing flash floods. Flood Warning Systems operate with traditional local wisdom that is highly dependent on the availability and responsiveness of the personnel assigned to oversee the river. Therefore, the development innovation of the existing system is expected to be able to provide information and data regularly and be an early warning to flash floods in order to minimize the impact. This research was conducted to design, create and implement an image-based flash flood early warning system prototype based on an Android cellular phone so that the public is expected to be able to access information on possible flash floods through their respective cell phones. Image processing is the basis of this development innovation through the Background Substraction method which is carried out to automatically detect High Levels of Water (TMA) on the control board (Peil) via an IP Camera which is an input parameter for the system and is processed into information for interested parties. The results of analysis of the detection of water level (TMA) on the peil board carried out on the server computer, become input parameters for determining the status of the dam water level. Flood early warning application that is installed on Android smartphone devices receives data from the server computer via the internet network.
Adaptive computer training system GraphLabs

A modular computer system provides laboratory work on graph theory. Creating an option for a student takes into account the student model. The student model is build and modified during laboratory work. An automaton model is used to build an individual work option for a student.
Analysis of Random Number Generators Based on Chaos-Modulated Dual Oscillator Architecture

This paper presents a numerical analysis of random number generators based on chaos-modulated dual oscillator architecture. Chaotic sampling of a regular clock waveform method is applied to produce random bits. A double-scroll chaotic system is chosen as the chaotic oscillator and it is simulated in normalized time domain to generate the chaotic signal. The chaotic signal is then used to modulate the output of voltage-controlled oscillator (VCO) model which is utilized for sampling a regular signal. Applying the concepts of autocorrelation and approximate entropy to the resulting RNG bitstreams, the impact of frequency ratios on the output randomness and the robustness of the RNG against variation in chaos-controlling parameters is analyzed. The limitation on the maximum throughput that can be extracted from a RNG based on chaos-modulated dual oscillator architecture is discussed. Design guidelines are developed for building RNGs based on chaos-modulated dual oscillator architecture.
Analyzing and Enriching English Language Proficiency in Children Using Interactive Storybook

This research is aim to develop web application that can motivate to learn reading storybook in English among children. The application uses storybook, interaction during the reading and timing to measure the reading process. The result shows this approach can help children to learn word better and enhance language proficiency. The research was conducted in Management and Science University (MSU) by applying the survey. There were 43 respondents from students in MSU. The result shows that many students think that interactive storybook can help children to learn word better and enhance language proficiency.
Information Retrieval Technique for Indonesian PDF Document with Modified Stemming Porter Method Using PHP

Abstract Finding relevant information from a collection of information requires a process of stemming. Stemming is the process of combining or solving each morphological variants of a word into a basic word. Based on the basic structure of the word morphology, Porter’s stemming looks appropriate to be applied in conducting basic word searches in Indonesian-language documents, but with a few modifications. For this need, an Information Retrieval Technique for Indonesian PDF Document Application Using PHP from Indonesian documents is made using the Modified Stemming Porter Method. Implementation of the application was carried out using the Php (Hypertext Preprocessor) programming language. Testing was performed on 26 pdf e-book documents are 23,197 basic words out of 28,532 total words. the experiment found 94% as the largest percentage of precision words in the document. And the results obtained 81% as the lowest percentage of the basic words that are precise in the document. The results obtained from the test are that the application can operate well in conducting stemming on e-books in Indonesian.
Utilization Of Social Media As A Learning Media For Promoting Sampit Language

Abstract Nowadays, social media not only functions as a medium for self-promotion and socializing. The emergence of many social media-based learning platforms opens up opportunities for the development of learning media. Social media is liked by all groups, easily accessible and inexpensive to become its strength, so it is very strategic if used as a learning medium. Sampit language itself is a language that is owned by one of the Dayak tribes in Central Kalimantan. Some facts on the ground show that the existence of the Sampit language is currently under threat. Through the use of social media as learning media, the Sampit language has begun to be looked at again by young people. Social media used as learning media here are Instagram and YouTube channel and learning material that is packaged in the form of videos and images. The research aimed at knowing the utilization of social media for promoting social media.The type of research used in this study is quantitative descriptive research, with data collection methods using questionnaires and observations. The results of this study indicate that the use of social media can increase the popularity of the Sampit language.