Reviewerly/exHarmony huggingface dataset:
Comparison of Discriminant Analysis and Adaptive Boosting Classification and Regression Trees on Data with Unbalanced Class

This study aims to determine the best classification results among discriminant analysis, CART, and Adaboost CART on Bank X's Home Ownership Credit (KPR) customers. This study uses secondary data which contains notes on the 5C assessment (Collateral, Character, Capacity, Condition, Capital) and collectibility of current and non-current loans. The sample used in this study was from 2000 debtors. Comparison of classifications based on model accuracy, sensitivity, and overall specificity shows that Adaboost CART is the best method for classifying credit collectibility at Bank X. This is due to the class imbalance in the data. This study compares the classification results between parametric statistics, namely discriminant analysis and non-parametric statistics, namely CART and Adaboost CART. The results of the research can be used as material for consideration and evaluation for banks in determining the policy for providing credit to prospective borrowers from the classification results of KPR Bank X consumers.
Indonesia Expressive Text to Speech System based on Global Style Token and Tacotron 2

This research aims to construct an Expressive Text to Speech (TTS) system in the domain of Indonesia Language. Tacotron 2 is used in this study with Global Style Token (GST) as an additional feature and Parallel WaveGAN as a vocoder. Linguistic features are extracted from input text using Encoder in Tacotron 2. Those features are combined with GST which acts as emotion representation features extracted from reference audio. Combined features are processed by Decoder in Tacotron 2 model to produce Spectrogram which will then be processed by Parallel WaveGAN to finally produce expressive output audio. Both model GST + Tacotron 2 and model Parallel WaveGAN are trained using the same expressive corpus. The expressive corpus is constructed with 11.482 pairs of text and audio with 21 hours 57 minutes total duration. That expressive corpus contains angry, happy, sad, and neutral emotions. GST + Tacotron 2 model compared with baseline model, a Tacotron 2 architecture alone without Global Style Token and combined with Parallel WaveGAN as a vocoder. Both models are tested using Mean Opinion Score (MOS) and AB Testing. GST + Tacotron 2 model produce $3.90 \pm 0.07$ for MOS score. Higher than baseline model with $3.33 \pm 0.10$ MOS score. Respondent's preference from AB Testing shows that most of the respondents chose GST + Tacotron2 Model (65.93%) than Baseline Model (34.07%).
Characterizing the Evolution of Size and Inheritance in Object-Oriented Software

Software evolution consists of adapting, correcting, and updating a system. It is widely known that the systems became increasingly complex and challenging to be maintained over their evolution. However, the way the systems’ internal structure evolves is not known in detail. Understanding how the internal software structure evolves is essential to help developers better plan, manage, and perform software maintenance tasks. In this work, we present an empirical analysis to investigate how the internal structure of object-oriented systems evolves from the perspective of inheritance hierarchy and size. Besides, we analyzed the set of classes within the systems that affect these dimensions’ evolution and how such classes evolve. We used a methodology based on time series, linear regression techniques, and trend tests to analyze the evolution of object-oriented systems. Using this methodology, we identified the function that better explains how the size and the inheritance tree evolve. This study revealed eight software evolution properties, among them: inheritance hierarchy tends to increase in depth and decrease in breadth; inheritance hierarchy depth and the size of classes grow according to a linear model. The empirical evidence found in this work provides a fine-grained knowledge of how object-oriented software systems’ internal structure evolves from internal dimensions.
IDNat-Blockchain: A Concept for Indonesia's National Blockchain

Blockchain gives a compelling way of making government administrations more effective, but standardizing the administration framework, forms and obligation for the application is vital for it to encourage advancement. This article, by giving an investigation of the hone of blockchain in e-government in Indonesia, may serve as an establishment for advance commonsense work and hypothetical investigation in government administrations. The purpose of this article is to propose a concept for the Indonesia&#x0027;s National Blockchain concept which fit to be implemented in Indonesia called IDN at-Blockchain which is derived into three blockchain; (1) IDGov-Blockchain, (2) IDVII-Blockchain, and (3) IDPub-Blockchain. Our research found that blockchain technology can bring the taking after benefits: (1) enhancements within the quality and amount of government administrations, (2) more noteworthy straightforwardness and openness of government data, (3) improvement of information-sharing over distinctive organizations, and (4) help in building a person credit framework in Indonesia. Be that as it may, data security, fetched, and unwavering quality are still major issues in the application. In this way, building up a common application stage of blockchain innovation and creating administration benchmarks are significant for advancing and applying blockchain in e-government.
Certain investigations on image segmentation algorithms on synthetic aperture radar images and classification using convolution neural network

Abstract Synthetic aperture radar (SAR) image segmentation is the most important step in detecting the land and sea areas in many applications like military in particular. This article experiments four prominent image segmentation algorithms on SAR images and we propose a hybrid method to segment SAR images, which shows better efficiency. First among them is OTSU thresholding method that segmented the image with less interest in the texture component. Further, adaptive thresholding, artificial bee colony segmentation, and wavelet transforms were experimented; wavelet transforms give better segmentation results compared to thresholding methods. In this article, wavelet along with masking threshold including segmentation method is proposed, which gives a better result based on texture and executes in almost the same time as other methods and the segmented images give better classification results using convolution neural network when compared to other methods.
An Automatic Paleography Script Recognition System for the Arabic Language based on Fast Independent Component Analysis (Fast-ICA) and Support Vector Machine (SVM)

Paleography is the study of historical writing and its concern with identifying the date, origin, author(s), and other information about a particular script. There are many paleography text analysis system aims to analyze English handwritten text. However, scarcely any of them are proposed to analyze the Arabic handwritten texts. Hence, the Arabic paleographers are forced to manually analyzes the handwritten text. To facilitate and reduce the amount of time required to analyze the scripts for paleographers and archaeologists. An Automatic Paleography Script Recognition (APSR) System for the Arabic language is proposed in this study. The APSR has three main steps: preprocessing, feature extraction using Fast Independent Component Analysis (Fast-ICA), and recognition using Polynomial, Linear, and Gaussian Support Vector Machine (SVM) classifiers. In the proposed system, the images of the scripts are normalized into a uniform size at first. Afterward, the image noise is reduced using Gaussian blur. Subsequently, the script skeleton is extracted using the Erosion operator for feature extraction and selection of the handwritten script using Fast-ICA. The accuracy of the proposed system is evaluated on version 2 of the IFN/ENIT dataset of handwritten Arabic text using Polynomial, Linear, and Gaussian SVM classifiers. Moreover, the accuracy result of the proposed system is compared with the accuracy result produced by a state-of-the-art OHATRS, which is based on Principle Component Analysis (PCA) and SVM classifiers using several normalization sizes of Arabic text images. The experiential result shows the effectiveness of the proposed system compared to the OHATRS model.
On the teaching mode of personalizing reading under the background of "internet +"

Conventional teaching mode of English reading has failed to meet the needs of current college students, thus college English teaching is facing new challenges. With the arrival of the information age, information technology has developed rapidly and the teaching mode of personalizing reading is becoming more and more important. This paper briefly describes the current situation of the teaching of English reading under the background of “Internet +” and explores the teaching mode of personalizing reading from the perspective of teaching objectives, students’ self-learning, the guidance of teachers and multi-mode of evaluation, so as to construct path for the practice of the teaching mode of personalizing reading and promote the personalized development of students in reading.
Pedagogic model of professional training of future music teachers in higher education

The article represents the study of professional training of future music teachers’ preparation. The article is paid attention on analysis of the concept of “professional training” and using innovative integrical technologies, projective technologies and art management technologies. The multi-aspect nature of this phenomenon is shown. The system of professional training, pedagogic model and characteristics of types of training in musical education and results of scientific research are presented. The article shows a modern view of the problem of training future specialists in the educational field of music preparation, where professional training is described as a process aimed more at the formation of knowledge, skills and abilities and the special course “Basic of art management” is presented. Special emphasis is placed on necessity of deepening into projective art-creative activity, presentation and implementation creative projects. In conclusion, the author emphasises the perspective of the “pedagogic model of professional training of futurе musіс tеасhers” tаkіng іntо ассоunt the specifics of professional activity in this field and using innovative pedagogic technologies.
Prediction of Pull Requests Review Time in Open Source Projects

In open-source projects that receive large amounts of pull requests, the tasks of maintaining quality and prioritizing code review have become a complex task. In this sense, several works explored data on pull requests in order to provide useful information. Although, the review time was treated as the interval between the submission and the integration of the pull requests, that is, the lifetime itself. Since December 2016, a feature allows requesting reviews to one or more specific reviewers, which, together with the review status, allowed to establish the period closest to the effective code review time, the interval between the review request and the last review with approval status. In this context, the main objective of this work is to predict the review time of pull requests. Furthermore, the lifetime and acceptance of pull requests with and without review time were compared and the CFS (Correlation-based Feature Selection) attribute selection strategy was used to identify those most relevant to the forecast. The results of the experiments indicate that the SMO (Sequential Minimal Optimization) algorithm had the smallest error, averaging 8,504 minutes (approximately 5,9 days) and that the presence of approvals in the review requests has a positive influence on both the acceptance and in the pull request lifetime.
Person und Gehirn. Zur Kritik des Zerebrozentrismus

No AccessPerson und Gehirn. Zur Kritik des ZerebrozentrismusThomas FuchsThomas FuchsSearch for more papers by this authorhttps://doi.org/10.13109/9783666573279.77SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book coverReligion, Theologie und Naturwissenschaft / Religion, Theology, and Natural ScienceVolume 35 1. AuflageISBN: 978-3-525-57327-3 eISBN: 978-3-666-57327-9HistoryPublished online:December 2021 PDF download
Enhancing label transfer in non-parametric scene parsing by superpixel-based dense alignment

Contemporary (parametric) scene parsing methods are learning-based and mostly operate in a closed-universe scenario. We introduce a non-parametric scene parsing framework that is model-free, data-driven, and scales naturally to growing data. The scene parsing performance in the non-parametric approach depends on reliable dense correspondence or alignment across scenes for label transfer. Incorrect correspondence is known to affect the scene parsing results adversely. We propose a label transfer approach that relies on the dense correspondence of super-pixel pairs (in a query and candidate image) matched by a homogeneous kernel map to guide semantic label transfer. The aggregation (fusing) of multiple labels is done through a simple heuristic aggregation scheme (simple majority voting). The Markov Random Field (MRF) provides a principled probabilistic framework for combining the disparate information in the smoothing stage and ensures plausible labeling results. Evaluation results show that our non-parametric system obtains competitive scene parsing performance on the standard SIFT Flow and MSRC-21 datasets.
Fuzzy Logic Controller Application to an Automatic Corn Sheller

Corn is the common agriculture product in Palembang suburban, which can be utilized to boost the income of local residents. Appropriate technology application is beneficial to improve the productivity of the household scale industry. This paper proposed an automatic corn sheller to apply appropriate technology for post-harvesting of the agriculture industry in the Palembang suburban. The corn sheller design is kept simple to ensure its repeatability. Experiments using the design test-best were conducted to show the effectiveness of the proposed method. Data results show that the sheller effectively follows the FLC design. The average total time of the shelling process is 4.7 s which is fast; therefore, it can improve the productivity of household-scale industries. The data results show that the proposed method is effective in shelling average corn found in Palembang.
An Analisys of the Domain of Information Security Systems

In this article, we complement our method of developing information security system for protection of organization's sensitive information. The method is based on analysis of the domain of problem area “Information Security”. We are taking into account the viewpoints of the different actors in the development process. unifying their requirements and contributions to the system development. Basic concepts, which are foundation of the field of information security are considered - Vulnerabilities, Threats, Sources, Threat Agents, Motivation, Vectors, Targets. Their relations describe the nature of the threats (internal and external), nature of attacks and the counteractions against them. The approaches to information security and the types of communications in the modern organization are presented as well. A brief analysis of the “Information Processing” viewpoint is also made. As a result of the analysis, we determine the main concepts, as well as the relationships between them, describing vulnerabilities, threats, attacks and countermeasures and gaining deeper knowledge about their nature.
Correction of wavelength error of white-light interference phase-shifting algorithm

In phase-shifting interferometry, phase unwrapping is an important algorithm for obtaining actual detection data. In general, fringe contrast is approximated to reduce the difficulty of operation. However, in the actual white light interference detection, the fringe contrast will vary with the change of the optical path difference as the wavelength is more abundant. In this paper, the three-step phase-shifting algorithm is taken as an example to derive the phase expression of the white light interference fringe contrast without approximation processing. A hemisphere with a radius of 10&mu;m and an irregular object with a size of less than 6&mu;m are applied to topography reconstruction. Compared with the topography reconstruction error of the three-step phase-shifting algorithm before and after the correction, it has proved that the three-step phase-shifting algorithm after the correction can increase the reconstruction accuracy by about two times, which provides a theoretical method to improve the reconstruction accuracy of the phase-shifting algorithm.
Global-view based Task Migration for Deep Learning Processor

In order to cope with the deep neural network application characterized by big data and intensive computation and memory access, deep learning processors often adopt NUMA architecture and multi-core architecture to alleviate the bandwidth bottleneck and contention problems existing in a single storage node and improve the parallelism of the system. This design also increases the complexity of task scheduling, considering not only the data affinity but also the utilization of DLP. When the load of DLP is uneven, the existing local view scheduling will make forward view scheduling according to the status of tasks waiting in line, instead of following the principle of data affinity, and forcibly migrate tasks to DLP in other quadrants with lower load. It is observed that this strategy produces unnecessary task migration in heterogeneous programming model. The reason is that the load is uneven from local view but is even from global view. This unnecessary migration leads to bandwidth fluctuation and overall performance degradation. In order to overcome this problem, this paper proposes global view scheduling, which schedules according to the status of tasks waiting in line for execution and tasks waiting in line for scheduling. Lazy migration is adopted when DLP load is balanced from the global view, and eager migration is adopted immediately when DLP load is unbalanced from the global view. Experimental results show that this scheduling method can reduce unnecessary migration, alleviate bandwidth fluctuation and improve overall performance without reducing NPU utilization.
Adjustment Model of CPIII Height Control Network which Taking Vertical Angle as Observation Value

CPIII control network plays an important role in the construction and operation of high-speed railway. However, due to the large number of control points and the low efficiency of leveling, the field observation of CPIII elevation control network becomes a very time-consuming and laborious work. According to the fact that the vertical angle of CPIII control network is very small, a new adjustment model of CPIII elevation control network with vertical angle as observation value is proposed in this paper. The example shows that the model does not need to be weighted, and has the advantages of simple algorithm and higher precision.
Distributed implementation of neuroevolution of augmenting topologies method

Despite the neuroevolution of augmenting topologies method strengths, like the capability to be used in cases where the formula for a cost function and the topology of the neural network are difficult to determine, one of the main problems of such methods is slow convergence towards optimal results, especially in cases with complex and challenging environments. This paper proposes the novel distributed implementation of neuroevolution of augmenting topologies method, which considering availability of sufficient computational resources allows drastically speed up the process of optimal neural network configuration search. Batch genome evaluation was implemented for the means of proposed solution performance optimization, fair, and even computational resources usage. The proposed distributed implementation benchmarking shows that the generated neural networks evaluation process gives a manifold increase of efficiency on the demonstrated task and computational environment.
Population Based Training for Text Classification Using Convolutional Neural Network

We propose Population Based Training (PBT) to dynamically adjust hyperparameters during the training of the network. Experiments on text classification show that the adapted hyperparameters provided by PBT have significant advantages over traditional fixed set of hyperparameters.
Pursuing Intelligent Behavior in Cyber−Physical Systems by Lightweight Diagnosis

Intelligence in its decisions is a trait that people have grown to expect from a cyber−physical system, in particular that it makes the right choices at runtime, that is, those that allow it to fulfill its tasks, even in case of faults or unexpected interactions with its environment. Analyzing how to continuously achieve the currently desired (and possibly continuously changing) goals and adapting its behavior to reach these goals is undoubtedly a serious challenge. This becomes even more challenging if the atomic actions a system can implement become unreliable due to faulty components or some exogenous event out of its control. Herein, a solution for the presented challenge is proposed. In particular, it is shown how to adopt a lightweight diagnosis concept to cope with such situations. The approach is based on rules coupled with means for rule selection that is based on previous information regarding success or failure of rule executions. Furthermore, Java‐based framework of the lightweight diagnosis concept is presented, and the results obtained from an experimental evaluation considering several application scenarios are discussed. At the end, a qualitative comparison with other related approaches that should help the readers decide which approach works best for them is presented. An interactive preprint version of the article can be found here: https://www.authorea.com/doi/full/10.22541/au.163578445.51350502 .
The Hybrid-Agile Design of Experiments Methodology

Abstract A DOE (Design of Experiments) is the laying out of a detailed experimental plan in advance of doing the experiment. Optimal DOEs maximize the amount of information that can be obtained for a given amount of experimental effort. The traditional DOE methodology is waterfall-type methodology implying a sequential and linear life-cycle process. The success of the experiment and usefulness of the results are highly dependent on the initial experimental setup and assumptions, and does not allow to go back and change something that was not well-documented or thought upon in the design stage. The fast-changing software development industry have made it understandable that the traditional waterfall methodology for developing systems, which follows similar patters to the traditional DOE, lacks the agility required for developing robust systems. These limitations have triggered the development of agile : a type of incremental model of software development based on principles that focuses more on flexible responses to change, instead of in-depth planning at the design stage. This paper proposes the hybrid-agile DOE methodology – a methodology that incorporates agile principles in traditional waterfall DOE methodologies – to design effective experimental layouts that allow for improvement during the experimental trial process. The methodology is applied to the natural ageing of adhesives tapes for building applications. This methodology can overcome traditional DOE, by adding agility in the whole process, especially in cases where the investigated products lack prior information and are characterised by large variability.