Reviewerly/exHarmony huggingface dataset:
Experimental Study of Tidal Flat Formation on Coastal Peat

Abstract Tidal flat formation on the coastal peat is a unique phenomenon that has occurred at western part of Bengkalis Island, Riau Province, Indonesia. It was identified that coastal erosion problems have been occurring continuously during at least the last 30 years along the north part of Bengkalis Island. The materials have moved to the south part of Bengkalis Island, and sedimented as tidal flat. Research on tidal flat in the peatlands has not yet been developed, so this study aims to carried out an experiment study in order to understand the tidal flat formation on coastal peat. The method used in this study is a simulation of tidal flat formation with several wave variations. Simulations were carried out using laboratory experiments with coastal peat samples and sea water at the wave channel. The results show that the waves and the velocity greatly influence the tidal flat formation and its formation rate. In addition, the formation of peat tidal flat was also influenced by velocity settling of the peat particles. The energy for tidal flat formation was the minimum energy generated by waves, that cannot transport the particles move back by reverse flow.
Evaluation of Feature Extraction for Indonesian News Classification

News is information about knowledge or event that occurs within a certain period. In the text news, there are several categories can be classified. This research proposes an evaluation of feature extraction to classify Indonesian language news. The dataset are from www.cnnindonesia.com (May 2018 - July 2018) with 4 categories and has a total of 3677 data and www.liputan6.com with 4 categories and has a total of 3415 data. All existing data will be processed to structured form and then the feature is extracted with 8 feature extraction method (TF, TF-IDF, TF-RF, TF-Prob, TF-CHI, TF-IDF-ISCDF, TF-IGM, and RTF-IGM) combined with 6 classification algorithms (Gaussian Naïve Bayes, k-NN, Decision Tree, Neural Network, Logistic Regression, and Support Vector Machine). From this research can be concluded that the Gaussian Naïve Bayes algorithm with TF-Prob was able to obtain the best accuracy with 99.701% (CNN Indonesia) and 99.824% (Liputan6) from 5 fold cross-validation.
Automatiс Recommendation of Video for Online School Lesson Using Neuro-Linguistic Programming

The article describes an approach to automating the matching of video materials to text slides in English classes in an online school by vectorizing slide text and video subtitles using the TF-IDF measure and maximizing the cosine similarity measure of these vector representations.
REACTIVE GOSSANS IN PERMAFROST: CASE STUDIES FROM THE CANADIAN ARCTIC ISLANDS

Gossans are surficial deposits that form through the chemical and physical weathering of the host bedrock. Gossans may produce secondary minerals that reflect the composition of their primary mineral sources, including buried ore deposits. The mineralogy of gossans (e.g. silica, jarosite and goethite) results from the oxidation of sulphides by acidic and oxidizing fluids. Research carried out by the Geological Survey of Canada (GSC) over the past decade has led to new insights on the mapping, morphology, stratigraphy, mineralogy and geochemistry of gossans that form in a permafrost environment. Gossans located on Victoria Island and Axel Heiberg Island were the focus of GSC field and laboratory studies carried out from 2011 to 2015. Study protocols included (1) the identification of gossans using satellite imagery; (2) field mapping, sampling and in situ spectral analyses; (3) mineralogical and geochemical studies; and (4) comparative studies of bedrock and stream sediments located in proximity to gossans. The distinctive colours of the oxide cap allow for the identification of gossans using multispectral and hyperspectral optical remotely sensed imagery especially in the visible-near-infrared (VNIR) portion of the electromagnetic spectrum. The small size of most gossans (a few tens of meters to less than 1 to 2 km in length) requires access to high spatial resolution images such as those provided by the WorldView multispectral sensor. Field observations and sampling of gossans at seven localities suggest that their morphology is complex. For example, the observed stratigraphy does not always match the classic gossan profile of sulphide-depleted and silica-enriched cap underlain by Fe-oxides and other secondary minerals. Gossans were grouped into three categories based on stratigraphy, presence or absence of evaporitic rocks and reactivity with permafrost. In all cases, cryogenic and oxidation processes were clearly ongoing and sulphide oxidation led to acidic conditions and the formation of relatively uniform mineralogical and textural characteristics. We propose that the pumping action of fluids in a permafrost environment, similar to a solifluction process, acts as a concentration mechanism over time and contributes to the transport of materials down slope.
Formation of Meta-Subject Results in Teaching a Foreign Language

The modern system of national education, developing in the competence paradigm, puts on the agenda the need to form students' communicative skills that belong to the category of critical competencies of a meta-subject nature. The article discusses the methodological features and pedagogical conditions for forming meta-subject skills of schoolchildren in foreign language lessons. An attempt is proposed to determine the characteristics of the meta-subject approach, including a complex of personality traits that allow the student to act in a given communication situation at the level determined by the age-related psychological characteristics and the possibilities of the social experience of a younger student. The authors used the following research methods: utilizing the study and generalization of pedagogical experience and the survey method, the communicative, regulatory - behavioral and motivational characteristics of a younger student, which must be taken into account when introducing pedagogical technologies focused on meta-subject results in foreign language lessons, are concretized, The analysis of the real practice of an educational activity is aimed at establishing a set of features of the pedagogical support of the meta subject approach within the framework of the "Foreign language" discipline in primary school. Based on the analysis of scientific literature on research and practical experience, several strategies have been identified that ensure the effective implementation of meta-subject results in younger students in foreign language lessons.
Rotation Axis Calibration of a Tumtable Using Adaptive Weighted-distance

Turntable rotation axis has significant influence on registration of multi-view point clouds in various 3D scanning systems. Under existing established method, different feature points have equal contribution in calibrating turntable rotation axis. To reduce calibration errors and to improve computation efficiency, this paper proposes a new method using adaptive weighted distance. Under proposal, weights of each calibrating corner point are computed according to the distance between adjacent positions of the corner. With the adaptive weights, rotation axis can be fitted with fewer errors. In addition, a robust error evaluating method is developed to measure the accuracy of proposed method. Further, a rotation angle correction method is proposed to reduce system error. Experiments demonstrate that the developed method achieves higher accuracy in turntable rotation axis calibrations.
Intelligent Assistance for End-Users in creating Point and Click Games Storylines

In this paper, we present an intelligent support End-User Developers (EUDevs) in creating plot lines for Point and Click games on the web. We introduce a story generator and the associated user interface, which help the EUDev in defining the game plot starting from the images providing the game setting. Such suggestions can be further developed by the EUDev, modifying the generated text and saving the result. The interface supports the control of different parameters of the story generator using a user-friendly vocabulary. The results of a user study show good effectiveness and usability of the proposed interface.
The Implementation of Electric Pile Sharing Operation System based on Identity-Based Cryptograph

Identity-Based Cryptograph (IBC) takes the user's identity (such as email address, mobile phone number, personal name, etc.) as the public key, which reduces the process of exchanging digital certificates and public keys, effectively reduces the complexity of key and certificate management in the public-key system, and makes the security system easy to deploy and manage. It is very suitable for end-to-end offline secure communication, cloud data encryption, attribute-based encryption, policy-based encryption of various occasions. In order to improve the safety level of end-to-end off-line safety communication in electric vehicle pile sharing application and reduce the system complexity, this paper proposes an identity authentication system solution based on identification algorithm.
Fusing IMU Data into SfM for Image-Based 3D Reconstruction

Structure-from-Motion (SfM), one of the most extensively used image-based 3D reconstruction methods in Computer Graphics and Vision, suffers from mismatching of image feature points when input images are sparse, thus leading to miscalculation of camera rotation matrices as well as subsequent reconstruction errors. To address these problems, this paper reports an improved SfM reconstruction system and proposes to suppress the miscalculation of camera rotation matrices during Bundle Adjustment (BA) by forming a rotation constraint with Internal Measurement Unit (IMU) data collected from a smartphone, consequently improving the reconstruction precision and efficiency. More specifically, being refined by the Kalman filter, the IMU data are adopted to estimate the camera rotation matrix of each image captured by the smartphone. As the camera rotation matrix calculated by the IMU data is based on the sensor coordinate system, this paper employs the Lie Algebra theory to transform the reference frame of the rotation matrix from the sensor coordinate system to the camera coordinate system.
Image Classification using DNN with an Improved Optimizer

In deep learning, the optimization techniques are for the most part dependent on gradient descent methods, such as SGD, ADAM which adopt the leading place in the area of optimization methods. Fortuitous methodologies which depend on stochastic gradients are non-adaptive because the prescribed parameter worth's usage should be tuned for every application. But the generalization performance of the stochastic optimizers is far superior to the adaptive methods, whereas Adam and its variants cannot maintain this without a fast convergence rate in deep neural networks. To improve this generalization performance, we need to diminish the oscillation of the weights which is the general problem of the accuracy fall. Along these lines, we have attempted to propose Mean- ADAM, a variance of ADAM which has extended the updated weights by an external weight to diminish the oscillation and overcome a superior accuracy rate than all other adaptive gradient methods till the conclusion of the breeding. Therefore, we can substantially improve the generalization performance, permitting it to contend with SGD with momentum on image classification datasets such as MNIST, CIFAR10, CIFAR100, ImageNet, etc. We have attained 82% at 150 epochs with CIFAR10 and 99.49% with MNIST whereas the ADAM has indicated 76% and 99.43% individually.
Developing Design Knowledge and a Conceptual Model for Virtual Reality Learning Environments

This chapter focuses on applying design science research for virtual reality learning environment (VRLE) design processes. Six selected case studies are presented in the context of VRLEs. The case selections were analyzed in terms of their contributions to design knowledge. The objective of this book chapter is twofold: 1) for researchers, the design knowledge contributions of case studies are highlighted for future reference, and 2) for developers and practitioners, design principles are presented for the development of VRLEs. The final outcome of the present study is a conceptual model describing the current design knowledge in the field of VRLEs and identifying the research gaps that should be addressed in future research on the educational use of VR.
Adaptive Weighted Maximum Margin Criterion

Feature extraction is an image processing method widely used in machine learning, playing a significant role in both recognition and classification tasks. In contrast with unsupervised feature learning methods, supervised feature learning methods supported by labels achieve more valuable discriminative information. However, this inevitably draws attention to the small sample size (SSS) problem, which leads to singular matrices and impacts on the accuracy of results. In this paper, we propose a novel method based on Maximum Margin Criterion (MMC) with adaptive weighting mechanism to preserve features of good discriminant performance while reducing feature dimensionality. There are two major novelties of the proposed method: (1) the MMC model is utilized to resolve the irreversibility of matrices by maximizing the difference of the between-class and within-class scatter matrix when the feature dimension outnumbers the number of samples; (2) the adaptive weighting mechanism which can automatically find the optimal weight is designed as the balancer to adjust the within-class scatter and the between-class scatter. We validate our new approach on three popular image datasets. The experimental results indicate that the proposed method shows superiority in the following classification tasks, which performs favorably against classical methods in accuracy and calculation cost.
True-Random Number Generator Based on Image Histogram

It is the non-repetitive distribution that makes the random numbers important in artificial intelligence techniques, cryptology, transferring a real environment to the virtual world and many more applications. Since the source of true random numbers consists of data from the physical world, the same number chain is never produced. In this study, images taken from the outside world were used as the source of randomness. The resulting image was first converted into an 8-bit gray image, and then the histogram of this gray image was revealed. As is known, an image histogram shows the color distribution in that image. In this study, the color distribution resulting from the histogram has been converted into a random distribution between 0 and 1. As a result, it was observed that the resulting distribution of numbers overlapped with histogram. Since the distribution of the numbers depends on the ratio of the pixels and the ratio of the pixels on the image obtained, the result is a real random number sequence.
Fixing Spellings of Colloquial Words using a Crowd Based Game

Colloquial, slang and transliterated words in Hindi ofen lack standardised spellings and hence pose a challenge in spelling correction using existing language resources. We propose the design of a crowd- based game that relies on knowledge of native Hindi speakers to determine spellings of colloquial words taken from a dataset of conversationally typed words.
A Novel Line Integral Transform for 2D Affine-Invariant Shape Retrieval

Radon transform is a popular mathematical tool for shape analysis. However, it cannot handle affine deformation. Although its extended version, trace transform, allow us to construct affine invariants, they are less informative and computational expensive due to the loss of spatial relationship between trace lines and the extensive repeated calculation of transform. To address this issue, a novel line integral transform is proposed. We first use binding line pairs that have the desirable property of affine preserving as a reference frame to rewrite the diametrical dimension parameters of the lines in a relative manner which make them independent on affine transform. Along polar angle dimension of the line parameters, a moment-based normalization is then conducted to degrade the affine transform to similarity transform which can be easily normalized by Fourier transform. The proposed transform is not only invariant to affine transform, but also preserves the spatial relationship between line integrals which make it very informative. Another advantage of the proposed transform is that it is more efficient than the trace transform. Conducting it one time can allow us to achieve a 2D matrix of affine invariants. While conducting the trace transform once only generates a single feature and multiple trace transforms of different functionals are needed to derive more to make the descriptors informative. The effectiveness of the proposed transform has been validated on two types of standard shape test cases, affinely distorted contour shape dataset and region shape dataset, respectively.
Towards Visualizing and Exploring Multivariate Networks on Mobile Devices

Mobile devices are getting more and more powerful, however, interfaces for exploring multivariate network data on-the-go remain rare and challenging to realize. In this work, we present considerations as well as concepts on how to enable such exploration on mobile devices. Specifically, we discuss that an interface with force-directed node-link representation alongside additional views for investigating multivariate aspects and filter functionalities seems promising. Further, such an interface could be advanced by incorporating multiple mobile devices in parallel. As part of our ongoing investigations, we have realized an early prototype indicating the general feasibility of our concepts.
Service Oriented Middleware for Tuberculosis`s Information Services Management

Reducing cases The prevalence of tuberculosis (TB) is still a global problem. Indonesia is still in the list of ten countries with high TB burden. This is due to the fact that the management of TB epidemic handling information is still partial, which is managed by each stakeholder, including the ministry of health as a provider of TB patient data, health facilities as a provider of patient care data, and TB non-governmental organizations (NGOs) as providers of accompanying data and information take medicine for patients. In addition, some TB information requires data analysis, while the technology currently used uses Message-Oriented Middleware (MOM) for data and system integration. But MOM cannot meet TB information needs because it depends on the metadata structure. This research tries to offer a new data integration architecture and system as a middleware application that has layers of data analysis from various metadata sources (TB dictionaries). The method used is Service Computing Systems Engineering Life Cycle. The results of this study are a model of data integration architecture and system design called Service-Oriented Middleware (SOM) for managing TB information. The SOM model can also be used as a reference in the integration of data, systems and data analysis modules to provide information services according to the needs of the company's business processes. Thus, each stakeholder involved can obtain complete data and information, as well as patients get a care service system that can help end the TB epidemic.
Location Misplacement Analysis on Global Positioning System

Global positioning system (GPS) works based on satellites signal synchronization to determine the predicted position of an object on earth surface. The position can be used for many applications such location based services. For mobile equipment that changes its position regularly, it is important to provide GPS location as soon as possible, since high latency position data from GPS service may lead misplacement. This paper analyzes the misplacement on GPS services by means of experiments. The minimum GPS receiver composed by SIM908-C module exerts some latencies values that may lead to misplacement in moving object. The assessment shows that the GPS location request experiences latency of 128.488 ms in average. This latency lead to misplacement of 1.43 m for moving object with speed of 40 m/s. Misplacement increases as object speed increases.
Study of Giving Training Images in SegNet

When using machine learning, it is necessary to prepare training data and set various parameters. In recent years, machine learning has attracted attention, and the number of cases where a person who does not have specialized knowledge or experience does machine learning has increased. SegNet, which is the target of this research, needs training images that annotated for recognition targets. Therefore, preparing training images requires an enormous amount of time and effort. Previous studies have shown data sets and parameters for learning each recognition target. However, there is no case where the investigation about how to give training images such as the number of training images and the setting of parameters required when using SegNet for the first time was conducted. The larger the number of training images, the higher the recognition accuracy can be expected, but the recognition accuracy does not necessarily increase in proportion to the number of the prepared training images. In this paper, we report the effects of the number of training images and the setting values of batch size on recognition accuracy as a way of giving training images.
Research on Face Recognition Based on Gabor-LeNet Convolutional Neural Network Model

Abstract Aiming at the problems of slowing convergence speed, and low recognition accuracy in the face recognition training of the existing LeNet-5 convolutional neural network, an improved LeNet-5 convolutional neural network model is proposed and applied for face recognition. The main improvement is the use of Gabor filter to initialize the first convolutional layer and the activation function of Parametric Rectified Linear Unit (PReLU). The recognition accuracy of the improved model on ORL and GT face datasets has reached more than 98%. At the same time, the recognition accuracy rate on the face data set AR with occlusion has reached more than 90%, indicating that the improved model has strong robustness.
Tool Support for Finding Anomalies in Refactoring Edits for Improving Software Evolution

Refactoring improves software quality in an evolving system. Programmers conduct runtime behavior-preserving modifications in refactorings. Integrated Development Environments (IDEs) support automated refactorings; however, recent studies report usability problems with refactoring tools. Developers often conduct refactorings manually although automated tools are available. These manual and time-consuming activities are inevitably dull and unreliable. To address this problem, we present a technique called REFBUGFINDER. We help programmers conduct manually refactoring-related tasks while our automated tool checks and detects refactoring-related anomalies. Our technique is implemented as a plugin running on the Eclipse IDE. In the evaluation, (1) we assessed REFBUGFINDER with three open source projects to demonstrate the applicability of our approach by detecting refactoring bugs. (2), Our user study was conducted with five computer science students at UNO. In our evaluations, we assessed REFBUGFINDER how effectively our tool can help programmers make refactoring modifications and find refactoring-related bugs automatically. These evaluations showed that our tool decreased significant manual costs and unreliable refactoring change risks.