Reviewerly/exHarmony huggingface dataset:
Pengembangan Web Service Sistem Informasi Sekolah

School information system is a system that provides information to support the operational, management, and decision making functions of an organization. Information sources within the information system that support day-to-day management operations such as transaction processing, status clarification, and so forth. School information systems generally consist of a variety of systems, including academic, financial, library and staffing systems. With a variety of systems in a school information system, it is necessary to integrate between systems aimed at avoiding data duplication in each system and increase system flexibility which can be achieved by utilizing web service technology. The purpose of this research is to design a web service on information systems schools that comply with the National Education Standards (SNP) use the REST web service architecture. The results of the development of an information system web service design that is by mapping class diagrams that will be made in the design of school web services by adding modules to JIBAS with the Minister of National Education Regulation in accordance with 8 BSNP standards.
Vocal Piano Accompaniment: A Constant Research Towards Emancipation (2)

This is the second and last article in the series dedicated to the investigation of the evolution of vocal piano accompaniment through history and the role-played by the piano in its relationship with the voice.If the previous chapter focused from the beginnings of piano accompaniment to Franz Schubert, this one will take up again the analysis of the piano part from the last lieder of Franz Schubert to Arnold Schoenberg.The research method used continues to be based on the musical analysis of the piano part, addressing both issues of the piano itself and its link with the text and the vocal part.By means of musical examples of different composers, the article investigates all those aspects that provide evidence of the change in the role of the piano in the correspondence of the piano with the voice and its repercussion on the final result of the work.In this way, we will look at the history of piano accompaniment for voice and see how the influence of Schubert's conception of vocal piano accompaniment materializes in contemporary and later composers, as well as the crucial change in the history of the piano as an accompanying instrument introduced by Schubert.The results and conclusions drawn from the evolution presented in these two articles are presented at the end of the article, based on the different aspects involved in the musical event, such as rhythm, harmony or texture.
Pedagogical model of curator activity for formation of student civil position at higher educational institution

The article shows that the education system should prepare people who can live in civil society and constitutional state and also create them. Literature theoretical analysis allowed us to determine the activities of the curator for the formation of student civic position as a focused and consciously carried out pedagogical process for the organization of socially significant activities of students and the development of self-government, necessary for self-realization of a person in society. In the course of the experimental work, a pedagogical model of the curator’s activity on the formation of the civic position of students was developed and implemented. It includes a number of components such as goals, objectives, content of the activities of curators and students at each stage of the formation of a civil position, methods, forms, means, criteria and indicators of the formation of a civil position. The implementation of this pedagogical model promotes the involvement of students in socially significant work, their active participation in various areas of interest, allows students to develop organizational skills, gain experience in participating in self- government and show a civil position. On the basis of developed the criteria and indicators we measured the level of formation of the civic position of students based on their introspection and expert assessments. The experts were individual group students, lecturers and curators. The study allowed us to conclude that the focused activities of the curators contribute to the activation of extracurricular activities of students, the development of student self-government of the group as a real form of student democracy with the corresponding rights, opportunities and responsibilities.
Artificial intelligence associated with satellite data in predicting energy potential in the Brazilian savanna woodland area

Jonas Inkotte (1
Intelligent Public Health Management System using Internet of Things

In recent times, the Internet of Things (IoT) is pre-eminent to our survival and is emerging as the technology of choice for the public as well as private sectors. IoT based health management system will provide effective communication between patient and physician to cure the patient in time. The proposed system consists of a smart patient health tracking mechanism that uses different medical sensors (Body Temperature Sensor, Environment Humidity and Temperature Sensor, Heart Beat Sensor, and ECG) to track patients' health, while using GPS (Global Positioning System) to inform the Ambulance Management and Physician about the basic and crucial health parameters of the patient. The data collected from sensors is also stored in a centralize cloud for further analysis of patient health. To get access to the stored information on the cloud, a graphical user interface is designed on a website. Moreover, LCDs and buzzers are used for real-time monitoring and indication of a patient's health parameters.
High-order Taylor expansion based image space transform method for real-time augmented reality

This paper proposes a real-time augmented reality method based on Taylor expansion formula. This method has the advantage that the pixel relationship in the discrete image space is converted to a continuous high-order Taylor space and maintains pixel invariance. After conversion to Taylor space, the ability to resist dramatic changes in illumination between frames is enhanced and robust to intra-frame local illumination mutations. In the spatial transformation process, differential low-order features are used to represent higher-order features, multiplied by appropriate feature coefficients. These coefficients are first determined theoretically and then experimentally verified, allowing us to obtain high-order feature information and approximate the original pixel values based on the features. We then applied this technique to color-based mean shift tracking problems to achieve promising results.
Cross-covariance matrix: Time-shifted correlations for 3D action recognition

In this paper, we introduce cross-covariance to form Symmetric Positive Definite (SPD) matrix-based representations for 3D action recognition. The cross-covariance is generated by the correlational statistics between the time-shifted poses, which brings more informative features and time-order structure to improve the discriminative power on actions. Due to the special manifold structure, the cross-covariance is a totally different SPD matrix representation compared to covariance. In order to utilize such cross-covariance statistics while holding the covariance statistics, we devise a fashion of expression to mix them together as a larger SPD matrix benefiting from the Riemannian geometry. Further, we extend the symmetric cross-covariance into the non-symmetric versions, where the time-order information can be embodied in the related matrix structures. Thus, the derived SPD representations are able to improve the discriminative power of actions. In addition, our representations are easy to implement and to be extended by other prevalent techniques, such as kernel methods, metric learning, etc. Experimental results show that our proposed method efficiently promotes the performance of 3D action recognition tasks. In some public databases, our method outperforms the state-of-the-art methods which are based on other feature representations, kernel matrices, temporal hierarchical features, etc.
UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM

Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.
Genus and Species-Level Classification of Wrasse Fishes Using Multidomain Features and Extreme Learning Machine Classifier

Automated recognition and classification of fishes are useful for studies dealing with counting of fishes for population assessments, discovering association between fishes and ecosystem, and monitoring of the ecosystem. This paper proposes a model which classifies the fishes belonging to the family Labridae in the genus and the species level. Features computed in the spatial and frequency domains are used in this work. All the images are preprocessed before feature extraction. Preprocessing step involves image segmentation for background elimination, de-noising and image enhancement. A combination of color, local binary pattern (LBP), histogram of oriented gradients (HOG), and wavelet features forms the feature vector. An ensemble feature reduction technique is used to reduce the attribute size. Performances of the system using combined as well as reduced feature sets are evaluated using seven popular classifiers. Among the classifiers, wavelet kernel extreme learning machine (ELM) showed higher classification accuracy of 96.65% in genus level and polynomial kernel ELM showed an accuracy of 92.42% in species level with the reduced feature set.
Model Pembelajaran Course Review Horay untuk Meningkatkan Pemahaman Konsep Bilangan 1-10

This experimental study aims to increase children’s comprehension of the concepts of number 1-10 by implementing the Course Review Horay Learning Model. The research subjects were group A children in PAUD Tunas Harapan Medan. The understanding of number concepts was evaluated from pretest and posttest with the indicator is to comprehend the concept of number. The scores were analyzed statistically by using the paired-sample t-test. The students in the experimental class were taught by using the Course Review Horay Learning Model, while the control was taught conventionally. The results of the study show the experimental class data group differs from the form control class with a significant value is 0.86 (p = 0.05). The results of the study concluded that the Course Review Horray Learning Model can significantly improve children's ability to understand the concept of number 1-10.
Document summarization using a structural metrics based representation

Currently, each person produces 1.7MB of information every second in different formats. However, the vast majority of information is text. This has increased the interest to study techniques to automate the identification of the relevant portions of text documents in order to offer as a result an automatic summary. This article presents a technique to extract the most representative sentences of a document taking into account by the user’s criteria. These criteria are learned using a neural network, from a minimum set of documents whose sentences have been rated by the user in terms of importance. To verify the performance of the proposed methodology, we used 220 scientific articles from the PLOS Medicine journal published between 2004 and 2016. The results obtained have been very satisfactory.
Behind the screen: content moderation in the shadows of social media

"Behind the screen: content moderation in the shadows of social media." Digital Journalism, 8(7), pp. 968–969
Aspect-Based Sentiment Analysis with Adjustments to Irrelevant Sentimental-Related Features

Aspect-based sentiment analysis is a fine-grained task of sentiment classification for multiple aspects in a sentence. While the overall polarity of a sentence has been successfully predicted with neural network architectures, aspect-specific sentiment analysis still faces challenges. In this paper, we propose a novel neural network named IRP-BERT. It is based on BERT which is the newest breakthrough in natural language processing. IRP-BERT aims at improving the accuracy of predicting the right polarity of aspects in a sentence, especially the neutral ones, because some sentimental-related words such as love in a noun phrase which is neutrally sentimental could have a wrong impact on the polarity classification to aspects. More specifically, we design a module named Irrelevance-Removing Process (IRP) to counteract the impact from irrelevant sentimental-related features. The final prediction depends on word representations from the outputs of both IRP and BERT. Experiments on a twitter dataset show that our model outperforms both existing state-of-the-art methods and fine-tuned BERT baselines.
Sikap Bahasa Mahasiswa dan Implikasinya terhadap Pembelajaran Bahasa Indonesia di Perguruan Tinggi

This study aimed to describe the students’ language attitudes toward Indonesian language course and its implication in higher education. This research method was a case study. The participants in this study were 92 students of Faculty of Literature at Universitas Muslim Indonesia Makassar. The data were collected by using a questionnaire technique which developed by three indicators such as loyalty attitude, pride attitude, and awareness of language norms. The data collected was analyzed statistically by using SPSS program and the results were converted into four attitude categories; very positive, positive, positive enough, and negative. The results showed that, overall, the students’ attitude toward Indonesian language was in very positive category (68% of students numbe,r). Definitely, the students’ loyalty attitude was in the very positive category (77% of students number), the pride attitude was in the very positive category (61% of students number), and students’ awareness of language norms was in a positive category (70% of students number). Furthermore, the results of this study implied to be considered to the development of teaching Indonesian language becomes more creative and innovative and the development of qualified coursebook of Indonesian Language in higher education which suitable with current development.&#x0D;
A Comparative Analysis of Various Deep Learning Models for Facial Recognition

The main idea behind facial recognition is to identify human faces in images as well as videos. Multiple techniques have been developed till date to improve the performance of facial recognition systems. Earlier methods were not competent of capturing prominent facial information due to pose, illumination, occlusion, resolution and skin color effects. A new emerging technique known as deep learning is showing impressive improvement in performance metrics for facial identification. With recent developments in deep learning techniques, facial detection systems can learn data by themselves and can extract useful features. It has been shown to exhibit promising recognition results for diverse real time applications. In this paper, we have examined deep convolutional neural networks and discussed various models related to deep learning such as VGGNet, GoogleNet, SqueezeNet, AlexNet and ResNet and have compared these with respect to various evaluation metrics such as error rate, prediction time and accuracy percentage.
Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification

Since a large number of bug reports are submitted to the bug repository every day, efficiently assigning bug reports to the correct developer is a considerable challenge. Because of the large differences between the different components of different projects, the current bug classification mainly relies on the components of the bug report to dispatch bug reports to the designated developer or developer community. Unfortunately, the component information of the bug report is filled in by default according to the bug submitter and the result is often incorrect. Thus, an automatic technology that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. In this paper, we propose a method based on the combination of imbalanced learning strategies such as random undersampling (RUS), random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and AdaCost algorithms with multiclass classification methods, OVO and OVA, to solve bug reports component classification problem. We investigate the effectiveness of different combinations, i.e., variants, each of which includes a specific imbalance learning strategy and a specific classification algorithm. We mainly perform an analytical study on five open bug repositories (Eclipse, Mozilla, GCC, OpenOffice, and NetBeans). The results show that different variants have different performance for bug reports component identification and the best performance variants are combined with the imbalanced learning strategy RUS and the OVA method based on the SVM classifier.
Managing Security Risks: An Assessment of U.S. Critical Cyber Infrastructure Protection

Cyberattacks by nation states and other organized groups against the United States have continued to escalate in recent years with ever-increasing concerns about ransomware attacks and other cyberattacks on the public sector and critical infrastructures. Society is highly dependent upon these critical infrastructures for water, food, communication, and transportation, to maintain public health and safety. The following article provides an assessment of the United States cybersecurity landscape and provides recommendations to ensure cyber protection and security readiness of critical infrastructures for the future.
Automatic Detection and Identification of Controlled Knives Based on Improved SSD Model

The automatic detection and identification of security inspection system is the inevitable trend of intelligent development. It is an important research problem to improve the accuracy and efficiency of X-ray security inspection equipment to detect dangerous goods. Taking the automatic tool detection and recognition system of security inspection system as the research object, aiming at the fact that the shallow characteristic chart representation ability of SSD algorithm is not strong, the small target feature of training stage gradually disappears, and the detection accuracy is not high, and there are some problems, such as missing detection and false detection of small targets such as controlling tool in security inspection dangerous goods. This paper proposes to replace the basic network VGGI 6of SSD with ResNetl01 with stronger anti-degradation performance to construct SSD-ResNet101 network structure. On this basis, the feature fusion method is used to make the shallow layer merge the deeper features to increase the receptive field of the shallow feature map. Make full use of the context information to improve the detection accuracy of the small target of dangerous goods in security inspection. The analysis of experimental test data shows that the improved algorithm has high detection accuracy, good real-time performance and good robustness to small targets such as control tools in security picture.
A Hybrid Information Retrieval for Indonesian Translation of Quran by Using Single Pass Clustering Algorithm

The holy Quran as a scripture and the source of law must be believed by more than one billion Muslim in the world. To be a good Muslim, someone must be able to understand the contents of the Quran and underlie all of their activity to the guidance that is in the Quran. However, the holy Quran is scripture that is very long document and derived in Arabic. Thus it can cause ordinary people those who do not understand Arabic find it increasingly difficult to find specific topics to learn the contents content of the Qur'an. This research aims to develop a web-based verse search system for Al-Qur'an. This paper proposed Quran information retrieval software which is integrated with clustering algorithm to ease Muslim to discover the relevant information in Quran verse by grouping the Quran verse into their own similar group. This proposed hybrid Quran information retrieval is composed of two main parts namely Quran indexing and Quran information searching.
Architecture and mobile application with augmented reality, visualizing videos and 3d objects in museums

This research presents an architecture and a mobile application with augmented reality (MAR), called MARHN, solving the problem of lack of information in a museum, since objects can be visualized both in videos and in three-dimensional objects. We report the development process and the functionality of the application, to find that the application is clear and easy to use. A field study of Natural History Museum of Lima visitors (n = 45) shows that the MARHN application generates satisfaction and ease to learn more about museum objects. The research also discusses the theoretical implications of other applications of AR (Augmented Reality), to use them as a model and improve.
Cluster Based Multi Layer User Authentication Data Center Storage Architecture for Big Data Security in Cloud Computing

In distributed computing, cloud is a fast emergent technique in data analytic technology era which is applied to stock up and retrieves the big data in distributed environment. Every day an individual person and companies are placing more data in the cloud. The organization authorities and individual users are started to worry about how the big data are safe in cloud. Information protection is one of the foremost troubles, which reduce the development of cloud computing and make difficult by means of data confidentiality and security. Day by day researchers has been introducing new techniques for secure big data in cloud computing environment. In conventional security systems are used encryption and decryption for providing security in cloud data. In our proposed work we use multilayer user authentication mechanism for big data security. First, we deal about the safety measures and challenges in cloud computing. Second, we propose cluster based multi layer user authentication data centre storage design for defending big data in cloud computing atmosphere. Multilayer authentication provides information security in three layers with different defence factors. Service providers categorize the data units in to three different clusters based on the usage of data. Proposed systems ensure the big data security through authentication in cloud and get extra engineering insights.