Reviewerly/exHarmony huggingface dataset:
STEMMING IMPACT ANALYSIS ON INDONESIAN QURAN TRANSLATION AND THEIR TAFSIR CLASSIFICATION FOR ONTOLOGY INSTANCES

The current gap which appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and their Tafsir to develop ontology instances. The existing studies of stemming effect analysis performed in various languages, dataset, stemming method, cases, and classifier. However, there is a lack of literature that studies about stemming influence on instances classification for Quran ontology with different dataset, classifier, Quran translation, and their Tafsir on Indonesian. Based on this problem, our study aims to investigate and analyze the stemming impact on instances classification results using Indonesian Quran translation and their Tafsir as datasets with multiple supervised classifiers. Our classification framework consists of text pre-processing, feature extraction, and text classification stage. Sastrawi stemmer was used to perform stemming operation in text pre-processing stage. Based on our experiment results, it was found that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation owns the best classification performance, i.e., 70.75% for accuracy and 71.55% for precision in Indonesian Quran translation dataset on 20% test data size. While in 30% test data size, SVM and TF-IDF with stemming process own the best classification performance, i.e., 67.30% for accuracy and 68.10% for precision in Ministry of Religious Affairs Indonesia dataset. Furthermore, in this study, it was also discovered that the Backpropagation Neural Network has the most precision and accuracy reduction due to the negative impact of stemming operations.
Kernel-based Gaussian process for anomaly detection in sparse gamma-ray data

In radioactive source surveying protocols, a number of task-inherent features degrade the quality of collected gamma ray spectra, including: limited dwell times, a fluctuating background, a large distance to the source, weak source activity, and the low sensitivity of mobile detectors. Thus, collected gamma ray spectra are expected to be sparse and noise dominated. For extremely sparse spectra, direct background subtraction is infeasible and many background estimation techniques do not apply. In this paper, we present a statistical algorithm for source estimation and anomaly detection under such conditions. We employ a fixed-hyperparameter Gaussian processes regression methodology with a linear innovation sequence scheme in order to quickly update an ongoing source distribution estimate with no prior training required. We have evaluated the effectiveness of this approach for anomaly detection using background spectra collected with a Kromek D3S and simulated source spectrum and hyperparameters defined by detector characteristics and information derived from collected spectra. We attained an area under the ROC curve of 0.902 for identifying sparse source peaks within a sparse gamma ray spectrum and achieved a true positive rate of 93% when selecting the optimum thresholding value derived from the ROC curve.
Classification of facial expressions using SVM and HOG

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.
A Novel Grasping Control Method for Dexterous Prosthesis based on Eye-tracking

The state-of-art dexterous prosthetic hand with multi-degree-of-freedom urgently requires a control method to match its dexterity. The purpose of this paper is to develop a method on rapid grasp control of prosthetic hand based on eye-tracking and verify its effectiveness by experiments. Firstly, an interactive experiment platform was established. Based on the platform, the operator can select the necessary grasping pattern from a graphic interface through eye movement and trigger the selected pattern by gazing the target for a short time (~300ms). The selected grasp pattern will be fed back to the operator during the hand prosthesis performing reach-and-grasp tasks. Finally, to verify the effectiveness of this method, experiments were conducted by an able-bodied participant wearing the prosthesis to grasp some daily-used objects. Our result shows that, by adopting the eye tracking technology in the human visual channel, the grasp control of the prosthesis can be supervised and regulated in real-time and thus an effective interaction between the dexterous prosthesis and the operator can be achieved.
EXAMINING THE EFFECT OF CONTEXTUAL TEACHING-LEARNING AND ANXIETY TOWARDS STUDENTS’ SPEAKING SKILLS

This research was aimed at finding out whether: (1) Contextual Teaching Learning has better effect than conventional method towards students speaking skill; (2) the students having low anxiety have better speaking skill than those who have high anxiety; and (3) there is an interaction between CTL and anxiety towards students speaking skill. This research was experimental study within quantitative approach. The total samples of this study were 66 students of tour and travel class of SMKN 2 Mataram that consist of two classes that determined by cluster random sampling technique. The instruments that were used to collect the data were test and questionnaire. The data was analyzed by using multifactor analysis of variance 2x2 and tuckey test. The result of this research revealed that the level of students anxiety that taught by CTL is lower than conventional, while students’ speaking skill score that taught by contextual method was higher than conventional method. The level of anxiety has significant effect toward students’ speaking skill. Therefore, it can be concluded that: 1) Contextual Teaching Learning method has better effect than Convetional method towards students speaking skill of the tour and travel students’ class of SMKN 2 Mataram; 2) The students who have low speaking anxiety have better speaking skill than those who have high speaking anxiety at the tour and travel students’ class of SMKN 2 Mataram; 3) There is an interaction between CTL and students’ anxiety towards students speaking skill of the tour and travel students’ class of SMKN 2 Mataram.
Ecological Big Data Adaptive Compression Method Combining 1D Convolutional Neural Network and Switching Idea

To address large data transmission requirements and high transmission power consumptions characterizing micro-environment monitoring systems that are commonly used in forest health and safety applications, we propose an ecological big data adaptive switching compression method based on a 1D convolutional neural network (1D CNN). First, to ensure that data samples apply to different compression dictionaries, a 1D CNN is used to classify the samples into two sets according to the characteristics of samples. Subsequently, based on the classification results, the switching factor $S$ is defined, such that the discrete cosine transform (DCT) predefined dictionary and the learning dictionary (K-SVD) can be used to adaptively achieve sparse expression and data compression. Finally, the orthogonal matching pursuit (OMP) algorithm reconstructs the sparse signal. To evaluate the feasibility and robustness of the proposed method, we conduct experiments on four types of data: air temperature (AT), air humidity (AH), soil temperature (ST), and soil humidity (SH). The results indicate that the proposed method, compared to K-SVD and DCT dictionary, exhibits excellent performance for all data samples having smaller sparse error (SE), smaller reconstruction error (RE), and larger compression ratio (CR) at different sparsity levels. In particular, when sparsity $K$ is 16, the reconstructed signal is the most similar to the original signal. In addition, the proposed method reduces power consumption by 79.90%, compared with uncompressed data transmission. Considering four factors, the adaptive switching compression method based on 1D CNN has higher reconstruction accuracy and lower power consumption than using only K-SVD or DCT dictionary.
Mining Heterogeneous Influence and Indirect Trust for Recommendation

Relationships between users in social networks have been widely used to improve recommender systems.However, actual social relationships are always sparse, which sometimes bring great harm to the performance of recommender systems.In fact, a user may interact with others that he/she does not connect directly, and thus has an impact on these users.To mine abundant information for social recommendation and alleviate the problem of data sparsity, we study the process of trust propagation and propose a novel recommendation algorithm that incorporates multiple information sources into matrix factorization.We first explore heterogeneous influence strength for each pair of linked users and mine indirect trust between users by using trust propagation and aggregation strategy in social networks.Then, explicit and implicit information of user trust and ratings are incorporated into matrix factorization, and the influence of indirect trust is considered in the recommendation process.Experimental results show that the proposed model achieves better performance than some state-of-the-art recommendation models in terms of accuracy and relieves the cold-start problem.
Underwater Dense Targets Detection and Classification based on YOLOv3

In order to meet the requirements of fast detection and classification of underwater targets during intelligent underwater robot operation, an improved YOLOv3 algorithm named YOLOv3-UW algorithm is proposed to improve the detection accuracy and detection speed. Compared with the YOLOv3 algorithm, the YOLOv3-UW algorithm improves the clusters algorithm of data sets, optimizes the network structure, and improves the residual module. The final experimental results show that detection speed and detection accuracy of the YOLOv3-UW algorithm are higher than the YOLOv3 algorithm.
Integrated Quantum-Walk Structure and NAND Tree on a Photonic Chip

In the age of the post-Moore era, the next-generation computing model would be a hybrid architecture consisting of different physical components, such as photonic chips. In 2008, it was proposed that the solving of the NAND-tree problem can be sped up by quantum walk. This scheme is groundbreaking due to the universality of the NAND gate. However, experimental demonstration has not been achieved so far, mostly due to the challenge in preparing the propagating initial state. Here we propose an alternative solution by including a structure called a "quantum slide," where a propagating Gaussian wave packet can be generated deterministically along a properly engineered chain. In our experimental demonstration, the optical NAND tree is capable of solving computational problems with a total of four input bits, based on the femtosecond laser 3D direct-writing technique on a photonic chip. These results remove one main roadblock to photonic NAND-tree computation, and the construction of a quantum slide may find other interesting applications in quantum information and quantum optics.
Extracting core answers using the grey wolf optimizer in community question answering

A rapidly increasing number of users in community question answering has led to an explosive growth of answers. Thus, it is becoming increasingly more difficult to browse all the answers. Choosing a subset of answers arbitrarily will likely lead to cognitive bias and even poor decisions. Reading a set of core answers that can cover most topics of all the answers is a novel method to overcome information overload and facilitates information retrieval. In this paper, the method named AnsExt for extracting core answers using a kind of swarm intelligence algorithm, named the grey wolf optimizer (GWO), is proposed. First, answers are modeled with the biterm topic model, which fits both short and long texts. Then, factors including quality, coverage, redundancy and number in extracting core answers are defined. Two scenarios are modeled with the requirements of quality, coverage and redundancy: extracting the least number of answers and extracting a predefined number of answers. To extract the minimum number of core questions, a binary GWO is used to resolve the single-objective optimal model. The binary multi-objective GWO is constructed to resolve the optimal model, which is used to extract a predefined number of core answers. Extensive experiments are conducted on real datasets. The results show that the proposed method is feasible and performs well.
PENGEMBANGAN MEDIA PEMBELAJARAN KARTIK (KARTU TEMATIK) TEMA 8 KESELAMATAN DI RUMAH DAN DI PERJALANAN BAGI SISWA SEKOLAH DASAR KELAS II

Abstract The lack of media in thematic learning underlies this study. This study aims at developing KARTIK (Thematic Card), and specifically describes, (1) steps of KARTIK media development, (2) feasibility of KARTIK according to experts, (3) students' responses to the KARTIK media. Drawing upon the notions developed by this Research and Development consisted of seven stages, namely collecting information, planning, developing preliminary product, preliminary field testing, main product revision, main field testing, and revision. The subjects in this study were 10 graders of Grojogan Public Elementary School in limited trial and 30 students in the main trial. Questionnaires were used for data collection, and the data was analyzed using quantitative descriptive. The KARTIK media was developed through seven steps, namely determining the theme, designing card, designing content, designing user manual, designing card box, expert validation, and product trial. The results showed that the KARTIK media was appropriate for use for grade II primary school students. Media experts score 86 (very good), material experts score 42 (good), and learning experts score 40 (good). The Students responses were 96.92% (very good) and 98.2% in the main group trials. The KARTIK media was declared appropriate for use.
PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF PADA MATA PELAJARAN SISTEM KOMPUTER DI SMK

The use of computer-based instructional media at Vocational High School (SMKN) 3 Pariaman is still limited to a media presentation using power point. Along with the advancement of information and communication technology (ICT), a lot of software that can be used to create interactive learning media. The results of the preliminary survey of the implementation of learning system computer at SMKN 3 Pariaman shows that there are no teachers who use interactive learning media with Adobe Director software. ICT facilities and infrastructure such as computers, LCD projectors, and internet at SMKN 3 Pariaman existing one, but have not used optimally. Learning media is one important factor in improving student learning outcomes. The selection of learning media in accordance with the needs and characteristics of students can help students understand the subject matter. This research aims to produce an interactive learning media that valid, practical, and effective to help students to master the subject of System Computer.The research method used was R&amp;D (research and development) by using 4D development model (define, design, develop and disseminate). The results showed that interactive learning media is valid based on the assessment of each aspect by the validator. interactive learning media is considered practical based on observation and responses teacher and student. This shows that, Interactive Learning Media otherwise effective use as a medium of learning in improving student learning outcomes. Keywords: I nteractive Learning Media, Sistem Komputer, Adobe Director
Conformance analysis for comorbid patients in Answer Set Programming

The treatment of comorbid patients is a hot problem in Medical Informatics, since the plain application of multiple Computer-Interpretable Guidelines (CIGs) can lead to interactions that are potentially dangerous for the patients. The specialized literature has mostly focused on the "a priori" or "execution-time" analysis of the interactions between multiple Computer-Interpretable Guidelines (CIGs), and/or CIG "merge". In this paper, we face a complementary problem, namely, the a posteriori analysis of the treatment of comorbid patients. Given the CIGs, the history of the status of the patient, and the log of the clinical actions executed on her, we try to explain the actions executed on the patient (i.e., the log) in terms of the actions recommended by the CIGs, of their potential interactions, and of the possible ways of managing each such interaction, pointing out (i) deviations from CIG recommendations not explained in terms of interaction management (if any) and (ii) unmanaged interactions (if any). Our approach is based on Answer Set Programming, and, to face realistic problems, devotes specific attention to the temporal dimension.
A Systematic Review on Privacy Preserving Data Publishing Techniques

What is Privacy?-According to the definition from Cambridge dictionary, "Someone's right to keep their personal matters and relationships secret".The term privacy is defined as an action where the data is kept hidden from either anonymous user, server to avoid use of malpractice of the data.Healthcare data is considered as a most significant but sensitive data in the world, since it has all private information about patient such as diseases, treatment, prescription, name, address etc.The volume of the data generated in healthcare industry is rapidly growing.In this patient centric world, to get effective results, we need to increase healthcare data utility.With increasing data utility , the privacy of the same data is compromised which is the another important challenge that users and healthcare data publishers are facing, Since there is no monitory control on data which is published on internet.Hiding sensitive healthcare data from either untrusted users or third party data publishers is an important concern today.Healthcare Data Publishing is the process where certain transformation (such as anonymization, generalization, suppression etc.) can be applied before publishing healthcare data online.From the available research, it is seen that such transformations are not susceptible to certain attacks like background knowledge, homogeneity etc.This review paper studies all existing Privacy Preserving Data Publishing (PPDP) schemes using data generalization.The literature review also touches recent researches on ARX tool -which is an open source data de-identification tool for analysing risk and utility factor of healthcare data.The paper finally concludes with feasible research gaps from available literature survey.
Towards a Collaborative Approach to Decision Making Based on Ontology and Multi-Agent System Application to crisis management

The coordination and cooperation of all the stakeholders involved is a decisive point for the control and the resolution of problems. In the insecurity events, the resolution should refer to a plan that defines a general framework of the procedures to be undertaken and the instructions to be complied with; also, a more precise process must be defined by the actors to deal with the case represented by the particular problem of the current situation. Indeed, this process has to cope with a dynamic, unstable and unpredictable environment, due to the heterogeneity and multiplicity of stakeholders, and finally due to their possible geographical distribution. In this article, we will present the first steps of validation of a collaborative decision-making approach in the context of crisis situations such as road accidents. This approach is based on ontologies and multi-agent systems.
Mobile-based monitoring system for an automatic cat feeder using Raspberry Pi

In a Saheeh hadith it is explained that the cat is a clean animal that is free from najis, so keeping a cat is not something that is forbidden. One of the things that is important when keeping a cat is feeding. However, keeping a cat at home takes time and effort. In this digital era, the use of technology has penetrated all aspects of life. The objective of this research is to create a monitoring system for an automatic cat feeder using a webcam and a stepper motor that is connected to a Raspberry Pi as the main controller. The webcam can take pictures (photographs or videos) processed with the fswebcam and the avconv functions on the Raspberry Pi. The stepper motor can rotate the feed valve by utilising a General-Purpose Input Output (GPIO) pin and a program which is inserted into the Raspberry Pi. Next, the Raspberry Pi will be connected to the Internet and a server network so that the system control can be done remotely by using a web browser or web view on a mobile. The overall function of the system in the form of feeding the cat either directly or scheduled, as well as monitoring of photographs or videos around the feed. The results of a questionnaire showed that this system has a need value of 87.3% of 79 respondent cat keepers, meaning cat keepers will be greatly helped by the existence of this system.
Perancangan User Interface Design dan User Experience Mobile Responsive Pada Website Perusahaan

Human-Centered Design Method is a method of approach in the development and design of a system that focuses on the user according to aspects of the needs and habits of the user. Difficulty in accessing information on the website becomes a problem faced by the user and in terms of visual website can not be responsive when accessed via mobile. The initial stage carried out in this method is observation which aims to find and to better understand the problems faced by users to conduct testing to find out whether the solutions provided can be understood and easily used by the user. Website testing is done by giving tasks to the user to interact on the website prototype, as the final result of success in the aspect of ease and comfort of the user using the website. After testing the user directly the test results are obtained, ie the user already feels quite understanding and easy when using the website that was created. The responsive mobile feature created also makes users feel helped when using a website on a smartphone.