Reviewerly/exHarmony huggingface dataset:
THE STUDENTS’ SPEAKING ANXIETY ON THE YOUTUBE VIDEO PROJECT IN EFL LEARNING IN INDONESIA

This research aimed to portray the occurrence of anxiety in the YouTube video project conducted by non-English students. It was descriptive-qualitative research. One hundred twenty-four students participated in this research. The research findings showed that 65 students felt the anxiety, and 59 were not anxious in doing the YouTube video project. There was a slight difference between those two findings. Some factors influenced the students’ anxiety, namely students’ low personal perception, peers’ negative judgment, and quite intimidating learning environment. While the factors that support the absence of anxiety were students’ positive own perception and fun-less intimidating and encouraging learning environment. However, the research finding highlighted that YouTube video project was the preferable media that was based on the 104 students’ responses claiming that they prefer doing YouTube video project than other speaking activities such as storytelling, oral presentation, and speech.
Comparison of clustering optimization for classification with PSO algorithms

In this paper, we compare Particle Swarm Optimization (PSO) algorithms for classification based on clustering.Clustering is presented within the proposed PSO algorithms as an optimization problem, and captured in a so called fitness function.Clusters are presented with centers that should represent the classes hidden in data.With the help of PSO algorithms and proper fitness function, we optimize centers of the clusters.Because clustering belongs to a field of unsupervised learning methods, we redefine the clustering problem to supervised learning with a new fitness function that helps PSO algorithms in finding clusters centers that can classify instances of data to the right class.Two experiments are performed in our study.In the former, various fitness functions for classification based on clustering are tested.The latter measures the performance of PSO algorithms using the best fitness function from the first experiment.For testing the PSO algorithms, we use three different datasets.Friedman non-parametric statistical test, and Nemenyi and Wilcoxon post-hoc tests are conducted to properly compare the PSO algorithms.
Towards Automatic Cataloging of Image and Textual Collections with Wikipedia

In recent years, a large amount of multimedia data consisting of images and text have been generated in libraries through the digitization of physical materials into data for their preservation. When they are archived, appropriate cataloging metadata are assigned to them by librarians. Automatic annotations are helpful for reducing the cost of manual annotations. To this end, we propose a mapping system that links images and the associated text to entries on Wikipedia as a replacement for annotation by targeting images and associated text from photo-sharing sites. The uploaded images are accompanied by descriptive labels of contents of the sites that can be indexed for the catalogue. However, because users freely tag images with labels, these user-assigned labels are often ambiguous. The label "albatross", for example, may refer to a type of bird or aircraft. If the ambiguities are resolved, we can use Wikipedia entries for cataloging as an alternative to ontologies. To formalize this, we propose a task called image label disambiguation where, given an image and assigned target labels to be disambiguated, an appropriate Wikipedia page is selected for the given labels. We propose a hybrid approach for this task that makes use of both user tags as textual information and features of images generated through image recognition. To evaluate the proposed task, we develop a freely available test collection containing 450 images and 2,280 ambiguous labels. The proposed method outperformed prevalent text-based approaches in terms of the mean reciprocal rank, attaining a value of over 0.6 on both our collection and the ImageCLEF collection.
Efficient Collision Detection for Path Planning for Industrial Robots

Efficient collision detection is crucial for the success of automated process planning and path planning for robotic manipulation and assembly.Yet, collision detection for articulated industrial robots holds various challenges.This paper gives an overview of these challenges, and presents an efficient implementation of collision detection techniques for such robots.The applicability of the developed techniques to support path planning in an industrial test case is also illustrated.
'Blending Dimensions' when Composing for DMI and Symphonic Orchestra

With a new digital music instrument (DMI), the interface itself, the sound generation, the composition, and the performance are often closely related and even intrinsically linked with each other. Similarly, the instrument designer, composer, and performer are often the same person. The Academic Festival Overture is a new piece of music for the DMI Trombosonic and symphonic orchestra written by a composer who had no prior experience with the instrument. The piece underwent the phases of a composition competition, rehearsals, a music video production, and a public live performance. This whole process was evaluated reflecting on the experience of three involved key stakeholder: the composer, the conductor, and the instrument designer as performer. ‘Blending dimensions’ of these stakeholder and decoupling the composition from the instrument designer inspired the newly involved composer to completely rethink the DMI’s interaction and sound concept. Thus, to deliberately avoid an early collaboration between a DMI designer and a composer bears the potential for new inspiration and at the same time the challenge to seek such a collaboration in the need of clarifying possible misunderstandings and improvement.
Analysis of Steganography on TIFF Image using Spread Spectrum and Adaptive Method

Nowadays the development of technology and information is very rapid and fast.Anyone can easily access or send any data and information which creates a problem to improve the system.The hidden message technique called steganography is a technique that allows users to hide a message in another message.We can use steganography to protect confidential data or information.This study will discuss about spread spectrum and adaptive method on Temporary Instruction File Format (TIFF).By using these methods, we compare how many characters from each method can be inserted in to TIFF image.Analysis using PSNR and MSE are also presented in this study.
Normalization of Kazakh Texts

Kazakh, like other agglutinative languages, has specific difficulties on both recognition of wrong words and generation the corrections for misspelt words.The main goal of this work is to develop a better algorithm for the normalization of Kazakh texts based on traditional and machine learning methods, as well as the new approach which is also considered in this paper.The procedure of election among methods of normalization has been conducted in a manner of comparative analysis.The results of the comparative analysis turned up successful and are shown in detail.
Implementasi Bussiness Intelligent dalam e-Tourism Berbasis Big Data

Abstract&#x0D; The use of information technology that is integrated with work processes in an organization has become an absolute necessity. The availability of complete, correct and accurate data and information has become a basic requirement for the survival of an organization. Business Intelligence (BI) is a form of implementation that is able to answer the above needs. BI has been widely used by organizations in managing data and information to support decision making. BI is usually associated with efforts to maximize the performance of an organization. Business Intelligence System is a term that is generally used for the type of application or technology used to assist BI activities, such as collecting data, providing access, and analyzing data and information about company performance. Along with the rapid online-based information systems including e-tourism, creating a huge data explosion on the internet (bigdata). The very high growth of tourism data on the internet can be utilized for the needs of the tourism industry and research needs in the field of tourism.&#x0D; Keywords: intelligent business, e-tourism, big data
Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism

Many previous studies on relation extraction have been focused on finding only one relation between two entities in a single sentence.However, we can easily find the fact that multiple entities exist in a single sentence and the entities form multiple relations.To resolve this problem, we propose a relation extraction model based on a dual pointer network with a multi-head attention mechanism.The proposed model finds n-to-1 subject-object relations by using a forward decoder called an object decoder.Then, it finds 1-to-n subject-object relations by using a backward decoder called a subject decoder.In the experiments with the ACE-05 dataset and the NYT dataset, the proposed model achieved the state-of-the-art performances (F1-score of 80.5% in the ACE-05 dataset, F1-score of 78.3% in the NYT dataset) Dual Pointer Network Model for Relation Extraction
Analysis on Preprocessing Techniques for Offline Handwritten Recognition

Analysis of document images for information recovery has been exceptionally prominent in the later past. Wide collection of information, which has been generally put away on paper, is as of now being changed over into electronic form for superior storage and intelligent processing. This needs processing of documents by utilizing different processing strategies. Pre-processing techniques are advantageous in the recognition of document images as well as medical images. The most significant goal of image recognition system is to perform operations on images to find patterns and to retrieve information from the images for feature extraction process. In this paper, we emphasize on various techniques for pre-processing an image that aids in the further process of Image recognition.
SALMON: Sharing, Annotating and Linking Learning Materials Online

In consideration of the growing availability of mobile devices for students, web-based and shared annotations of learning materials are becoming more popular. Annotating learning material is a method to promote engagement, understanding, and independence for all learners in a shared environment. Open educational resources have the potential to add valuable information and close the gap between learning materials by automatically linking them. However, current popular web-based text annotation tools for learners, such as Hypothesis and Diigo, do not support learners in discovering new learning resources based on the context, metadata and the content of the annotated resource. In this article, we present SALMON, a collaborative web-based annotation system, which dynamically links and recommends learning resources based on annotations, content and metadata. It facilitates methods of semantic analysis in order to automatically extract relevant content from lecture materials in the form of PDF web documents. SALMON categorizes documents automatically in a way that finding similar resources becomes faster for the learners and they can discover communities for interesting topics.
A conceptual integrated health information systems framework in postnatal care for modern and traditional malay medicine

&lt;span&gt;This paper proposes an integrated health information systems framework for Traditional Malay Medicine (TMM) and modern medicine in the field of postnatal care. A qualitative study was conducted via healthcare experts in the field of modern medicine and Traditional Malay Medicine to assess the current situation and identify the research gap and point of isolation between both traditional and modern medicine field. A total of 26 healthcare practitioners whom represented five different set of healthcare organisations participated in this study. The healthcare practitioners consist of modern and traditional Malay medicine background with and without proper training. Results show that there is a gap in the current people, process and technology areas of the current framework. A novel conceptual framework, MyPostnatal, proposes the existence of a sufficiently generic, extensible in-formation model where new data sources can be integrated without major changes to the data scheme. Human and organization factors are also highlighted to stimulate the adoption towards electronic health records.&lt;/span&gt;
Cross-Domain Training for Goal-Oriented Conversational Agents

Goal-Oriented Chatbots in fields such as customer support, providing specific information or general help with bookings or reservations, suffer from low performance partly due to the difficulty of obtaining large domain-specific annotated datasets.Given that the problem is closely related to the domain of the conversational agent and that data belonging to a specific domain is difficult to annotate, there have been some attempts at surpassing these challenges such as unsupervised pre-training or transfer learning between different domains.A more thorough analysis of the transfer learning mechanism is justified by the significant boost of the results demonstrated in the results section.We describe extensive experiments using transfer learning and warm-starting techniques with improvements of more than 5% in relative percentage of success rate in the majority of cases, and up to 10x faster convergence as opposed to training the system without them.
FENOMENOLOGI LITERASI MEDIA PADA REMAJA DI KOTA PEKANBARU

The 4.0 of industrial revolution had a considerable impact on the advancement of media and information technology. The flow of information that can no longer be dammed causes anyone, as a media user, to be ready and have a filter to use it. The impact of the media has a great influence on the younger generation. The ease of access makes this media an inseparable important part of adolescents, including teenagers in Pekanbaru City. However, in today’s reality not a few teenagers who are trapped in the nature of access. This phenomenon is of concern to researchers to find out how media literacy experiences among teenagers in Pekanbaru City by Individual Competence Framework. Adolescents referred to in this study are students who sit in the Senior High School in the City of Pekanbaru, which is a representation of the teenagers. The research method used is qualitative with a phenomenological approach. The results of this study indicate that media literacy experience in adolescents in Pekanbaru city is a three level of adolescent, there are: technical skills at the medium level, and critical understanding at the basic level. At the level of non-formal education, adolescents barely get it from the family, but instead get it from the peergroup.&#x0D;
TaRad: A Thing-Centric Sensing System for Detecting Activities of Daily Living

Activities of Daily Living Scale (ADLs) is widely used to evaluate living abilities of the patients and the elderly. Most of the currently proposed approaches for tracking indicators of ADLs are human-centric. Considering the privacy concerns of the human-centric approaches, a new thing-centric sensing system, named TaRad, for detecting some indicators of ADLs (i.e. using fridge, making a phone call), through identifying vibration of objects when a person interacts with objects. It consists of action transceivers (named ViNode), smart phones and a server. By taking into account the limited computation resource of the action transceiver, and the drift and accuracy issues of the cheap sensor, a method of extracting features from the vibration signal, named ViFE, along with a light-weight activity recognition method, named ViAR, have been implemented in ViNode. Besides, an operator recognition method, named ViOR, has been proposed to recognize the acting person who generates vibration of action transceiver, when two or more people exist simultaneously within an area. Experimental results verify the performance of TaRad with different persons, in terms of the sensitivity to correctly detect the activities, and probability to successfully recognize the operators of the activities.
A Mega-Trend-Diffusion and Monte Carlo based virtual sample generation method for small sample size problem

Abstract Data-driven modeling has attracted wide attention in academia because of its effectiveness. However, Due to the lack of data, some traditional modeling methods, such as extreme learning machine (ELM), can’t achieve high learning accuracy. A novel approach based on Mega-Trend-Diffusion (MTD) and Monte Carlo is presented in this paper to deal with the problem, named Monte Carlo Mega-Trend-Diffusion (MCMTD). The proposed approach utilizes MTD to estimate the acceptable range of the attributions and Latin hypercube sampling method to sample. ELM is employed to establish the prediction model. In this paper, two real data sets, the multi-layer ceramic capacitors (MLCC) and the purified terephthalic acid (PTA), are used to verify the effectiveness and reasonability of MCMTD. The experimental results show that MCMTD can significantly enhance the accuracy and ability of the forecasting model.
Estimating the Distribution of Heavy Metals in Soil from Airborne Hyperspectral Imagery Over Jilin Gongzhuling Gold Mining Area of China

In this study, we used HyMap-C airborne hyperspectral imagery and ground samples collected synchronously to explore the estimation of soil heavy metal concentration. Preprocessing methods such as first-order derivative were used to enhance the weak spectral information related heavy metals. The multivariate stepwise regression method was used to select the spectral characteristics and establish the inversion model. The samples were divided into 3 parts, model set, validation set and test set. For the arsenic (As) the errors of the samples sets were 0.55, 0.75, 0.44, and the root-mean-square error were 51.20, 30.12, 32.78 mg/kg respectively. The results show that this method can predict the heavy metals arsenic in the study area.
Video Surveillance System with a Deep Learning Approach

Abstract— The application of in-depth learning methods has been successfully applied in computer vision task with the ability to learn the features of differences in real world images by directly from the original image by passing layer after layer to get the high dimensions image, in this study we applied the YOLO method approach with network adaptation features based on Darknet-53 on a video dataset recorded by the activities of University of Indonesia Prima (UNPRI) students with are conditions of video with different objects as a surveillance system, based on the results of research into object classification produces an overall accuracy of 93%, but for the classification of objects bikes, buses, and cars have the lowest accuracy of 30% for bikes, 54% of cars and buses by 40% so it is necessary to develop methods to improve accuracy.
Comparison of deep learning and classical breast mass classification methods in ultrasound

We developed breast mass classification methods based on deep convolutional neural networks (CNNs) and morphological features (MF), then compared those to assessment of four experienced radiologists employing BI-RADS protocol. The classification models were developed based on 882 clinical ultrasound B-mode images of masses with confirmed findings and regions of interest indicating mass areas. Various transfer learning techniques, including fine-tuning of a pre-trained CNN, were investigated to develop deep learning models. A matching layer technique was applied to convert gray-scale images to red, green, blue to efficiently utilize discrimination of the pre-trained model. For the classical approach, we calculated MF related to breast mass shape (e.g., height-width ratio, circularity) and then trained binary classifiers. We additionally evaluated both approaches using two publicly available US datasets. Several statistical measures (area under the receiver operating curve [AUC], sensitivity and specificity) were used to assess the classification performance on a test set of 150 cases. The matching layer significantly increased AUC from 0.895 to 0.936 while radiologists’ AUCs ranged from 0.806 to 0.882. This study shows both deep learning and classical models achieve high performance. When developed as a clinical tool, the methods examined in this study have potential to aid radiologists accurate breast mass classification with ultrasound.
Circle Hough Transform on Real-Time Object Following Unmanned Surface Vessel

Indonesia as the largest archipelago in the world, is lacking in sophisticated naval army. USV short for unmanned surface vehicle could provide risk-free naval surveyor and firepower army. In this research we implement object following system on USV. Sensor for navigation is using a mono-camera to capture series of images in real-time, the image resolution used is low resolution image at 240p. The application of object detection using Circle Hough Transform is to navigate the ship based on distance and direction between the object and the USV. USV will moves based on information from captured object in the images. USV equipped with single brushless motor and rudder to accommodate USV’s maneuver. The speed control used is PID control, with object distance as feedback. In this research we conclude that the ASV is able to follow the object using visual information and speed controlled by PID. With Kp at 3, Ki at 0.3, and Kd at 2, USV could close the distance to reference in 11 seconds.
$G$-codes over formal power series rings and finite chain rings

In this work, we define $G$-codes over the infinite ring $R_\infty$ as ideals in the group ring $R_\infty G$. We show that the dual of a $G$-code is again a $G$-code in this setting. We study the projections and lifts of $G$-codes over the finite chain rings and over the formal power series rings respectively. We extend known results of constructing $\gamma$-adic codes over $R_\infty$ to $\gamma$-adic $G$-codes over the same ring. We also study $G$-codes over principal ideal rings.
Multiple-Degradation Video Super-Resolution with Direct Inversion of the Low-Resolution Formation Model

With the increase of popularity of high and ultra high definition displays, the need to improve the quality of content already obtained at much lower resolutions has grown. Since current video super-resolution methods are trained with a single degradation model (usually bicubic downsampling), they are not robust to mismatch between training and testing degradation models, in which case their performance deteriorates. In this work we propose a new Convolutional Neural Network for video super resolution which is robust to multiple degradation models and uses the pseudo-inverse image formation model as part of the network architecture during training. The experimental validation shows that our approach outperforms current state of the art methods.