Reviewerly/exHarmony huggingface dataset:
Jaringan Syaraf Tiruan Model Backpropagation untuk Peramalan Suhu Minimum dan Maksimum, Kelembaban, Tekanan Udara, Jumlah Hari Hujan, dan Curah Hujan Bulanan di Kota Mataram

Climate conditions are very influential on various sectors of human life, such as agriculture. Extreme climate can affect the planting and harvest period which can eventually lead to crop failure. Climate forecasting can be done to help mapping the planting period so that agricultural productivity is optimal. Artificial neural network is a method that can be used to forecast climate in the future. In this research, a backpropagation model was used to forecast monthly data on minimum and maximum temperatures, humidity, air pressure, number of rainy days, and rainfall in Mataram city. The accuracy of forecasting result is measured based on MSE (mean square error). Based on the research conducted, the MSE of testing phase are minimum temperature 3.59 x 10-3, maximum temperature 1.97 x 10-4, humidity 2.11 x 10-3, air pressure 2.50 x 10-3, number of rainy days 7.97 x 10-4, and rainfall 1.00 x 10-3.
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability

Nowadays, the use of machine learning models is becoming a utility in many applications. Companies deliver pre-trained models encapsulated as application programming interfaces (APIs) that developers combine with third party components and their own models and data to create complex data products to solve specific problems. The complexity of such products and the lack of control and knowledge of the internals of each component used cause unavoidable effects, such as lack of transparency, difficulty in auditability, and emergence of potential uncontrolled risks. They are effectively black-boxes. Accountability of such solutions is a challenge for the auditors and the machine learning community. In this work, we propose a wrapper that given a black-box model enriches its output prediction with a measure of uncertainty. By using this wrapper, we make the black-box auditable for the accuracy risk (risk derived from low quality or uncertain decisions) and at the same time we provide an actionable mechanism to mitigate that risk in the form of decision rejection; we can choose not to issue a prediction when the risk or uncertainty in that decision is significant. Based on the resulting uncertainty measure, we advocate for a rejection system that selects the more confident predictions, discarding those more uncertain, leading to an improvement in the trustability of the resulting system. We showcase the proposed technique and methodology in a practical scenario where a simulated sentiment analysis API based on natural language processing is applied to different domains. Results demonstrate the effectiveness of the uncertainty computed by the wrapper and its high correlation to bad quality predictions and misclassifications.
Trust, Literasi Media, Kepribadian Hexaco dan Husnudzon Terhadap Kecenderungan Menggunakan Media Sosial

AbstractThis study was conducted to determine the significance of the influence of trust information, media literacy, HEXACO personality and Husnudzon towards the tendency of using social media for seeking information at student college. The subject of this research was 280 students taken by probability sampling technique, namely cluster sampling. CFA (Confirmatory Factor Analysis) was used to test the validity of measuring instruments and logistic regression analysis is used to test the research hypothesis. The results showed that there was a significant joint effect of trust, media literacy, HEXACO's personality and Husnudzon towards the tendency of using social media for seeking information. Hypothesis test results show that six variables namely trust, structure of media literacy knowledge, personal media literacy locus, extraversion personality type, Husnudzon towards God, and Husnudzon towards others have a significant influence on the tendency of using social media for seeking information. Meanwhile, media literacy competencies, personality types; honesty-humility, emotionality, agreeableness, conscientiousness, and openness to experience do not have a significant influence on the tendency of using social media for seeking information. The results also show the proportion of variance from the tendency of using social media for seeking information explained by all independent variables is 18.0% while the remaining 82.0% is influenced by other variables outside of this study.AbstrakPenelitian ini dilakukan untuk mengetahui signifikansi pengaruh trust informasi, literasi media, kepribadian HEXACO dan husnudzon terhadap kecenderungan menggunakan media sosial untuk mencari informasi pada mahasiswa. Subjek penelitian ini berjumlah 280 mahasiswa yang diambil dengan teknik probability sampling yaitu cluster sampling. CFA (Confirmatory Factor Analysis) digunakan untuk menguji validitas alat ukur dan logistic regression analysis digunakan untuk menguji hipotesis penelitian. Hasil penelitian menunjukkan bahwa ada pengaruh secara bersama-sama yang signifikan dari trust, literasi media, kepribadian HEXACO dan husnudzon terhadap kecenderungan menggunakan media sosial untuk mencari informasi. Hasil uji hipotesis menunjukkan bahwa enam variabel yaitu trust, struktur pengetahuan literasi media, lokus personal literasi media, tipe kepribadian extraversion,husnudzon terhadap Allah, dan husnudzon terhadap sesamamemiliki pengaruh yang signifikan terhadap kecenderungan menggunakan media sosial untuk mencari informasi. Sementara itu, kompetensi-keterampilan literasi media, tipe kepribadian; honesty-humility,emotionality, agreeableness, conscientiousness, dan openness to experience tidak memiliki pengaruh yang signifikan terhadap kecenderungan menggunakan media sosial untuk mencari informasi. Hasil penelitian juga menunjukkan proporsi varians dari kecenderungan menggunakan media sosial untuk mencari informasi yang dijelaskan oleh seluruh variabel independen adalah 18.0% sedangkan 82.0% sisanya dipengaruhi oleh variabel lain diluar penelitian ini.
Headgear for Weakening Impact from Concussion in Rugby Games: Design and Development

Rugby headgear is one of the important protective tool for protecting rugby players from head injury. However, there are still some professional or amateur rugby players who are not concerned about the use of headgear as a protective device. Rugby headgear has been created to protect the heads of rugby players in particular to avoid minor injuries such as wounds because rugby is a sport that requires strong physical movements and abusive techniques. At the same time, the ability of rugby headgear to protect against the occurrence of brain injury is questionable. Therefore, the objective of this project is to propose a conceptual design of rugby headgear by taking into account factors to prevent or reduce the potential of brain injury. The methodology used was a survey of professional and amateur rugby players to determine the need for the design of the headgear, conceptual map for the idea of development, House of Quality (HoQ) in assessing the needs of rugby players, Morphological Chart in determining the concept, Pugh's Selection Method in finding the most appropriate design. Headgear that is round and cover most of the heads has been selected as a conceptual design with sorbothane as core material. Valco strap was used as fastener and sponge in the head for the comfort to the player’s head
On the Filter Issues in Multiplexing Classical and QKD Links through WSS-Based Nodes

We present an extensive study on the quantum link multiplexed with intense classical signal in a WSS-based node. Theoretical results on QBER and SKR performance revealed the dependence of BB84-QKD link on passband spectral response.
Experimental validation of wind energy estimation

Wind power assessment in complex terrain is a very demanding task. Modeling wind conditions with standard linear models does not sufficiently reproduce wind conditions in complex terrains, especially on leeward sides of terrain slopes, primarily due to the vorticity. A more complex non-linear model, based on Reynolds averaged Navier-Stokes equations has been used. Turbulence was modeled by modified two-equations k-? model for neutral atmospheric boundary-layer conditions, written in general curvelinear non-orthogonal co-ordinate system. The full set of mass and momentum conservation equations as well as turbulence model equations are numerically solved, using the as CFD technique. A comparison of the application of linear model and non-linear model is presented. Considerable discrepancies of estimated wind speed have been obtained using linear and non-linear models. Statistics of annual electricity production vary up to 30% of the model site. Even anemometer measurements directly at a wind turbine?s site do not necessarily deliver the results needed for prediction calculations, as extrapolations of wind speed to hub height is tricky. The results of the simulation are compared by means of the turbine type, quality and quantity of the wind data and capacity factor. Finally, the comparison of the estimated results with the measured data at 10, 30, and 50 m is shown.
Extensions of Hidden Markov Models for supporting instructors in the analysis of training operations in an Unmanned Aircraft System

The increasing use of Unmanned Aircraft Systems has not been met with appropriate integration of training science. Most of the tasks of evaluation and analysis carried out by an instructor during the debriefing of a training session are still performed rudimentarily and individually for each operator, due to the current lack of methods and tools capable of doing it automatically on a large scale. This work is focused on providing intelligent and automated methods to training operations in a UAS by supporting instructors in some debriefing tasks, such as the extraction of behavioural patterns. In this regard, the current methods based on Hidden Markov Models (HMMs) are used to create predictive models of the operator’s behaviour. These methods have been extended in two different ways: first, the use of Multichannel HMMs is proposed in order to enrich the meaningfulness of the model states with the usage of parallel sources of information from the mission logs; secondly, the inner modelling limitations of HMMs are considered, and based on this, the applicability of a more flexible approach based on high order Double Chain Markov Models (DCMMs) is studied. In order to demonstrate the effectiveness of each of the proposed approaches, several experiments have been carried out in a lightweight simulation environment, with inexperienced operators.
Document Validation using Blockchain: A validation scheme for natural persons documents

The great extension of Brazil's territory, combined with its demographics of more than 200 million inhabitants, results in a complex, slow and expensive notary system. Blockchain technologies can be of huge help in this scenario. It provides a decentralized peer-to-peer way of storing and validating documents. In this article, we start the discussion about a blockchain-based national notary system with means to store and validate the natural person's public documents. We prototype a solution, comprising of birth, marriage, divorce, and death documents/certificates. In the end, we present a comparison between the operational costs of the implemented prototype and the current notary system.
Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation

Lectures translation is a case of spoken language translation and there is a lack of publicly available parallel corpora for this purpose. To address this, we examine a language independent framework for parallel corpus mining which is a quick and effective way to mine a parallel corpus from publicly available lectures at Coursera. Our approach determines sentence alignments, relying on machine translation and cosine similarity over continuous-space sentence representations. We also show how to use the resulting corpora in a multistage fine-tuning based domain adaptation for high-quality lectures translation. For Japanese--English lectures translation, we extracted parallel data of approximately 40,000 lines and created development and test sets through manual filtering for benchmarking translation performance. We demonstrate that the mined corpus greatly enhances the quality of translation when used in conjunction with out-of-domain parallel corpora via multistage training. This paper also suggests some guidelines to gather and clean corpora, mine parallel sentences, address noise in the mined data, and create high-quality evaluation splits. For the sake of reproducibility, we will release our code for parallel data creation.
Identificação com uma Variação de Mínimos Quadrados Recursivo aplicado a um Manipulador Robótico

Robotics has been reaching a lot of space in recent years and is evolving every day.Today, robotics is seen as an important area in many applications.Dynamic control is an essential tool for robotics.To design the control, a well-modeled / identified plant is required to have a well-defined controller design.A misidentified system can compromise the project, causing it to have a control effort and a very long tuning time, which is not good at all.The present work aims to show some solutions for the identification of a robotic manipulator with a hybrid system of a recursive least squares technique and a metaheuristic called differential evolution (DE).The results are compared with other methods and can surpass them in order to validate the study and showing its potential.
Halftoning-based BTC image reconstruction using patch processing with border constraint

This paper presents a new halftoning-based block truncation coding (HBTC) image reconstruction using sparse representation framework. The HBTC is a simple yet powerful image compression technique, which can effectively remove the typical blocking effect and false contour. Two types of HBTC methods are discussed in this paper, i.e., ordered dither block truncation coding (ODBTC) and error diffusion block truncation coding (EDBTC). The proposed sparsity-based method suppresses the impulsive noise on ODBTC and EDBTC decoded image with a coupled dictionary containing the HBTC image component and the clean image component dictionaries. Herein, a sparse coefficient is estimated from the HBTC decoded image by means of the HBTC image dictionary. The reconstructed image is subsequently built and aligned from the clean, i.e. non-compressed image dictionary and predicted sparse coefficient. To further reduce the blocking effect, the image patch is firstly identified as “border” and “non-border” type before applying the sparse representation framework. Adding the Laplacian prior knowledge on HBTC decoded image, it yields better reconstructed image quality. The experimental results demonstrate the effectiveness of the proposed HBTC image reconstruction. The proposed method also outperforms the former schemes in terms of reconstructed image quality.
A Weighted Viewport Quality Metric for Omnidirectional Images

Thanks to the ability to bring immersive experiences to users, Virtual Reality (VR) technologies have been gaining popularity in recent years. A key component in VR systems is omnidirectional content, which can provide 360-degree views of scenes. However, at a given time, only a portion of the full omnidirectional content, called viewport, is displayed corresponding to the user's current viewing direction. In this work, we first develop Weighted-Viewport PSNR (W-VPSNR), an objective quality metric for quality assessment of omnidirectional content. The proposed metric takes into account the foveation feature of the human visual system. Then, we build a subjective database consisting of 72 stimuli with spatial varying viewport quality. By using this database, an evaluation of the proposed metric and four conventional metrics is conducted. Experiment results show that the W-VPSNR metric well correlates with the mean opinion scores (MOS) and outperforms the conventional metrics. Also, it is found that the conventional metrics do not perform well for omnidirectional content.
Withdrawal Notice: Empowering Secured Outsourcing in Cloud Storage Through Data Integrity Verification

The article has been withdrawn at the request of the authors and editor of the journal Recent Advances in Computer Science and Communications. ; Bentham Science Disclaimer: The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php Bentham Science Disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.
The Implementation of Multiliteration Learning Models in Improving The Ability of Reading Comprehension for Fifth Grade of Primary School

This research is motivated by the low reading comprehension ability of students in learning Indonesian. This study aims to improve students' reading comprehension skills in class V of Talaga Kulon 1 Primary School by using multiliteration learning models. The research method used by researchers is classroom action research in collaboration with fifth grade teachers at Talaga Kulon 1 Primary School, with a total of 30 students in class V consisting of 16 male students and 14 female students. This research took place in 3 cycles, each cycle consisting of 3 actions. The techniques used in data collection are observation, tests, interviews, documentation, and field notes. The results showed that the implementation of the multiliteration model could improve students' reading comprehension skills in class V of Talaga Kulon 1 Elementary School.
Development of Interactive Tutoring System with Question Answering Function based on Knowledge Graph

Interactive tutoring systems is expected to contribute self-learning in various type of topics, such as machine learning. In this study, we develop an interactive tutoring system that performs tutoring in a three-party dialogue form using multiple virtual agents. It would reduce the psychological burden of the learner compared with one-to-one tense dialogue. To adapt this system to various levels of users, question-answering functionality is essential. The proposed system generates an answer to the question using a knowledge graph automatically constructed from the textbook.
Inverse kinematics of curved surface robots and solution by numerical simulation

In this paper, we will discuss the inverse kinematics problem of curved surface robots. In this problem, the shape of a point, a curve or a partial area is given as constraint conditions. We need to formulate the inverse kinematics problem based on these. We will present this formulation and show numerical simulation results on the simplest inverse kinematics problem when the overall shape of the curved surface is given as a constraint condition in this paper.
Large-scale Gender/Age Prediction of Tumblr Users

Tumblr, as a leading content provider and social media, attracts 371 million monthly visits, 280 million blogs and 53.3 million daily posts. The popularity of Tumblr provides great opportunities for advertisers to promote their products through sponsored posts. However, it is a challenging task to target specific demographic groups for ads, since Tumblr does not require user information like gender and ages during their registration. Hence, to promote ad targeting, it is essential to predict user's demography using rich content such as posts, images and social connections. In this paper, we propose graph based and deep learning models for age and gender predictions, which take into account user activities and content features. For graph based models, we come up with two approaches, network embedding and label propagation, to generate connection features as well as directly infer user's demography. For deep learning models, we leverage convolutional neural network (CNN) and multilayer perceptron (MLP) to prediction users' age and gender. Experimental results on real Tumblr daily dataset, with hundreds of millions of active users and billions of following relations, demonstrate that our approaches significantly outperform the baseline model, by improving the accuracy relatively by 81% for age, and the AUC and accuracy by 5\% for gender.
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In this work we focus on their ability to have invariance towards the presence or absence of details. For example, humans are able to watch cartoons, which are missing many visual details, without being explicitly trained to do so. As another example, 3D rendering software is a relatively recent development, yet people are able to understand such rendered scenes even though they are missing details (consider a film like Toy Story). The failure of machine learning algorithms to do this indicates a significant gap in generalization between human abilities and the abilities of deep networks. We propose a dataset that will make it easier to study the detail-invariance problem concretely. We produce a concrete task for this: SketchTransfer, and we show that state-of-the-art domain transfer algorithms still struggle with this task. The state-of-the-art technique which achieves over 95\% on MNIST $\xrightarrow{}$ SVHN transfer only achieves 59\% accuracy on the SketchTransfer task, which is much better than random (11\% accuracy) but falls short of the 87\% accuracy of a classifier trained directly on labeled sketches. This indicates that this task is approachable with today's best methods but has substantial room for improvement.
Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation. In this paper, we propose a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets for multi-organ segmentation. In addition, a new network architecture for multi-scale feature abstraction is proposed to integrate pyramid input and feature analysis into a U-shape pyramid structure. To bridge the semantic gap caused by directly merging features from different scales, an equal convolutional depth mechanism is introduced. Furthermore, we employ a deep supervision mechanism to refine the outputs in different scales. To fully leverage the segmentation features from all the scales, we design an adaptive weighting layer to fuse the outputs in an automatic fashion. All these mechanisms together are integrated into a Pyramid Input Pyramid Output Feature Abstraction Network (PIPO-FAN). Our proposed method was evaluated on four publicly available datasets, including BTCV, LiTS, KiTS and Spleen, where very promising performance has been achieved. The source code of this work is publicly shared at https://github.com/DIAL-RPI/PIPO-FAN for others to easily reproduce the work and build their own models with the introduced mechanisms.