Reviewerly/exHarmony huggingface dataset:
Penggunaan sistem informasi geografis (sig) untuk pemetaan kerentanan wilayah berdasarkan faktor risiko kejadian diare pada balita

Background: Diarrheal is a disease that until now is still a world health problem especially in developing countries and if handling is not right can lead to death.Geographic Information Systems (GIS) can be used to create health maps and obtain epidemiological data.This information when mapped will be a useful tool for mapping out diarrheal disease characteristics.Objective: This study aims to implement the use of geographic information system for mapping of regional vulnerability based on risk factors of diarrhea in children under five years.Methods: Type of observational analytic study with cross sectional design, using GIS tool to determine spatial distribution of diarrhea and region vulnerability based on risk factors of diarrhea in children underfive in Kendari City. Results:The multivariate analysis showed that the risk factors for underfive diarrhea were drinking water source (p=0,009), latrine condition (p=0,007), and sewerage channels (p=0,048).The result of spatial analysis of regional vulnerability using GIS is the village of Puuwatu with the highest number of cases as much as 7.8% and the majority of respondent families who have drinking water sources, latrines and sewerage channels are not eligible in the village puuwatu. Conclusion:The use of geographic information system can be implemented for vulnerability mapping based on risk factors of diarrhea occurrence in toddlers in Kendari city where puuwatu village is the most vulnerable area with the most dominant risk factor ie drinking water source, latrine and sewerage channels.
Design and Development of A Diagnostic System for Early Prediction of P53 Mutation Causing Cancer from Microscopic Biopsy Images

The major complication associated with cancer care is delayed cancer detection, which would also reduce the likelihood of survival. This situation could be resolved to some extend with an early diagnostic system. In the current study, designing an early detection system for TP53 mutation, which is a common primary mutation for most of the types of cancer, has been carried out using the `Pharmacogenomics', `Gene expression profiling' and `Deep imaging processing technique'. The input for the analysis is microscopic biopsy images collected from the `Expression atlas database'. The high level of expression of TP53 gene mutation has been observed in Breast and Ovarian cancers samples. The involvement of associated genes like BARD1, CHEK2, ATM, BRCA2, BRCA1, and RAD51 has also been analyzed. A deep neural network with a `Siamese Neural Network (SNN)', architecture has been implemented using one-short learning process to comprehend the data and make valid predictions on TP53 mutation. This `algorithm and learning platform' helps in making dependable predictions even from a low input data and the machine's measured predictive performance is 89%.
Fast Maximum A Posteriori Estimation using Particle Filter and Adaptive Vector Quantization

Particle filters (PFs) are used for the discrete approximation of dynamic and non-Gaussian probability distributions using numerous particles. Maximum a posteriori (MAP) estimation, which is a point estimation method to extract a unique state value from the probability distributions formed by a PF, functions appropriately against multimodal distributions. However, MAP entails an enormous calculation cost. Therefore, we propose a method to perform MAP estimation with a low calculation cost by compressing the information configured by PF, using adaptive vector quantization. For MAP estimation with 900 particles, the proposed method reduced the computational cost by approximately 96% compared to the conventional method and maintained the same estimation accuracy during simulation.
Congruences for Stochastic Automata

Congruences for stochastic automata are defined, the correspondin factor automata are constructed and investigated for automata ove analytic spaces. We study the behavior under finite and infinite streams. Congruences consist of multiple parts, it is shown that factoring can be done in multiple steps, guided by these parts.
Performance of Bounded-Rational Agents With the Ability to Self-Modify

Self-modification of agents embedded in complex environments is hard to avoid, whether it happens via direct means (e.g. own code modification) or indirectly (e.g. influencing the operator, exploiting bugs or the environment). It has been argued that intelligent agents have an incentive to avoid modifying their utility function so that their future instances work towards the same goals. Everitt et al. (2016) formally show that providing an option to self-modify is harmless for perfectly rational agents. We show that this result is no longer true for agents with bounded rationality. In such agents, self-modification may cause exponential deterioration in performance and gradual misalignment of a previously aligned agent. We investigate how the size of this effect depends on the type and magnitude of imperfections in the agent's rationality (1-4 below). We also discuss model assumptions and the wider problem and framing space. We examine four ways in which an agent can be bounded-rational: it either (1) doesn't always choose the optimal action, (2) is not perfectly aligned with human values, (3) has an inaccurate model of the environment, or (4) uses the wrong temporal discounting factor. We show that while in the cases (2)-(4) the misalignment caused by the agent's imperfection does not increase over time, with (1) the misalignment may grow exponentially.
Unimodal Cyclic Regularization for Training Multimodal Image Registration Networks

The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization. In the deep learning era, researchers proposed many approaches to automatically learn the similarity metric, which has been shown effective in improving registration performance. However, for the regularization term, most existing multimodal registration approaches still use a hand-crafted formula to impose artificial properties on the estimated deformation field. In this work, we propose a unimodal cyclic regularization training pipeline, which learns task-specific prior knowledge from simpler unimodal registration, to constrain the deformation field of multimodal registration. In the experiment of abdominal CT-MR registration, the proposed method yields better results over conventional regularization methods, especially for severely deformed local regions.
A Multi-Modal English-Italian Parallel Corpus for End-to-End Speech-to-Text Machine Translation

We discuss a set of methods for the creation of IESTAC: a English-Italian speech and text parallel corpus designed for the training of end-to-end speech-to-text machine translation models and publicly released as part of this work. We first mapped English LibriVox audiobooks and their corresponding English Gutenberg Project e-books to Italian e-books with a set of three complementary methods. Then we aligned the English and the Italian texts using both traditional Gale-Church based alignment methods and a recently proposed tool to perform bilingual sentences alignment computing the cosine similarity of multilingual sentence embeddings. Finally, we forced the alignment between the English audiobooks and the English side of our textual parallel corpus with a text-to-speech and dynamic time warping based forced alignment tool. For each step, we provide the reader with a critical discussion based on detailed evaluation and comparison of the results of the different methods.
Natural Language Processing (NLP) and Text Analytics

In the preceding chapters, we have solely relied on the structure of the HTML documents themselves to scrape information from them, and that is a powerful method to extract information.
LibGuides: Games Design: New Resources

A comprehensive guide to the resources provided by the Library to support you with your course. A selection of resources, recently purchased for your subject area.
Distributing Web Components

In this chapter, you will learn how to make our web components available in npm. You are also going to learn about the browser support offered by Web Components APIS, how to add polyfills to support more web browsers, and how to add Webpack and Babel to process and prepare our web components for publication.
Peramalan Harga Minyak Mentah Menggunakan Model Autoregressive Integrated Moving Average Neural Network (ARIMA-NN)

Crude oil prices can affect the production and consumption of a country. Crude oil prices are set every month and semester by Indonesian Crude Oil Price(ICP). Bontang Return Condensate (BRC) is one of crude oil type in Indonesia. Forecasting is an expectation of a request or thing that will come based on several forecasting variables.In this study, data was used in July 2010-December 2017. The purpose of this study was to determine the best model of Indonesian crude oil price data for the BRC type and the forecasting results. The model used in this study is the ARIMA-NN model which is a combination of ARIMA model and Neural Network (NN) model. The best ARIMA-NN model has ARIMA (2,1,0) and NN components with 2 inputs and 2 neurons in the hidden layer. The NN model is a Feed Forward Neural Network (FFNN) model with backpropagation algorithm. The results of the forecasting of the BRC Indonesia crude oil price for January-December 2018 are around the value of 60 USD / Barrel.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling and ineffective reward functions. In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning and occlusion reasoning. Specifically, we use unknown object segmentation to ignore distractors in the scene for better reward computation and goal generation; we further enable occlusion reasoning by employing a novel auxiliary loss and training scheme. We demonstrate that our proposed algorithm, ROLL (Reinforcement learning with Object Level Learning), learns dramatically faster and achieves better final performance compared with previous methods in several simulated visual control tasks. Project video and code are available at https://sites.google.com/andrew.cmu.edu/roll.
AMBR: Boosting the Performance of Personalized Recommendation via Learning from Multi-behavior Data

The performance of personalized recommendation can be further improved by exploiting multiple user behaviors (e.g., browsing, adding-to-cart, product purchasing) to predict items of user interests. However, the challenge lies in how to accurately model the relations among multiple user behaviors. The commonly adopted cascade relation over-simplifies the problem and cannot model the real user behavior patterns. In this paper, we propose a novel multi-behavior recommendation algorithm called AMBR (Attentive Multi-Behavior Recommendation), which can well capture the complicated relations among multiple behaviors. AMBR integrates the representation learning module and the matching function learning module into one framework. By utilizing the modern neural network techniques, AMBR is more flexible in modeling the relations of multiple behaviors without presuming a fixed cascade relation. Finally, we also conduct a set of experiments based on two real-world datasets, and the results show that our AMBR algorithm significantly outperforms other state-of-the-art algorithms by over 8.6%, 9.3% in terms of HR and NDCG.
Publisher Correction: Mnemonic prediction errors bias hippocampal states

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Pengembangan aplikasi raspberry pi sebagai mesin survei kepuasan pelanggan

This customer satisfaction survey machine was created to facilitate the survey process that will be used in the framework of the feedback process. This feedback is used as an evaluation material to continuously improve servants. This machine is made of Raspberry Pi as a mini personal computer (PC), which is equipped with a push button switch button as a survey input, as well as a 32 inch TV screen as a display. The programming language used is python, with Graphic User Interface (GUI) using Tkinter programming. From the survey engine, it can be seen a report on the number of respondents taking the survey. In addition, this tool can record survey data and present it in graphical form as a report.
Search-Based Online Trajectory Planning for Car-like Robots in Highly Dynamic Environments

This paper presents a search-based partial motion planner to generate dynamically feasible trajectories for car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives, which are generated by discretizing the time dimension and the control space. To enable fast online planning, we first propose an efficient path searching algorithm based on the aggregation and pruning of motion primitives. We then propose a fast collision checking algorithm that takes into account the motions of moving obstacles. The algorithm linearizes relative motions between the robot and obstacles and then checks collisions by comparing a point-line distance. Benefiting from the fast searching and collision checking algorithms, the planner can effectively and safely explore the state-time space to generate near-time-optimal solutions. The results through extensive experiments show that the proposed method can generate feasible trajectories within milliseconds while maintaining a higher success rate than up-to-date methods, which significantly demonstrates its advantages.
Memory Prediction on Real-Time User Behavior Traffic Detection

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Human brain is a learning system. Human have to learn by getting exposed to something. This capability of learning system to recognize new patterns is called generalization. The abilities of human brain to perform generalization are yet to be matched by neural network or even by any of artificial intelligence algorithm in general. Thus, the need for new machine intelligence approach is imperative. Neural network is designed to take advantages of the speed of computers to solve engineering and computational complex problems intelligently. On the other hand, human brain is somewhat not computationally powerful. Human brain is not even able to calculate quadratic problems within milliseconds. Instead, it uses its vast amounts of memory to store everything human know and have learned. According to a modern neuroscience theory named memory-prediction framework, introduced by Hawkins and Blakeslee in 2005, human brain uses this memory-based model to make continuous predictions of future events. Therefore, a hybrid approach that possesses the ability to compute like neural network and at the same time think like human brain will shed some light in the advancement of machine learning research as well as the development of a truly intelligent machine. This talk discusses the memory-prediction framework and proposes simplified single cell assembled sequential hierarchical memory (s-SCASHM) model instead of hierarchical temporal memory (HTM) in order to speed up the learning convergence. s-SCASHM consists of single neuronal cell (SNC) model and simplified sequential hierarchical superset (SHS) platform. The SHS platform is designed by simplifying to have a region with four rows columnar architecture instead of having six rows per region as in human neocortex. Then, the s-SCASHM is implemented as the prediction engine of user behavior analysis tool to detect insider attacks/anomalies. As nearly half of incidents in enterprise security triggered by the Insider, it is important to deploy more intelligent defense system to assist the enterprise be able to pinpoint and resolve any incidents caused by the Insider or malicious software (malware). The attacks evolve; however, current detection systems that use the deep learning techniques cannot perform online (on-the-fly) learning. Thus, an intelligent detection system with on-the-fly learning capability is required. Experimental results show that the proposed memory model is able to predict user behavior traffic with significant level of accuracy and performs on-the-fly learning.
Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching the stickers image with previous utterances. However, existing methods usually focus on measuring the matching degree between the dialog context and sticker image, which ignores the user preference of using stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user. Two main challenges are confronted in this task. One is to model the sticker preference of user based on the previous sticker selection history. Another challenge is to jointly fuse the user preference and the matching between dialog context and candidate sticker into final prediction making. To tackle these challenges, we propose a \emph{Preference Enhanced Sticker Response Selector} (PESRS) model. Specifically, PESRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker and each utterance. Then, we model the user preference by using the recently selected stickers as input, and use a key-value memory network to store the preference representation. PESRS then learns the short-term and long-term dependency between all interaction results by a fusion network, and dynamically fuse the user preference representation into the final sticker selection prediction. Extensive experiments conducted on a large-scale real-world dialog dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the effectiveness of each component of PESRS.
Rancang Bangun Aplikasi Uji Kompetensi Bidan Pada Politeknik Baubau Berbasis Android

&lt;p&gt;&lt;em&gt;Midwife competency test as one of the requirements in the framework of quality assurance and standardization of competence of midwifery diploma III graduates, one of the educational institutions in the City of Baubau namely Baubau Polytechnic, has carried out midwife competency tests. In the process of implementing the midwife competency test at Baubau Polytechnic, it is still manual. The use of this manual method has experienced many obstacles in its implementation, including in terms of the cost of providing question material, answering the test, and the time for taking the exam. The purpose of this study was to design a midwife competency examination software and to make it easier for exam participants and the organizing committee to carry out the midwife competency test. Data collection methods used were observation and interviews. The results of this study are in the form of an android application to provide convenience in inputting question data, inputting participant data, and making test results reports quickly, efficiently, and accurately.&lt;/em&gt; &lt;em&gt;Can help test-takers in conducting exams using the android application. The exam supervisor can also see the active sessions of the examinees while conducting the exam, can see the remaining time, and see the final score of each participant who has completed the exam.&lt;/em&gt;&lt;/p&gt;
Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

&lt;p&gt;In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.&lt;/p&gt;
Pengembangan Bahan Ajar Buku Teks IPS Tema Indahnya Kebersamaan Pada Peserta Didik Kelas 4 MI An Nuriyah Kwanyar Bangkalan

This research aims to develop Social Science Textbook teaching materials. The material teaching is developed in the form of social studies textbooks with the theme "Beautiful Togetherness" and appropriate based on student’s expertise and readability as seen from the material or content, linguistic, presentation, and graphic aspects. This research is a research and development study with the design of analysis, planning, and developing textbook development. The feasibility of the expert test was carried out by material experts and media experts, while the students' openness test was carried out by the 4th grade students of MI An-Nuriyah from group A with a total of 36 students and 4th grade students of MI An-Nuriyah from group B with a total of 32 students. The ttechnique of Collecting data is done using a questionnaire. The method used to analyze data is quantitative descriptive analysis techniques by the distribution of five scales and the rating scale predetermined. Textbooks are well considered by material experts and media experts that has a minimum average score "enough" category. The text book feasibility test results the assesment Experts in the "good" category. Evaluation of textbooks by media experts are "enough" and "good" categories. The results of the textbook feasibility test by textbook students are categorized as "good."
AN EMPIRICALLY TESTED DESIGN PATTERN SELECTION FRAMEWORK BASED ON DEVELOPER EXPERIENCE

In most IT projects, software maintenance had always been a difficult task to perform especially when the software is not designed properly. This has led to numerous changes all over the places despite a minor change request. Design patterns are widely known for its proven solution to recurring problems. If fully utilized, design patterns can prevent such incident from happening as changes can often be added without affecting existing components. However, due to numerous design patterns that are available these days, it is difficult to select the right pattern. In addition, existing studies still lack a proven guideline to select the right pattern. This difficulty is faced by many beginners and experienced developers. The aim of this research is to propose a framework to select the most suitable design patterns in software development. The research methodology used is quantitative research where primary data are collected using survey and questionnaires. This is mainly to find out developers’ attitudes towards the use of design patterns. 25 out of 48 papers regarding design patterns are referenced in this paper. The framework developed is evaluated and empirically tested using expert judgement and controlled experiment. The result shows that the framework does help in evaluating and selecting the most suitable pattern. Therefore, the authors highly recommend developers to make use of this framework in software development.