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MLEA： A Solution for Users of Android in UTPVirtual

Computer Technology and Application 2011年第5期2卷 406-412页

作者： Gisela Torres de Clunie Sergio Crespo Coelho da Silva Pinto Aris Linet Castillo Lucas Monteiro Braz Tasia Serrao Norman Rangel Jeanette Riley Olinda de Barraza Kexy Rodriguez Computer Systems Engineering Department Technological University of Panama Panama 0819 Panama Computer Science Research and Graduate Program in Applied Computing University of the Sinos Valley （UNISINOS） RS 93022-000 Brazil CIDITIC Technological University of Panama Panama 0819 Panama

This paper describes a design of an educational platform for a mobile learning architecture, which is a state of the an topic in distance education. The product will allow users to interact in an efficient, flexible, and transparent fashion with a web-based education environment, in this case Module Object-Oriented Dynamic Learning Environment （Moodle）, using Android mobile devices. In order to provide a strong and lasting architecture, the Service Oriented Architecture （SOA） methodology is used given that it allows easy software re-utilization as well as integration of heterogeneous services. The architecture is based on web services implemented with Representational State Transfer （REST）, as it has been demonstrated to be lighter and less consuming than other protocols, for devices with limited resources such as mobile devices. Web services provide the communication means between the server side and the client side of the architecture, whereas agents are used to deliver the services itself. The authors propose the development of an environment that facilitates the integration of various educational resources to support m-learning. An important aspect of the proposal is the offering of a tool to provide customized alerts for students and teachers, enabling them to remain updated about activities taking place in the courses.

关键词： Distance learning virtual education m-learning service oriented architecture （SOA） mobile learning mobile computing interoperability representational state transfer （REST）.

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Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

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IFAC-PapersOnLine 2023年第2期56卷 9056-9061页

作者： Vittorio Giammarino James Queeney Lucas C. Carstensen Michael E. Hasselmo Ioannis Ch Paschalidis Division of Systems Engineering Boston University Boston MA 02215 USA Center for Systems Neuroscience Department of Psychological and Brain Sciences and Graduate Program for Neuroscience Boston University Boston MA 02215 USA College of Engineering and Hariri Institute for Computing & Computational Science & Engineering Boston University Boston MA 02215 USA

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.

关键词： Learning for Control Reinforcement Learning Deep Learning RL with demonstrations Imitation Learning Bio-inspired Control

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Women and Technology: Reversing the Trends of Attrition and Obtaining a Balance

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Women's Studies 2008年第3期37卷 173-175页

作者： Leroy, Gondy gondy.leroy@cgu.edu Tolle, Kristin M. Perkins, Linda Assistant Professor School of Information Systems and Technology Claremont Graduate University Claremont California USA Program Manager for Biomedical Computing External Research @ Microsoft Research Redmond Washington USA University Associate Professor and Director of Applied Women''s Studies Claremont Graduate University Claremont California USA

The article discusses various reports published within the issue including one by Louise Soe and Elaine K. Yakura on women's attrition from technical fields of work, and another by Indira Guzman and Jeffrey M. Sta... 详细信息

关键词： WOMEN in technology EQUALITY in the workplace

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Determining environmental indicators related to the propagation of contagious diseases and health issues: A Systematic Literature Review

Determining environmental indicators related to the propagat...

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Iberian Conference on Information systems and Technologies (CISTI)

作者： Janeth Domínguez Portillo Laura M. Rodríguez Peralta Paulo N. M. Sampaio Eldman de Oliveira Nunes Faculty of Information Technologies and Data Science Universidad Popular Autónoma del Estado de Puebla (UPAEP) México Graduate Program in Computing and Systems University of Salvador (UNIFACS) Salvador Bahia Brazil

The environmental indicators provide significant information in order to describe the state of a phenomenon, environment or area [1]. This paper addresses the indicators present in the air and their relationship in the accommodation and transmission of viruses, as well as how these environmental indicators can lead to health problems. Therefore, the main goal of this work is to review the scientific contributions in the literature in order to determine and select the potential environmental indicators present in indoor environments which have an impact on the health of people who work and live within these spaces. For this purpose, we carried out a Systematic Literature Review (SLR) related to environmental monitoring, COVID-19, Internet of things, air pollution and environmental indicators within indoor spaces. This SLR allowed us to identify indicators that according to their level of presence can represent a high risk for people’s health. Therefore, the process of planning, conducting and reporting the Systematic Literature Review enabled us not only to identify some environmental indicators, but also their corresponding monitoring advantages and disadvantages, and multiple perspectives and approaches of each contribution in the literature. Further on, this study will be the basis for the proposal of an automatic and intelligent platform based on Internet of Things for monitoring indoor environments in order to detect high risk levels for the contamination of contagious diseases.

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Dynamic timeout power policy for network interfaces

Dynamic timeout power policy for network interfaces

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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Bruna M. J. C. Vilar Fabiano De M Silva Saulo O. D. Luiz J. J. Silva Hyggo Almeida Angelo Perkusich Post-Graduate Program in Electrical Engineering - PPgEE COPELE Embedded Systems and Pervasive Computing Laboratory Universidade Federal de Campina Grande Campina Grande PB BR Center of Electrical Engineering and Informatics Federal University of Campina Grande Campina Grande Brazil Embedded Systems and Pervasive Computing Laboratory Federal University of Campina Grande

Network interfaces are present in a variety of battery powered computer systems, such as notebooks, ultrabooks, tablets and mobile phones. Considering that the battery life is still a constraint, there are opportunities for power management by reducing the power consumption of idle network interfaces. In this work, a dynamic timeout power policy for network interfaces is presented, incorporating workload estimation and a problem optimization at runtime, thus providing power savings and suitable performance.

关键词： Heuristic algorithms Network interfaces Power demand Optimization Mobile handsets Internet Stochastic processes

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Machine Learning for Prediction of Adverse Drug Reactions: Application to Neonatal Intensive Care Units

SSRN

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SSRN 2023年

作者： do Nascimento, Amanda Roseane Farias Greati, Victor Rodrigues Leopoldino, Ramon Weyler Duarte Martins, Rand Randall Barbosa, Euzebio Guimarães Graduate Program in Pharmaceutical Sciences Health Sciences Center Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Graduate Program in Systems and Computing Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Introduction: The intensive care of neonates is associated with the entry of a large volume of data in their medical records. The treatment of this data can be done through Machine Learning: a tool capable of assisting in the detection and decision-making of a wide range of medical conditions, including adverse drug reactions (ADR). Objective: To train a prediction model to assist in the detection of adverse drug reactions in neonates admitted to an ICU. Methodology: observational study developed in the Neonatal Intensive Care Unit of a teaching hospital in Brazil. Clinical data were collected from daily pharmacotherapeutic follow-up, processed and analyzed by machine learning through libraries written in Python language. Results: Eight hundred and three neonates were included in the study, with a mean gestational age of 32.2 ± 4.2 weeks and mean birth weight of 1807.2 ± 936.6g. The incidence of ADR was 10.8%. Antimicrobials, especially aminoglycosides, were the most prescribed drugs for this population. An algorithm was trained and tested to predict ADR in the NICU, in which the metrics were precision (0.35) and recall (0.823), with specificity (80%) and sensitivity (67%). Conclusion: There is a high potential in the machine learning method to predict ADR in neonates admitted to an ICU. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Mobile Apps for Healthy Life Education for Young People 2

Mobile Apps for Healthy Life Education for Young People

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Suwandi, Benedictus Evan Warnars, Diana Teresia Spits Warnars, Harco Leslie Hendric Spits Utomo, Wiranto Herry Ramadhan, Arief Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Computing President University Department of Magister Science of Information Technology Bekasi17550 Indonesia

ISBN: (纸本)9781665400527

Health education is crucial for the community, especially the younger generation, because adolescent behavior/lifestyle will carry over into adulthood, and it is difficult to change it. This paper proposes designing and implementing a mobile application for young people for healthy lifestyle education. This mobile application implemented with the android studio is designed using use case diagrams to describe the business processes of this mobile application. In addition, activity diagrams describe the process details of each activity use case, and class diagrams describe the design of the database model, which is implemented using a MySQL database. © 2022 IEEE.

关键词： Mobile computing

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DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection

DeepGuard: Deep Generative User-behavior Analytics for Ranso...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Gaddisa Olani Ganfure Chun-Feng Wu Yuan-Hao Chang Wei-Kuan Shih Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan National Tsing Hua University Hsinchu City Taiwan

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

关键词： Training Training data Organizations Tools Ransomware Security Informatics

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Influence Value: Quantifying Topic Influence on Social Media

Influence Value: Quantifying Topic Influence on Social Media

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Fernando H. Calderon Yi-Shin Chen Taiwan International Graduate Program Social Networks and Human-Centered Computing Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781509021802

Social Media specifically Micro-blogging sites have become very rich data repositories. However the generated data is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life experiences, discussions or events have a direct impact on the behaviors reflected in social networks. It is therefore of great importance to asses the impact these interactions are having over a social group. An alternative to answer this is determining how influential a topic is according to the behavior presented on a social network over time. It is then necessary to find and develop methods that can contribute towards this task. Having identified a topic in social media we can first classify it as time specific or long term, then posts relevant to the topic are collected and each one is assigned an emotion label. We then propose an Influence Value score which will be given to each topic based on its lifespan, emotion transition and reach. This lays ground to quantify how influential a topic is over a social group, specifically from events detected on twitter.

关键词： Media Social groups Twitter Wheels Measurement Big data

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach

Minimizing the Number of Stunting Prevalence Using the Eucli...

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International Conference of Computer Science and Information Technology

作者： Muhammad Zarlis Tanty Oktavia Relita Buaton Ferda Ernawan Kevin Andrian Department BINUS Graduate Program - Master of Information System Management Information System Management Bina Nusantara University Jakarta Indonesia Informatics Engineering STMIK Kaputama Binjai Indonesia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city.

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