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arXiv 2022年

作者： Grisci, Bruno Iochins Kuhn, Gabriela Colombelli, Felipe Matter, Vítor Kehl Lima, Leomar Heinen, Karine Pegoraro, Mauricio Borges, Marcio Rigo, Sandro José Barbosa, Jorge Luis Victória da Rosa Righi, Rodrigo da Costa, Cristiano André de Oliveira Ramos, Gabriel Software Innovation Laboratory - Softwarelab Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos Av. Unisinos 950 RS São Leopoldo93022-750 Brazil Institute of Informatics Federal University of Rio Grande do Sul Av. Bento Gonçalves 9500 RS Porto Alegre91501-970 Brazil Dell Technologies Brazil Av. Industrial Belgraf 400 Eldorado do Sul RS 92990-000 Brazil

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Predicting User Preferences with XGBoost Learning to Rank Method

Predicting User Preferences with XGBoost Learning to Rank Me...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Nunung Nurul Qomariyah Dimitar Kazakov Ahmad Nurul Fajar Faculty of Computing and Media Bina Nusantara University Jakarta Indonesia University of York York United Kingdom BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728184067

ISBN: (纸本)9781728184074

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation.

关键词： Predictive models Measurement Motion pictures Task analysis Linear programming Boosting Web search

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User Preference Modelling from Movie Database

User Preference Modelling from Movie Database

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International Conference on ICT For Smart Society (ICISS)

ISBN: (数字)9781665404228

ISBN: (纸本)9781665404235

Film industry becomes one of the largest amongst the other economic sectors worldwide nowadays. This industry also has a significant impact in the global economic market. Many movies have been produced each year. However, some movies still fail to reach break-even points every year. Players in this industry need to think carefully when spending most of their money to make a great and successful movie in the market. Several attempts also need to be made to estimate the success performance before a movie can be released. On the audience side, there is an indicator that we can use to decide that a movie is good by looking to its ratings. The industry can use this rating systems as a factor to be considered when evaluating the past movie performances to make better movie for the next production. This study aims to predict the movie ratings and find the most important features which correlate to the ratings. We performed an experiment on IMDb dataset which contains 5,043 movie metadata on four models of machine learning, Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN). From the experiment, Random Forest achieves the best performance amongst the four models.

关键词： Motion pictures Industries Prediction algorithms Machine learning algorithms Predictive models Social networking (online) Random forests

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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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Open government proposal for transparency and information access in Costa Rica 18

Open government proposal for transparency and information ac...

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18th International Conference on WWW/Internet 2019

作者： Rodriguez-Arias, Franchesca Cortes-Morales, Roberto Master Program on Management for Information Technologies Instituto Tecnologico de Costa Rica San Jose Costa Rica Graduate Department of Computing School Instituto Tecnologico de Costa Rica Cartago Costa Rica

ISBN: (纸本)9789898533944

This research deals with a public issue that affects many countries around the world, which is transparency. Therefore, an Open Government solution is proposed to face this problem. Through the multidimensional analysis, the current situation in Costa Rica can be assessed while best practices in Taiwan are identified to propose a roadmap that can be adopted in Costa Rica. Taiwan is one of the countries with the best open data index. Using comparative study case analysis, the research proposes how to implement Open Government in Costa Rica improving transparency and access to information offered by the State to general public. © 2019.

关键词： Transparency

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Effects of spatial uniformity of continuous and pulse laser irradiation on the microstructure and mechanical properties of Ti-6Al-4V in powder bed fusion

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Materials Today Communications 2025年 46卷

作者： Seunghwan Moon Seungmuk Ji Yoonseok Ko Min-Su Lee Tea-Sung Jun Dong-Ik Kim Jong-Souk Yeo School of Integrated Technology College of Computing Yonsei University Seoul 03722 Republic of Korea BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul 03722 Republic of Korea Center for Energy Materials Research Korea Institute of Science and Technology Seoul 02792 Republic of Korea Department of Mechanical Engineering Incheon National University Incheon 22012 Republic of Korea

Powder bed fusion of metals with laser beam (PBF-LB/M) using Ti-6Al-4V (Ti64) has enabled delicate fabrication of biocompatible medical implants. This research focuses on how the spatial uniformity of laser irradiation affects material under continuous wave (CW) and pulse width modulated (PWM) laser conditions, using a self-developed PBF-LB/M system. The volumetric energy density (VED) was used as a reference parameter in order to accurately compare the effects of CW and PWM lasers, but limitations of VED, which does not include spatial and kinetic factors, which are reinforced in the PWM laser process, were also considered. Results revealed that the uniform distribution of energy irradiation across the entire region brought similar porosities and Vickers hardness tendencies for CW and PWM laser processes. In contrast, the spatially nonuniform energy transfer resulted in a significantly higher porosity in the specimen fabricated using the PWM laser than that produced using the CW laser at the same VED, reducing compressive yield strength. The electron backscattered diffraction analysis showed no significant differences in the average grain size, phase distribution, and misorientation angle of the samples produced using CW and PWM lasers under the same VED. However, sharper grains with an aspect ratio of less than 0.3 were 7 % higher in specimens produced using the PWM laser. This study highlights the possibility of controlling the microstructure and mechanical properties in a wider range when using the PWM laser with spatially nonuniform irradiation.

关键词： Vickers hardness

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Performance Optimization and Scalability Analysis of the MGB Hydrological Model

Performance Optimization and Scalability Analysis of the MGB...

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International Conference on High Performance computing

作者： Henrique R. A. Freitas Celso L. Mendes Aleksandar Ilic Graduate Program in Applied Computing National Institute for Space Research São José dos Campos Brazil Associate Laboratory of Computing National Institute for Space Research São José dos Campos Brazil INESC-ID Instituto Superior Técnico University of Lisbon Lisbon Portugal

Hydrological models are extensively used in applications such as water resources, climate change, land use, and forecast systems. The focus of this paper is performance optimization of the MGB hydrological model, which is widely employed to simulate water flows in large-scale watersheds. The optimization strategies that we selected include AVX-512 vectorization, thread-parallelism on multi-core CPUs (OpenMP), and data-parallelism on many-core GPUs (CUDA). We conducted experiments for real-world input datasets on state-of-the-art HPC systems based on Intel's Skylake CPUs and NVIDIA GPUs. In addition, a Roofline model characterization for these datasets confirmed performance improvements of up to 37.5x on the most time-consuming part of the code and 8.6x on the full MGB model. The work proposed herein shows that careful optimizations are needed for hydrological models to achieve a significant fraction of the performance potential in modern processors.

关键词： Analytical models Computational modeling Scalability High performance computing Conferences Graphics processing units Predictive models

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A Quantitative Analysis on Different Carbonate Indicators Based on Spaceborne Data in a Controlled Karst Area

A Quantitative Analysis on Different Carbonate Indicators Ba...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M. Muller V. F. Sales D. C. Zanotta A. Marques T. T. Guimarães L. Bachi E. M. Souza D. Brum L. Gonzaga M. R. Veronez C. L. Cazarin Vizlab X-Reality & GeoInformatics Lab - Graduate Program in Applied Computing UNISINOS University Rio Grande do Sul Brazil State University of Maringa Parana Brazil CENPES - PETROBRAS Rio de Janeiro Brazil

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

New sensors aboard recently launched satellites have induced the development of several measures aimed to indicate the presence of many materials over the Earth. Karsts are places rich in carbonate rocks and present large economic and environmental importance. This paper aimed at assessing the performance and consistency of different carbonate estimators derived from orbital images acquired over a controlled karst area. Experiments were assisted by a multi-scaled reference data built through a high spatial resolution Unmanned Aerial Vehicle (UAV) image acquired over the selected area. Results show a considerable unconformity among selected measures and better performance presented by indices exploiting measures along visible and infrared spectral regions.

关键词： Rocks Remote sensing Earth Indexes Artificial satellites Thermal sensors Sensor phenomena and characterization

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Janus - A Software-Defined Networking MPEG-DASH Video Streaming Load Balancer

Janus - A Software-Defined Networking MPEG-DASH Video Stream...

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International Conference on Network and Service Management

作者： Edenilson Jônatas dos Passos Adriano Fiorese Graduate Program in Apllied Computing (PPGCA) Santa Catarina State University (UDESC) Joinville Brazil

ISBN: (数字)9783903176249

ISBN: (纸本)9781728153964

Recently, with popularisation of video streaming service, new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. Even so, with the limitation of the TCP/IP network structure, the end user Quality of Experience (QoE) may be affected. One issue that can affect user QoE is the workload of content distribution servers. Thus, the unbalancing of server's workload comprising user's attendance can lead to a content server provider non optimised choice. This work presents two load-balancing solutions between MPEG-DASH video servers based on Software-Defined Networks, using as a balancing workload metric the throughput of the content server as well as the CPU load.

关键词： Electrostatic discharges Hafnium Iron

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