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30th International Conference on software engineering and Knowledge engineering, SEKE 2018

作者： Farias, Kleinner Gonçales, Lucian Bischoff, Vinicius Da Silval, Bruno Guimarães, Everton Nogle, Jacob Univ. of Vale Do Rio Dos Sinos São Leopoldo Brazil Dept. of Computer Science and Software Engineering California Polytechnic State University San Luis Obispo United States Department of Computer Science Drexel University Philadelphia United States

ISBN: (纸本)1891706446

Context: The Unified Modeling Language (UML) has become the standard for modeling software. Several surveys on the UML usage have been proposed in recent years. However, none of them explores the UML use in specific regional scope, and thus little is known about the practices and perceptions of UML use from the perspective of practitioners in the Brazilian industry. Objective: This paper reports on a survey focused on identifying the state-of-The-practice of the Brazilian industry for what concerns the UML usage in real-world settings. Method: In total, 222 practitioners from 140 different Information Technology companies have answered an on-line (or printed) questionnaire concerning their UML use experiences, the difficulty in adopting UML and what should be done to increase the UML adoption in practice. Result: The results show that: (1) 60 participants (28.2%) have used UML in their daily work, while 73.2% have not;(2) 55.41% of the surveyed participants did not disagree with the statement that UML is the "lingua franca" in software modeling;(3) 61.26% reported to find that the automatic creation of UML diagrams to represent a big picture of the system under development would be useful to boost UML use. Conclusion: The UML is not often used in the work life of participants. In addition, no relationship was identified between the use of UML and the participant company being a software factory. © 2018 Universitat zu Koln. All rights reserved.

关键词： Unified Modeling Language

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Search for Coordinates of WSN Nodes using Multidimensional Scaling Method

Search for Coordinates of WSN Nodes using Multidimensional S...

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International Conference on Ultra Modern Telecommunications and Workshops (ICUMT)

作者： Ilya Grishin Van Dai Pham Darina Okuneva Ruslan Kirichek Dept. of Communication Networks and Data Transmission Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia Dept. of Software Engineering and Computer Science Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia Bonch-Bruevich St. Petersburg State University of Telecommunications Saint Petersburg Russia V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781728192819

ISBN: (纸本)9781728192826

Nowadays, wireless sensor networks (WSN) are widely used for various Internet of Things applications. The sensor nodes are usually deployed randomly in the field. Thus, searching for node coordinates is one of the requirements to construct the network structure. With knowing distances between nodes and the coordinates of anchor nodes in a certain network fragment, we propose a method based on the multidimensional scaling for searching for desired node coordinates.

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Ensuring Proof-of-Authenticity of IoT Edge Devices Using Blockchain Technology

Ensuring Proof-of-Authenticity of IoT Edge Devices Using Blo...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Ujjwal Guin Pinchen Cui Anthony Skjellum Dept. of Electrical and Computer Engineering Auburn University Dept. of Computer Science and Software Engineering Auburn University SimtCenter & Dept. of Computer Science and Engineering University of Tennessee at Chattanooga

Imposter devices pose serious threats. The majority of low-cost edge devices can easily be counterfeited or cloned; the supply chain is insufficiently secure. Reliability of deployed devices can be called into question simply because they might be counterfeit or cloned. It is a must to identify edge devices' sourcing uniquely and verify their validity periodically at runtime. We integrate blockchain technology to authenticate resource-constrained, low-cost edge devices. We use SRAM-based physically unclonable functions (PUFs)to generate unique “digital fingerprints” (device IDs). Registered manufacturers upload a cryptographic hash of each device ID to a “globally accessible” blockchain instance (key-value store or smart contract). While registering/designating a device locally, the end-user verifies whether the hash is present in that blockchain. We utilize a “locally permissioned” blockchain infrastructure (which is still a globally managed blockchain or, in future, a sidechain)to authenticate edge devices for a defense-in-depth approach. Devices can authenticated periodically to prevent device cloning. Target environments can be large and have varied trust among users and lack a specific perimeter; this “local” blockchain methodology is thus pertinent, especially since blockchains gain security over time. Our approach reduces the potential for classes of information leakage and types of sabotage in a critical infrastructure or large-scale deployment (such as a smart city)arising from imposter devices. This methodology protects against such imposters in mobile settings within an IoT infrastructure too.

关键词： Blockchain Cryptography Internet of Things Smart contracts Hardware Random access memory

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Optimizing prediction intervals by tuning random forest via meta-validation

arXiv

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arXiv 2018年

作者： Bayley, Sean Falessi, Davide Dept. of Computer Science and Engineering Notre Dame United States Dept. of Computer Science and Software Engineering California Polytechnic State University

Recent studies have shown that tuning prediction models increases prediction accuracy and that Random Forest can be used to construct prediction intervals. However, to our best knowledge, no study has investigated the need to, and the manner in which one can, tune Random Forest for optimizing prediction intervals - this paper aims to fill this gap. We explore a tuning approach that combines an effectively exhaustive search with a validation technique on a single Random Forest parameter. This paper investigates which, out of eight validation techniques, are beneficial for tuning, i.e., which automatically choose a Random Forest configuration constructing prediction intervals that are reliable and with a smaller width than the default configuration. Additionally, we present and validate three meta-validation techniques to determine which are beneficial, i.e., those which automatically chose a beneficial validation technique. This study uses data from our industrial partner (Keymind Inc.) and the Tukutuku Research Project, related to post-release defect prediction and Web application effort estimation, respectively. Results from our study indicate that: i) the default configuration is frequently unreliable, ii) most of the validation techniques, including previously successfully adopted ones such as 50/50 holdout and bootstrap, are counterproductive in most of the cases, and iii) the 75/25 holdout meta-validation technique is always beneficial;i.e., it avoids the likely counterproductive effects of validation techniques. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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LARNN: Linear Attention Recurrent Neural Network

arXiv

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arXiv 2018年

作者： Chevalier, Guillaume Dept. of Computer Science and Software Engineering Laval University Quebec Canada

The Linear Attention Recurrent Neural Network (LARNN) is a recurrent attention module derived from the LSTM cell and ideas from the consciousness Recurrent Neural Network (RNN). Yes, it LARNNs. The LARNN uses attention on its past cell state values for a limited window size k. The formulas are also derived from the Batch Normalized Long Short-Term Memory (BN-LSTM) cell and the Transformer Network for its Multi-Head Attention Mechanism. The Multi-Head Attention Mechanism is used inside the cell such that it can query its own k past values with attention with restricted windowing on the k most recent previous cell state. This has the effect of augmenting the rank of the tensor with the attention mechanism, such that the cell can perform complex queries to question its previous inner memories, which should augment the long short-term effect of the memory. With a clever trick, the LARNN cell with attention can be easily used inside a loop on the cell state, just like how any other Recurrent Neural Network (RNN) cell can be looped linearly through time series. This is due to the fact that its state, which is looped upon throughout time steps within time series, stores the inner states in a "first in, first out" queue which contains the k most recent states and on which it is easily possible to add static positional encoding when the queue is represented as a tensor. This neural architecture yields better results than the plain, vanilla LSTM cells. It can obtain results of 91.924% for the test accuracy, compared to the previously attained 91.653% using vanilla LSTM cells. Note that this is not to compare to other research, where up to 93.349% is obtained, but by costly using 18 LSTM cells rather than with 2 to 3 cells as analyzed here and in comparison. Finally, an interesting discovery is made, such that adding activation within the multi-head attention mechanism’s linear layers can yield better results in the context researched hereto. Copyright © 2018, The Authors. All

关键词： Cells

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A Prediction of Grain Yield Based on Hybrid Intelligent Algorithm

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Journal of Physics: Conference Series 2020年第1期1591卷

作者： Ibrahim Ahmed Saleh Wasan Abdallah Alawsi Omar Ibrahim Alsaif Khalil Alsaif University Of Mosul Software Engineering Dept. Iraq University Of Al- Qadisiyah college Science Iraq Northern Technical University Mosul Technical Institute Iraq University of Mosul Computer Science Dept. Iraq

The prediction is most important goals in economic quantitative studies, it basis in design and plan future economic policies properly process over forecasting accuracy. This paper is aiming at the problem salp swarm algorithm (SSA) for predicting grain yield is prone to fall into the local optimal problem. An improved SSA is proposed with combine with back propagation neural network. Using the different advantages of SSA algorithm in global search capabilities, combining the two for further optimize the weight, improve the accuracy and robustness of the grain yield prediction model. The specific implementation is selected from 1963 to 2013. These methods are used to define agricultural datasets that supports crop growth decision for grain product and its influencing factors were tested as a data set. The results show that, the improved salp swarm optimization can be classified as a good predict tool for the domestic food production trend in recent years compared with the SSA. This paper briefly introduces three artificial methods BP neural networks, SSA and improved SSA optimization algorithm. The natural behavior of salp, barrel-shaped plankton that are mostly water by weight optimization and combined with mixed-group of intelligent algorithm are simulated. The simulation results of grain production prediction illustrate that the predict precision of the improved SSA is much higher than of both conventional BPNN and SSA techniques and it's very efficient and practicable.

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Collaborative Filtering Method with the use of Production Rules

Collaborative Filtering Method with the use of Production Ru...

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International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

作者： Amin Salih Mohammed Yelyzaveta Meleshko Saravana Balaji B Semenov Serhii Dept of Computer Engineering Lebanese French University Salahaddin University Erbil KR-Iraq Dept of Cyber Security & Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Dept of Information Technology Lebanese French University Erbil KR-Iraq Dept of Computer Science and Programming National Technical University (Kharkiv Polytechnic Institute) Ukraine

ISBN: (数字)9781728137780

ISBN: (纸本)9781728137797

this paper proposes a new collaborative filtering method with the calculation of unknown similarity coefficients between users via the application of production rules, which aims to improve the work quality of recommendation systems, to develop of production rules for the developed system. The methods used are: graph theory, the theory of algorithms, mathematical statistics, object-oriented programming, and fuzzy logic. The developed systems are new collaborative filtering method with the definition of unknown similarity coefficients between users through the application of production rules was developed, software for the implementation and testing of this method was developed, experiments on the developed software was conducted. The production rules to determine unknown similarity coefficients in recommendation systems was proposed. The new method of collaborative filtering with the application of production rules has been developed to find unknown similarity coefficients between users that may be possibly used to improve the work quality of the recommendation system. The conducted experiments showed that the developed method enhances the quality indicators of the recommendation system, such as item space coverage and the total number of predicted preferences of users.

关键词： Collaboration Production Information filters Computational intelligence software

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Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks

Normal and Acute Tympanic Membrane Diagnosis based on Gray L...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Erdal Başaran Abdülkadir Şengür Zafer Cömert Ümit Budak Yüksel Çelık Subha Velappan Dept. of Computer Engineering Karabük University Karabük Turkey Dept. of Electrical and Electronics Eng. Firat University Elazlğ Turkey Dept. of Software Engineering Samsun University Samsun Turkey Dept. of Electrical and Electronics Eng. Bitlis Eren University Bitlis Turkey Dept. of Computer Science and Eng. Manonmaniam Sundaranar University Tirunelveli India

Otitis Media (OM) is the general name of middle ear inflammation. In order to diagnose this disease, it is important to examine the middle ear tympanic membrane (TM) by a standard otoscopy device. In recent years, biomedical image processing and machine learning algorithms have become quite effective in diagnostic applications. To this aim, we propose a combination of gray-level co-occurrence matrix (GLCM) and artificial neural network (ANN) to distinguish acute tympanic membrane otoscope images from normal images. For the experiment, totally 223 middle ear otoscope images were collected from the volunteer patients admitted to Van Özel Akdamar Hospital. In the experimental study, the texture features are obtained separately from R, G, and B channels and then consolidated. In addition to the texture features provided by GLCM, the average values of each channels of the otoscope images are taken into account so as to determine whether the otoscope image belongs to acute or normal class. Lastly, this feature set is applied as the input to ANN. By experimental studies, we achieved 76.14% accuracy and with this model, we achieved promising results in diagnosing normal and acute OM disease. Consequently, the texture features were found as useful to classify normal and acute OM disease.

关键词： Artificial neural networks Feature extraction Biomembranes Ear Diseases Measurement

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Photo tagging tool for rapid and detailed post-earthquake structural damage identification 11

Photo tagging tool for rapid and detailed post-earthquake st...

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11th National Conference on Earthquake engineering 2018: Integrating science, engineering, and Policy, NCEE 2018

作者： Behrouzi, A. Pantoja, M. Dept. of Architectural Engineering California Polytechnic State University San Luis ObispoCA93407 United States Dept. of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA93407 United States

ISBN: (纸本)9781510873254

A significant task in earthquake reconnaissance is to conduct rapid and accurate assessments of damage to built infrastructure. This can be accomplished, in part, by analyzing the large volumes of high-resolution image data collected after a seismic event. However, detailed image tagging remains a task for trained human volunteers, which is both time-intensive and error prone. The authors developed a software tool to simplify and standardize the process of assigning damage and structure pairs to sub-regions of images. The goal of the tool is to facilitate the tagging of thousands of images from historic and recent earthquakes to train a deep learning (DL) algorithm to automatically identify damage observed in civil infrastructure. DL is a subset of machine learning that can be used for image classification problems. This process requires thousands of expertly tagged images for robust and automatic visual recognition capabilities. In detecting specific structural damage after an earthquake, images must have explicit tags for the building material, damage and location, as well as the impacted structural members. To obtain such a descriptive set of images, there is a need for a task-specific tool that facilitated tagging of the most common post-earthquake structural damage types. The resulting software solution consists of a simple user interface that displays the most frequently used damage and structural member tags as pre-loaded radio buttons and includes the flexibility for users to customize tags when necessary. The program generates marked-up images that show location-specific damage and structural member labels, as well as output files in the PASCAL Visual Object Classes (VOC) format that are compatible with TensorFlow and most DL frameworks, such that tagged images are ready to be used for training a DL algorithm. © 2018 11th National Conference on Earthquake engineering 2018, NCEE 2018: Integrating science, engineering, and Policy. All rights reserved.

关键词： Earthquakes

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Heptagonal Photonic Crystal Fiber Based Chemical Sensor in THz Regime

Heptagonal Photonic Crystal Fiber Based Chemical Sensor in T...

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International Conference on Informatics, Electronics & Vision (ICIEV)

作者： Md. Mahamudul Hasan Shuvo Sen Md. Juwel Rana Bikash Kumar Paul Md. Ahsan Habib Golam Moktader Daiyan Kawsar Ahmed Dept. of Physics Mawlana Bhashani Science and Technology University Tangail Bangladesh Dept. of ICT Mawlana Bhashani Science and Technology University Tangail Bangladesh Dept. of Software Engineering (SWE) Daffodil International University Dhaka Bangladesh School of Computer Science and Technology University of Electronic Science and Technology Sichuan Province China Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Tangail Bangladesh

In this article, here is developed a novel TOPAS based heptagonal photonic crystal fiber (H-PCF) for the identification of various chemical substances in the terahertz frequency territory. The full-vector finite element method (FV-FEM) is used for the numerical investigation about the competence of the proposed PCF. Simulation results show the maximum relative sensitivity as well as less connement loss for various chemicals. Numerically processed outcomes apparent that the proposed sensor shows 63.24 %, 61.05 % and 60.03 % detecting reactions for focused analytes benzene, ethanol and water individually at 1 THz frequency. Major purposes is to use H-PCF as the application of chemical sensor in commercial sector to detect harmful chemicals.

关键词： Sensitivity Ethanol Sensors Chemicals Terahertz radiation Optical losses Photonic crystals

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