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Research Square 2020年

作者： Bagheri, Hamid Dyer, Robert Severin, Andrew Rajan, Hridesh Department of Computer Science Iowa State University 226 Atanasoff Hall Ames50011 United States Dept. of Computer Science & Engineering University of Nebraska Lincoln Lincoln68588 United States Genome Informatics Facility Iowa State University 206 Science I Ames50011 United States

Background: Scientists around the world use NCBI’s non-redundant (NR) database to identify the taxonomic origin and functional annotation of their favorite protein sequences using BLAST. Unfortunately, due to the exponential growth of this database, many scientists do not have a good understanding of the contents of the NR database. There is a need for tools to explore the contents of large biological datasets, such as NR, to better understand the assumptions and limitations of the data they contain. Results: Protein sequence data, protein functional annotation, and taxonomic assignment from NCBI’s NR database were placed into a BoaG database, a domain-specific language and shared data science infrastructure for genomics, along with a CD-HIT clustering of all these protein sequences at different sequence similarity levels. We show that BoaG can efficiently perform queries on this large dataset to determine the average length of protein sequences and identify the most common taxonomic assignments and functional annotations. Using the clustering information, we also show that the non-redundant (NR) database has a considerable amount of annotation redundancy at the 95% similarity level. Conclusions: We implemented BoaG and provided a web-based interface to BoaG’s infrastructure that will help researchers to explore the dataset further. Researchers can submit queries and download the results or share them with others. Availability and implementation: The web-interface of the BoaG infrastructure can be accessed here: http://***/boag. Please use user = boag and password = boag to login. Source code and other documentation are also provided as a GitHub repository: https://***/boalang/NR_Dataset. © 2020, CC BY.

关键词： Database systems

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An improved simulation model for pedestrian crowd evacuation

arXiv

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arXiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani SulaymaniyahKRG46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler ErbilKRG44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Sydney BathurstNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil ErbilKRG44001 Iraq Department of Information Technology Lebanese French University ErbilKRG44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan BurdwanWest Bengal713101 India

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location;then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs. © 2020, CC BY.

关键词： Location

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Pigmented Skin Lesions Classification using Convolutional Neural Networks

Pigmented Skin Lesions Classification using Convolutional Ne...

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International Conference on Biomedical Innovations and Applications (BIA)

作者： Prasitthichai Naronglerdrit Iosif Mporas Isidoros Perikos Michael Paraskevas Dept. of Computer Engineering Kasetsart University Sriracha Campus Chonburi Thailand Dept. of Engineering University of Hertfordshire Hatfield United Kingdom Computer Engineering and Informatics Dept. University of Patras Patras Greece Dept. of Electrical and Computer Engineering University of Peloponnesse Patras Greece

ISBN: (纸本)9781728147550

In this paper we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image pre-processing for natural hair removal and image segmentation for extraction of the skin lesion area. The segmented images were processed by a convolutional neural network classifier. The training process was done by using the Keras and TensorFlow python packets with CUDA supported. The best performance was achieved by a convolutional neural network architecture with three convolution layers and the classification accuracy was equal to 76.83%.

关键词： Dermatoscopy Image classification Convolutional neural network CUDA computing Dermoscopy Image classification Taxonomy Skin lesions Defeathering Neural network optimum performance ARCHITECTURE Building construction image segmentation images neural net architecture

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A multidisciplinary definition of privacy labels: The story of princess privacy and the seven helpers

arXiv

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arXiv 2020年

作者： Johansen, Johanna Pedersen, Tore Fischer-Hübner, Simone Johansen, Christian Schneider, Gerardo Roosendaal, Arnold Zwingelberg, Harald Sivesind, Anders Jakob Noll, Josef Dept. of Informatics University of Oslo Norway Bjørknes University College Norway Dept. of Mathematics and Computer Science Karlstad University Sweden Norwegian University of Science and Technology Norway Dept. of Computer Science and Engineering University of Gothenburg Sweden Privacy Company Unabhängiges Landeszentrum für Datenschutz Schleswig-Holstein Dept. of Technology Systems University of Oslo Norway

Privacy is currently in distress and in need of rescue, much like princesses in the all-familiar fairytales. We employ storytelling and metaphors from fairytales to make reader-friendly and streamline our arguments about how a complex concept of Privacy Labeling (the ‘knight in shining armor’) can be a solution to the current state of Privacy (the ‘princess in distress’). We give a precise definition of Privacy Labeling (PL), painting a panoptic portrait from seven different perspectives (the ‘seven helpers’): Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological, and Multidisciplinary. We describe a common vision, proposing several important ‘traits of character’ of PL as well as identifying ‘undeveloped potentialities’, i.e., open problems on which the community can focus. More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like in order to address these needs. Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we consider to be, the ideal PL. In the end we present three approaches to establish and manage PL, through: self-evaluations, certifications, or community endeavors. Based on these, we sketch a roadmap for future developments. © 2020, CC BY-NC-SA.

关键词： Data privacy

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Non-blind RGB image watermarking technique using 2-level discrete wavelet transform and singular value decomposition 1

Non-blind RGB image watermarking technique using 2-level dis...

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1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Mekarsari, Yudit Arum Setiadi, De Rosal Ignatius Moses Sari, Christy Atika Rachmawanto, Eko Hari Muljono Dept. of Informatics Engineering Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia

ISBN: (纸本)9781538609545

The main problem in using various watermarking methods to secure digital images is how to optimize the trade-off between robustness watermarked image against the effect of distortion and imperceptibility on watermark insertion. This research proposes a watermark insertion method to protect copyright in true color (RGB) images by combining Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) algorithms. DWT process will be done as much as 2 levels then selected subband LL2 to inserted watermark by Singular Value Decomposition (SVD) method. Measuring tool in this research using MSE, PSNR, SSI M for imperceptibility and NC quality measurement for robustness measurement. The results show that the proposed scheme can optimize the trade-off between imperceptibility and robustness. Some attacks are used such as Gaussian noise, salt & pepper noise, crop, blur and rotate for watermarked image resilience. The PSNR and SSIM values produced from this method are fairly stable values above 40 dB for PSNR and above 0.99 for SSIM. While the average value of NC resulting from this method is 1. © 2018 IEEE.

关键词： Image watermarking

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MRI Image Segmentation using Morphological Enhancement and Noise Removal based on Fuzzy C-means 5

MRI Image Segmentation using Morphological Enhancement and N...

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5th International Conference on Information Technology, computer and Electrical engineering, ICITACEE 2018

作者： Setyawan, Robert Almahfud, Mustofa Alisahid Sari, Christy Atika Setiadi, De Rosal Ignatius Moses Rachmawanto, Eko Hari Dept. Informatics Engineering of Computer Science Faculty Dian Nuswantoro University Semarang Indonesia

ISBN: (纸本)9781538655276

Fuzzy C-means (FCM) is one of the image segmentation algorithms that has been widely used and proven to have good performance in image segmentation. This study aims to examine the effect of pre-processing stages on the FCM algorithm to segment the MRI image especially to detect brain tumors. This research proposed a pre-processing method with opening and closing morphology technique, noise removal with Weiner filter and contrast enhancement. The morphological method is used to connect the pixels adjacent. The morphological method used is opening and closing, because it can minimize the noise that is in the image by connecting the pixels are adjacent. Furthermore, noise removal and contrast enhancement are used to complete the morphological stage to produce MRI images that have less noise but are detailed. Based on the experimental results proved that the combination of pre-processing methods can improve the results of segmentation so that the tumor objects can be detected more accurately, where the accuracy is 73.585%. © 2018 IEEE.

关键词： Image segmentation

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Pathogenic bacteria genus classification using support vector machine

Pathogenic bacteria genus classification using support vecto...

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2018 International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2018

作者： Rahmayuna, Novita Rahardwika, Dewangga Satriya Sari, Christy Atika Setiadi, De Rosal Ignatius Moses Rachmawanto, Eko Hari Dept. Informatics Engineering Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia

ISBN: (纸本)9781538674222

This research was conducted to classify the pathogen bacteria at the level of the genus of the bacterial image derived from the optical sensor system. The study focused on exploring bacterial patterns and classifying four popular bacteria. The first step is to improve the image quality using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method on the object image so that it can generate the object in the clearer image. The second step is to extract the value of features to be trained using the Support Vector Machine (SVM) method, which includes texture analysis and Zernike Moment Invariant (ZMI). The next stage, selected features will be trained and classified using two kinds of SVM kernels i.e. linear and Radial Basis Function (RBF). Experiments were performed on four pathogenic bacteria such as Escherichia, Listeria, Salmonella, and Staphylococcus. Based on the results of classification experiments using SVM with RBF kernel is superior to Linear kernel that is obtained 90,33% accuracy. © 2018 IEEE.

关键词： Support vector machines

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Goal-Aware RSS for Complex Scenarios via Program Logic

arXiv

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arXiv 2022年

作者： Hasuo, Ichiro Eberhart, Clovis Haydon, James Dubut, Jérémy Bohrer, Rose Kobayashi, Tsutomu Pruekprasert, Sasinee Zhang, Xiao-Yi Pallas, Erik André Yamada, Akihisa Suenaga, Kohei Ishikawa, Fuyuki Kamijo, Kenji Shinya, Yoshiyuki Suetomi, Takamasa Tokyo101-8430 Japan Dept. Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609-2280 United States Inst. Software & Systems Engineering University of Augsburg Universitätstraße 6a AugsburgD-86135 Germany Cyber Physical Security Research Center AIST Aomi 2-4-7 Tokyo135-0064 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Mazda Motor Corporation Fuchu730-8670 Japan SOKENDAI The Graduate University for Advanced Studies Japan Tokyo Japan

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules;we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement. © 2022, CC BY-NC-ND.

关键词： computer circuits

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Simple and secure image steganography using LSB and triple XOR operation on MSB 1

Simple and secure image steganography using LSB and triple X...

引用

1st International Conference on Information and Communications Technology, ICOIACT 2018

作者： Astuti, Yani Parti Setiadi, De Rosal Ignatius Moses Rachmawanto, Eko Hari Sari, Christy Atika Dept. of Informatics Engineering Faculty of Computer Science Dian Nuswantoro University Semarang Indonesia

ISBN: (纸本)9781538609545

Least Significant Bit (LSB) is a very popular method in the spatial domain of steganographic images. This method is widely used and continues to be developed to date, because of its advantages in steganographic image quality. However, the traditional LSB method is very simple and predictable. It needs a way to improve the security of hidden messages in this way. This research proposes a simple and safe way to hide messages in LSB techniques. Three times the XOR operation is done to encrypt the message before it is embedded on the LSB. To facilitate the process of encryption and decryption of messages, three MSB bits are used as keys in XOR operations. The results of this study prove that this method provides security to messages with very simple operation. The imperceptibility quality of the stego image is also excellent with a PSNR value above 50 dB. © 2018 IEEE.

关键词： Steganography

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Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

arXiv

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arXiv 2021年

作者： Shoeibi, Afshin Sadeghi, Delaram Moridian, Parisa Ghassemi, Navid Heras, Jonathan Alizadehsani, Roohallah Khadem, Ali Kong, Yinan Nahavandi, Saeid Zhang, Yu-Dong Gorriz, Juan M. The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran The Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran The Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Deakin University VIC3217 Australia The School of Engineering Macquarie University Sydney2109 Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Informatics University of Leicester Leicester United Kingdom The Department of Signal Theory Networking and Communications Universidad de Granada Spain The Department of Psychiatry. University of Cambridge United Kingdom

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1DCNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25. © 2021, CC BY-NC-ND.

关键词： Diseases

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