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Web Security and Vulnerability: A Literature Review

Journal of Physics: Conference Series 2020年第2期1477卷

作者： H Yulianton H L H S Warnars B Soewito F L Gaol E Abdurachman Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Information Technology Universitas Stikubank Semarang Indonesia 50243

The web continues to grow and attacks against the web continue to increase. This paper focuses on the literature review on scanning web vulnerabilities and solutions to mitigate web attacks. Vulnerability scanning methods will be reviewed as well as frameworks for improving web security. This research is the basis for future work that will end with the elaboration of web scanning and security with the aim of proposing better innovations.

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Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weak lensing with HSC and SDSS using the minimal bias model

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Physical Review D 2023年第12期108卷 123521-123521页

作者： Sunao Sugiyama Hironao Miyatake Surhud More Xiangchong Li Masato Shirasaki Masahiro Takada Yosuke Kobayashi Ryuichi Takahashi Takahiro Nishimichi Atsushi J. Nishizawa Markus M. Rau Tianqing Zhang Roohi Dalal Rachel Mandelbaum Michael A. Strauss Takashi Hamana Masamune Oguri Ken Osato Arun Kannawadi Bau-Ching Hsieh Wentao Luo Robert Armstrong James Bosch Yutaka Komiyama Robert H. Lupton Nate B. Lust Satoshi Miyazaki Hitoshi Murayama Yuki Okura Paul A. Price Philip J. Tait Masayuki Tanaka Shiang-Yu Wang Kavli Institute for the Physics and Mathematics of the Universe (WPI) The University of Tokyo Institutes for Advanced Study (UTIAS) The University of Tokyo Chiba 277-8583 Japan Department of Physics The University of Tokyo Bunkyo Tokyo 113-0031 Japan Kobayashi-Maskawa Institute for the Origin of Particles and the Universe (KMI) Nagoya University Nagoya 464-8602 Japan Institute for Advanced Research Nagoya University Nagoya 464-8601 Japan The Inter-University Centre for Astronomy and Astrophysics Post bag 4 Ganeshkhind Pune 411007 India McWilliams Center for Cosmology Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA National Astronomical Observatory of Japan Mitaka Tokyo 181-8588 Japan The Institute of Statistical Mathematics Tachikawa Tokyo 190-8562 Japan Department of Astronomy/Steward Observatory University of Arizona 933 North Cherry Avenue Tucson Arizona 85721-0065 USA Faculty of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Center for Gravitational Physics and Quantum Information Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan Department of Astrophysics and Atmospheric Sciences Faculty of Science Kyoto Sangyo University Motoyama Kamigamo Kita-ku Kyoto 603-8555 Japan Gifu Shotoku Gakuen University Gifu 501-6194 Japan Institute for Advanced Research/Kobayashi Maskawa Institute Nagoya University Nagoya 464-8602 Japan High Energy Physics Division Argonne National Laboratory Lemont Illinois 60439 USA Department of Astrophysical Sciences Princeton University Princeton New Jersey 08544 USA National Astronomical Observatory of Japan National Institutes of Natural Sciences Mitaka Tokyo 181-8588 Japan Center for Frontier Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan Research Center for the Early Universe The University of Tokyo Bunkyo Tokyo 113-0031 Japan Center for Frontier Science Chiba University Chiba 263-852

We present cosmological parameter constraints from a blind joint analysis of three two-point correlation functions measured from the Year 3 Hyper Suprime-Cam (HSC-Y3) imaging data, covering about 416 deg2, and the SDSS DR11 spectroscopic galaxies spanning the redshift range [0.15, 0.70]. We subdivide the SDSS galaxies into three luminosity-cut, and therefore nearly volume-limited samples separated in redshift, each of which acts as a large-scale structure tracer characterized by the measurement of the projected correlation function, wp(R). We also use the measurements of the galaxy-galaxy weak-lensing signal ΔΣ(R) for each of these SDSS samples which act as lenses for a secure sample of source galaxies selected from the HSC-Y3 shape catalog based on their photometric redshifts. We combine these measurements with the cosmic shear correlation functions, ξ±(ϑ) measured for our HSC source sample. We model these observables with the minimal bias model of the galaxy clustering observables in the context of a flat ΛCDM cosmology. We use conservative scale cuts, R>12 and 8h−1 Mpc for ΔΣ and wp, respectively, where the minimal bias model is valid, in addition to conservative prior on the residual bias in the mean redshift of the HSC photometric source galaxies. We present various validation tests of our model as well as analysis methods. Our baseline analysis yields S8=0.775−0.038+0.043 (68% C.I.) for the ΛCDM model, after marginalizing over uncertainties in other parameters. Our value of S8 is consistent with that from the Planck 2018 data, but the credible interval of our result is still relatively large. We show that various internal consistency tests based on different splits of the data are passed. Our results are statistically consistent with those of a companion paper, which extends this analysis to smaller scales with an emulator-based halo model, using ΔΣ(R) and wp(R) down to R>3 and 2h−1 Mpc, respectively.

关键词： Cosmological parameters Dark energy Dark matter Large scale structure of the Universe

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Bridge Sub Structure Defect Inspection Assistance by using Deep Learning

Bridge Sub Structure Defect Inspection Assistance by using D...

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IEEE International Conference on Awareness Science and technology

作者： Pravee Kruachottikul Kittikul Kovitanggoon Nagul Cooharojananone Donnaphat Trakulwaranont Gridsada Phanomchoeng Kanokwan Atchariyachanvanich Thira Chavarnakul Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Mathematics and Computer Science Chulalongkorn University Bangkok Thailand Department of Mechanical Engineering Chulalongkorn University Bangkok Thailand Department of Commerce Chulalongkorn University Bangkok Thailand Faculty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Road transportation is the most popular transportation in Thailand, which the top two highest traffic are the region-to-region highways;and then inter-city highways. Therefore, the regular maintenance is required to maintain the good condition due to road safety. The most significant process of bridge inspection procedures is sub structure inspection, which requires visual inspection as an initial step. This process is used to quick determine the damage severity i.e. appearance and crack that may cause damage to the structure strength. The current process requires that the experienced maintenance engineer to be on the field in order to visual inspect and estimate whether the maintenance is required. Yet, due to the limitation of number of expert engineers to be on the field, the photo verification is introduced to assist them so that they are no need on every inspection site. However, using human to verify has no standard and uncontrollable. They need to have experience and good knowledge. As well as it is highly depended on individual decisionmaking skill. Thus, in this paper, the deep learning technique will be presented to assist the expert for quality inspection process of bridge sub structure images. That is using image enhancement and then image splitting and overlapping for image pre-processing. After that applying CNNs for object classification. As a result, the total accuracy is 89% based on 3926 dataset.

关键词： Bridges Inspection Machine learning Maintenance engineering Convolution Training Concrete

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Hyper Suprime-Cam Year 3 results: Cosmology from cosmic shear two-point correlation functions

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Physical Review D 2023年第12期108卷 123518-123518页

作者： Xiangchong Li Tianqing Zhang Sunao Sugiyama Roohi Dalal Ryo Terasawa Markus M. Rau Rachel Mandelbaum Masahiro Takada Surhud More Michael A. Strauss Hironao Miyatake Masato Shirasaki Takashi Hamana Masamune Oguri Wentao Luo Atsushi J. Nishizawa Ryuichi Takahashi Andrina Nicola Ken Osato Arun Kannawadi Tomomi Sunayama Robert Armstrong James Bosch Yutaka Komiyama Robert H. Lupton Nate B. Lust Lauren A. MacArthur Satoshi Miyazaki Hitoshi Murayama Takahiro Nishimichi Yuki Okura Paul A. Price Philip J. Tait Masayuki Tanaka Shiang-Yu Wang McWilliams Center for Cosmology Department of Physics Carnegie Mellon University 5000 Forbes Avenue Pittsburgh Pennsylvania 15213 USA Kavli Institute for the Physics and Mathematics of the Universe (WPI) The University of Tokyo Institutes for Advanced Study (UTIAS) The University of Tokyo Chiba 277-8583 Japan Department of Physics The University of Tokyo Bunkyo Tokyo 113-0031 Japan Department of Astrophysical Sciences Princeton University Princeton New Jersey 08544 USA High Energy Physics Division Argonne National Laboratory Lemont Illinois 60439 USA The Inter-University Centre for Astronomy and Astrophysics Post bag 4 Ganeshkhind Pune 411007 India Kobayashi-Maskawa Institute for the Origin of Particles and the Universe (KMI) Nagoya University Nagoya 464-8602 Japan Institute for Advanced Research Nagoya University Nagoya 464-8601 Japan National Astronomical Observatory of Japan National Institutes of Natural Sciences Mitaka Tokyo 181-8588 Japan The Institute of Statistical Mathematics Tachikawa Tokyo 190-8562 Japan Center for Frontier Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan Research Center for the Early Universe The University of Tokyo Bunkyo Tokyo 113-0031 Japan School of Physical Sciences University of Science and Technology of China Hefei Anhui 230026 China CAS Key Laboratory for Researches in Galaxies and Cosmology/Department of Astronomy School of Astronomy and Space Science University of Science and Technology of China Hefei Anhui 230026 China Gifu Shotoku Gakuen University Gifu 501-6194 Japan Faculty of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Argelander Institut für Astronomie Universität Bonn Auf dem Hügel 71 53121 Bonn Germany Department of Physics Graduate School of Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba 263-8522 Japan Department of Astronomy and Steward Observatory University of Arizona 933 North Cherry Avenue Tucson Arizona 85719 U

We perform a blinded cosmology analysis with cosmic shear two-point correlation functions measured from more than 25 million galaxies in the Hyper Suprime-Cam three-year shear catalog in four tomographic redshift bins ranging from 0.3 to 1.5. After conservative masking and galaxy selection, the survey covers 416 deg2 of the northern sky with an effective galaxy number density of 15 arcmin−2 over the four redshift bins. The 2PCFs adopted for cosmology analysis are measured in the angular range; 7.1<θ/arcmin<56.6 for ξ+ and 31.2<θ/arcmin<248 for ξ−, with a total signal-to-noise ratio of 26.6. We apply a conservative, wide, flat prior on the photometric redshift errors on the last two tomographic bins, and the relative magnitudes of the cosmic shear amplitude across four redshift bins allow us to calibrate the photometric redshift errors. With this flat prior on redshift errors, we find Ωm=0.256−0.044+0.056 and S8≡σ8Ωm/0.3=0.769−0.034+0.031 (both 68% C.I.) for a flat Λ cold dark matter cosmology. We find, after unblinding, that our constraint on S8 is consistent with the Fourier space cosmic shear and the 3×2 pt analyses on the same HSC dataset. We carefully study the potential systematics from astrophysical and systematic model uncertainties in our fiducial analysis using synthetic data, and report no biases (including projection bias in the posterior space) greater than 0.5σ in the estimation of S8. Our analysis hints that the mean redshifts of the two highest tomographic bins are higher than initially estimated. In addition, a number of consistency tests are conducted to assess the robustness of our analysis. Comparing our result with Planck-2018 cosmic microwave background observations, we find a ∼2σ tension for the ΛCDM model.

关键词： Cosmological parameters Cosmology Dark matter Large scale structure of the Universe

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Web Application Vulnerability Detection Using Taint Analysis and Black-box Testing

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IOP Conference Series: Materials Science and Engineering 2020年第1期879卷

作者： Heribertus Yulianton Agung Trisetyarso Wayan Suparta Bahtiar Saleh Abbas Chul Ho Kang Computer Science Department Binus Graduate Program | Doctor of Computer Science Bina Nusantara University West Jakarta Indonesia Faculty of Information Technology Universitas Stikubank Semarang Department of Informatics Universitas Pembangunan Jaya

Web applications continue to grow however web attacks are also increasing, this shows an increase in web application vulnerabilities. Several methods have been used to detect vulnerabilities in web applications such as black-box testing, dynamic analysis, and static analysis. In this article, we propose a framework for detecting web application vulnerabilities by combining all three methods. The strengths of one method are used to overcome the weaknesses of another method. This framework is believed to provide better results than if each method was used separately.

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High Performance Computing (HPC) Implementation: A Survey 1

High Performance Computing (HPC) Implementation: A Survey

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1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Assiroj, Priati Hananto, April Lia Fauzi, Ahmad Hendric Spits Warnars, Harco Leslie Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Information System Faculty of Technology and Computer Science United States Buana Perjuangan University Faculty of Technology and Computer Science Information Technology Karawang Indonesia

ISBN: (纸本)9781538694220

To maintain excellence in global hyper-competitive economies in the upcoming decades, manufacturers must improve the design, development, and distribution of subsequent product generations, production technologies, equipment, and processes. High-performance computing (HPC) solves the application of significant challenges using computer modeling, simulation, and analytical technology. By using HPC technology, products design will be faster, making and testing prototypes will save time, the production process can be simplified, the cost of innovation will decrease, and high-value innovation will be quickly developed. This study uses the PRISMA method, based on predetermined keywords and analyzes the algorithms, frameworks and programming languages and the processes that are most commonly used to optimize the Graphics Processing Unit (GPU). The results of this study can be a reference for future research on the implementation of High-Performance Computing (HPC) Application by combine modeling, simulation, and analysis. © 2018 IEEE.

关键词： Graphics processing unit

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The Accuracies of ANFIS and Genetic Algorithm with Tournament Selection on Classifying Hepatitis Data

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： W FA Siahaan O S Sitompul Z Situmorang Graduate Program of Computer Science Universitas Sumatera Utara Medan Indonesia Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Universitas Katolik Santo Thomas Medan Indonesia

Classification is an important technique in data mining to create patterns and data modeling. Hepatitis is a disease that is dangerous to humans as this disease affects the human liver which is a vital organ. Early detection of hepatitis by diagnosing its occurrence on patients is needed to give them help, care, and treatment. Therefore, a classification technique is required to create patterns and models of data. To achieve accuracy in classifying hepatitis there would be requirements to separate relevant from irrelevant features. By only considering relevance features in classifying hepatitis, it is expected that the classification process will be faster, easier, and more accurate. In this research, the ANFIS algorithm and genetic algorithm are used in classifying the diagnosis of hepatitis based on a dataset from the UCI data mining repository. Results obtained showed that the genetic algorithm gave a slightly higher accuracy of 98.73% compared to 86.67% results obtained using ANFIS.

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Corrigendum to "Cross-sectional analysis of students and school workers reveals a high number of asymptomatic SARS-CoV-2 infections during school reopening in Brazilian cities" [Heliyon 8 (11) (November 2022) Article e11368]

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Heliyon 2023年第3期9卷 e14398页

作者： Lysandro P Borges Adriana G Guimarães Dennyson Leandro M Fonseca Paula P Freire Íkaro D C Barreto Daniela R V Souza Ricardo Q Gurgel Aline S A Lopes José Melquiades de Rezende Neto Kezia A Dos Santos Igor L S Matos Grazielly B da Invenção Brenda M Oliveira Aryanne A Santos Daniele Almeida Soares Pamela C de Jesus Cliomar A Dos Santos Marco A O Goes Desirée Rodrigues Plaça Igor Salerno Filgueiras Alexandre H C Marques Gabriela Crispim Baiocchi William CabralMiranda Gustavo Cabral de Miranda Niels Olsen Saraiva Camara Vera Lúcia Garcia Calich Rodrigo Nalio Ramos Helder I Nakaya Vanderson Rocha Lasse M Giil Hans D Ochs Lena F Schimke Mércia S F de Souza Luis E Cuevas Aline F Martins Otavio Cabral-Marques Department of Pharmacy Federal University of Sergipe São Cristóvão Sergipe Brazil. Interunit Postgraduate Program on Bioinformatics Institute of Mathematics and Statistics (IME) University of Sao Paulo (USP) Sao Paulo SP Brazil. Department of Immunology Institute of Biomedical Sciences University of São Paulo São Paulo SP Brazil. Nuclear and Energy Technology Graduate Program (UFPE) Recife Pernambuco Brazil. Department of Education in Health Lagarto and Post-graduate Program in Health Sciences Federal University of Sergipe Sergipe Brazil. Department of Medicine and Post-Graduate Programs in Parasitic Biology and Health Sciences Federal University of Sergipe Aracaju Sergipe Brazil. Sergipe Central Public Health Laboratory Aracaju Sergipe Brazil. State Health Department Sergipe Brazil. Department of Clinical and Toxicological Analyses School of Pharmaceutical Sciences University of São Paulo São Paulo SP Brazil. The Sabará Children's Hospital and PENSI Institute São Paulo SP Brazil. Laboratory of Medical Investigation in Pathogenesis and Directed Therapy in Onco-ImmunoHematology (LIM-31) Department of Hematology and Cell Therapy Hospital das Clínicas HCFMUSP Faculdade de Medicina University of São Paulo São Paulo Brazil. Instituto D'Or de Ensino e Pesquisa São Paulo Brazil. Hospital Israelita Albert Einstein São Paulo 05652-900 Brazil. Scientific Platform Pasteur University of São Paulo São Paulo 05508-020 Brazil. Fundação Pró-Sangue-Hemocentro de São Paulo São Paulo Brazil. Churchill Hospital Department of Hematology University of Oxford Oxford United Kingdom. Department of Internal Medicine Haraldsplass Deaconess Hospital Bergen Norway. Department of Pediatrics University of Washington School of Medicine And Seattle Children's Research Institute Seattle WA USA. Department of Clinical Sciences Liverpool School of Tropical Medicine Liverpool United Kingdom. Laboratory of Medical Investigation 29 University of São Paulo School of Medicine Brazil. D

[This corrects the article DOI: 10.1016/***.2022.e11368.].

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Ensembling Handcarafted Features to Representation Learning for Content Based Image Classification

Ensembling Handcarafted Features to Representation Learning ...

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IEEE Punecon

作者： Rik Das Khushbu Kumari P.K. Manjhi Sudeep D. Thepade Dept.of Post Graduate Program-Information Technology Xavier Institute of Social Service Ranchi Jharkhand India Dept.of Computer Applications Yogoda Satsanga Mahavidyalaya Ranchi Jharkhand India Dept.of Mathematics Vinoba Bhave University Hazaribag Jharkhand India Computer Engineering Dept. Pimpri Chinchwad College of Engineering Maharashtra India

ISBN: (数字)9781728119243

ISBN: (纸本)9781728119250

Content Based Image Classification (CBIC) has garnered massive interest within research community for it's applications and significances in multiple areas. This paper has explored a robust feature vector definition scheme based on fusion of handcrafted features to neural network based feature extraction, popularly known as representation learning. The extracted handcrafted features are primarily evaluated with classification results to identify the best color space for significant descriptor definition. Further, feature vector extraction using pre-trained Convolution Neural Network is exercised for evaluating classification performance with representation learning features. Finally, an early fusion of handcrafted feature and representation learning feature is accomplished to examine the classification performance of the model. The test bed is prepared using Wang dataset having 10 different categories of 1000 images. Six different color spaces are explored to finalize the best fit for feature extraction with the proposed handcrafted technique. Performance analyses of the proposed model have turned out to be superior compared to state-of-the-art techniques and have established the importance of fusion based approach in enhancing the accuracy of content based image classification.

关键词： Colour Intervals Color Space Hand-Crafted Features Representation Learning Fusion Classification color spaces Image classification Fusion reactions Fusion, biological Taxonomy FUSION fusion (melting) Feature extraction Learning based test bed Neural network Eigenvector

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Performance improvement of palm oil foaming agent concentrates as fire extinguishment materials in peatlands

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IOP Conference Series: Earth and Environmental Science 2020年第1期460卷 012026-012026页

作者： M Rivai E Hambali A Suryani G Pramuhadi P Subekti Surfactant and Bioenergy Research Center IPB University Indonesia Department of Agroindustrial Technology IPB University Indonesia Department of Mechanical and Biosystem Engineering IPB University Indonesia Post Graduate Student Study Program of Agroindustrial Technology IPB University Indonesia Department of Mechanical Engineering Pasir Pengaraian University Indonesia

Foaming agent concentrate made from palm oil produced as a fire extinguishing material in peatlands at a laboratory-scale test were able to extinguish a fire within 3.16 to 4.49 hours. Fire extinguishing ability of this foaming agent concentrate resulted in a lower solution use and shorter extinguishing time than water. However, when compared to commercial products, fire extinguishing time was still longer although the amount of solution needed to extinguish the fire was lower. Therefore, it was necessary to improve the formula to produce a foaming agent concentrate that was able to extinguish the fire in a shorter time with a lower amount of fire extinguishing solution. This study aims to improve the performance of palm oil foaming agent concentrate on extinguishing the fire in peatlands. Efforts to improve the performance were made by reformulation and the addition of chemical fire retardants. Results showed that the addition of fire retardant reduced foaming ability, increased surface tension, lowered viscosity, lowered contact angle, and reduced fire extinguishing time to about 13.08-69.21%.

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