Input Validation Research Articles

Monte Carlo calculation of absorbed dose in breast radiotherapy due to photon beams of different energies

In this study, breast cancer treatment reproduced from a VARIAN eclipse planning system is simulated. This treatment was simulate using the Monte Carlo system. To this end, the body of the woman is simulate by the geometry of the Phantom FAX06, which was reproduced using the ImageJ software that allowed the import and conversion of DICOM images into valid input files for the MCNPX code. Additionally, geometry of the linear accelerator head (including the MLC) was constructed. Co-60 photons, 6 and 15 MV photon spectra were simulate with an angle that allowed for the recreation of two tangential fields used in conventional radiation therapy treatments for breast cancer. The main objective of this study was to estimate the absorbed dose in each of the organs involved in this type of treatment and for each of the sources simulated. The result allowed us to establish the differences between the absorbed dose that is transferred to the target organ and that which is transferred to the organs at risk, such as the heart and lungs. The dose absorbed in organs at risk and target organ when Co-60 is used has advantages over the use of 6 and 15 MV beams, these results should be considered by the manufacturers of LINAC’S and by the scientific community. The proposal in this sense is to manufacture a device with Co-60 and with the technology of a LINAC for the treatment of some cancer.

Short-Term Stock Price Prediction Based on CAE-LSTM Method

Artificial intelligence methods are important tools for mining information for forecasting in the stock market. Most of the literature related to short-term stock price prediction focuses on the technical data, but in the real market, many individual investors make investment decisions more from stock price shape characteristics rather than specific stock price values. Compared with traditional measurement methods, deep neural networks perform better in processing high-dimensional complex data such as images. This paper proposes a model that combines CAE (convolutional autoencoder) and LSTM (long short-term memory) neural network, uses CAE to extract stock price image feature data, and combines technical data to predict short-term stock prices. The results show that the CAE-LSTM model, based on stock price image morphological feature data and technical data, performs well in short-term stock price prediction and has good generalization ability. The root mean square error of the CAE-LSTM model decreased by about 4% from that of LSTM. CAE-LSTM models have better predictive power than LSTM models that only use technical indicator data as valid inputs.

Statically Detecting Vulnerabilities by Processing Programming Languages as Natural Languages

Web applications continue to be a favorite target for hackers due to a combination of wide adoption and rapid deployment cycles, which often lead to the introduction of high-impact vulnerabilities. Static analysis tools are important to search for vulnerabilities automatically in the program source code, supporting developers on their removal. However, building these tools requires programming the knowledge on how to discover the vulnerabilities. This article presents an alternative approach in which tools <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn</i> to detect flaws automatically by resorting to artificial intelligence concepts, more concretely to natural language processing. The approach employs a sequence model to learn to characterize vulnerabilities based on an annotated corpus. Afterwards, the model is utilized to discover and identify vulnerabilities in the source code. It was implemented in the DEKANT tool and evaluated experimentally with a large set of PHP applications and WordPress plugins. Overall, we found several thousand vulnerabilities belonging to 15 classes of input validation vulnerabilities, where 4143 of them were zero-day.

Wet aggregate stability modeling based on support vector machine in multiuse soils

Accurate assessment of wet aggregate stability is critical in evaluating soil quality. However, a few general models are used to assess it. In this work, we use the support vector machine to evaluate wet aggregate stability and compare it with a benchmark model based on artificial neural networks. One hundred thirty-four soil samples from various land uses, such as crops, grasslands, and bare land are adopted to verify the effectiveness of the proposed method and confirm the valid input parameters. We select 107 samples for calibrating the prediction model and the rest for evaluation. Experiments show that organic carbon is the main control parameter of wet aggregate stability, although the most influential factors for different land use are various. Comparing the determination coefficient and the root mean square error, it proves that the support vector machine method is superior to the artificial neural network method. In addition, the relative importance analysis shows that contents of organic carbon, silt, and clay are the primary input parameters. Finally, the impact of land use and management types is evaluated.

Sustainable Urban Mobility Boost Smart Toolbox Upgrade

SUMBooST2 research develops universally applicable data science methodology which extracts key urban mobility parameters and origin/destination matrices from the anonymized big data set gathered from telecom operator. The methodology (toolbox) provides transport planners with a method for fast, efficient, and reliable provision of data on movements within the certain area. Origin/destination matrices with modal split will provide transport planners with valid input data for the planning of urban transport systems. The algorithms which separate relevant mobility data from the overall dataset are the unique part of the toolbox. The algorithms to identify passenger car trips are developed in 2020 project SUMBooST, and they are being upgraded in 2021 to detect trips made by active mobility modes and public transport. For the methodology to be valid, it must be implemented in representative number of cities. Previous SUMBooST project included implementation and validation in the City of Rijeka, and SUMBooST2 continues with two other cities, City of Zagreb, and City of Dubrovnik. The aim of the paper is to present innovative toolbox for the boost of sustainable urban planning based on big data science.

Assessing and assuring interoperability of a genomics file format.

MotivationBioinformatics software tools operate largely through the use of specialized genomics file formats. Often these formats lack formal specification, making it difficult or impossible for the creators of these tools to robustly test them for correct handling of input and output. This causes problems in interoperability between different tools that, at best, wastes time and frustrates users. At worst, interoperability issues could lead to undetected errors in scientific results.ResultsWe developed a new verification system, Acidbio, which tests for correct behavior in bioinformatics software packages. We crafted tests to unify correct behavior when tools encounter various edge cases—potentially unexpected inputs that exemplify the limits of the format. To analyze the performance of existing software, we tested the input validation of 80 Bioconda packages that parsed the Browser Extensible Data (BED) format. We also used a fuzzing approach to automatically perform additional testing. Of 80 software packages examined, 75 achieved less than 70% correctness on our test suite. We categorized multiple root causes for the poor performance of different types of software. Fuzzing detected other errors that the manually designed test suite could not. We also created a badge system that developers can use to indicate more precisely which BED variants their software accepts and to advertise the software’s performance on the test suite.Availability and implementationAcidbio is available at https://github.com/hoffmangroup/acidbio.Supplementary information Supplementary data are available at Bioinformatics online.

A Novel Machine Learning–Based Gap-Filling of Fine-Resolution Remotely Sensed Snow Cover Fraction Data by Combining Downscaling and Regression

Abstract Satellite-based remotely sensed observations of snow cover fraction (SCF) can have data gaps in spatially distributed coverage from sensor and orbital limitations. We mitigate these limitations in the example fine-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data by gap-filling using auxiliary 1-km datasets that either aid in downscaling from coarser-resolution (5 km) MODIS SCF wherever not fully covered by clouds, or else by themselves via regression wherever fully cloud covered. This study’s prototype predicts a 1-km version of the 500-m MOD10A1 SCF target. Due to noncollocatedness of spatial gaps even across input and auxiliary datasets, we consider a recent gap-agnostic advancement of partial convolution in computer vision for both training and predictive gap-filling. Partial convolution accommodates spatially consistent gaps across the input images, effectively implementing a two-dimensional masking. To overcome reduced usable data from noncollocated spatial gaps across inputs, we innovate a fully generalized three-dimensional masking in this partial convolution. This enables a valid output value at a pixel even if only a single valid input variable and its value exist in the neighborhood covered by the convolutional filter zone centered around that pixel. Thus, our gap-agnostic technique can use significantly more examples for training (∼67%) and prediction (∼100%), instead of only less than 10% for the previous partial convolution. We train an example simple three-layer legacy super-resolution convolutional neural network (SRCNN) to obtain downscaling and regression component performances that are better than baseline values of either climatology or MOD10C1 SCF as relevant. Our generalized partial convolution can enable multiple Earth science applications like downscaling, regression, classification, and segmentation that were hindered by data gaps.

Fingerprint Recognition Based on Deep Learning Pre-Train with Our Best CNN Model for Person Identification

In this article, we use pre-train VGG16, VGG19, and ResNet50 with ImageNet wights and our best CNN model to identify human fingerprint patterns. The system including pre-processing phase where the input fingerprint images first technique apply cropping and normalize for unwanted part remove of fingerprint images and normalize its dimension, second Image Enhancement for removing noise in to ridgelines, and last Canny Edge Detection technique for adjustment to smooth image with Gaussian to remove noise. Then apply one by one model on KVKR fingerprint dataset. Our best CNN model has automatically extracted features and RMSprop optimizer use for classification this features. This study performing experimental work of each pre-processed dataset and testing these three models with different dataset size of input train, test, and validation data. The VGG16 model got a better recognition accuracy than VGG19 and ResNet50 models.

Integration of robotic total station and digital image correlation to assess the three-dimensional surface kinematics of a landslide

In the field of landslide monitoring, the assessment of the spatially-distributed three-dimensional surface displacement is crucial to understand the underlying mechanisms. Nevertheless, available technologies and techniques that provide such a datum are few and often suffer spatio-temporal resolution, logistic and/or financial limitations. In this framework, we developed a methodology that merges the three-dimensional measurements at specific points, acquired by a robotic total station (RTS), and the spatially-distributed data obtained with digital image correlation (DIC) of time-lapse camera photographs, to achieve the spatially-distributed three-dimensional surface displacement. The integration method follows this procedure: i) the DIC results are orthorectified on an existing digital elevation model; ii) the RTS data are rototranslated into the camera coordinate system; iii) the ratio α between displacement vertical component and module measured by the RTS is calculated and interpolated across the region of interest; iv) the orthorectified DIC results are rescaled according to α, obtaining the three surface displacement components; v) the displacement vector is rototranslated into the geographical coordinate system. The sensitivity analysis respect to α revealed that the integration method can be successfully applied even with a limited number of RTS measurement points. The developed methodology has been applied to the Mont de La Saxe rockslide case study, during a phase of strong acceleration. In this period, the displacement magnitudes varied between 0.1 m and 10 m, thus providing a stress-test input for methodology development and validation. The results have been compared with independent ground-based interferometric radar measurements, obtaining 0.99 linear correlation coefficient and median absolute deviation of 0.086 m, which is comparable with the DIC measurement uncertainty. The proposed method is based on the use of low-cost portable and commonly used field equipment, thus it can be easily implemented in existing monitoring networks without additional financial costs.

Soft City Sensing: A turn to computational humanities in data-driven urbanism

Data-driven urbanism is often entangled with the smart city and practiced in a way that prioritizes control over physical objects and downplays the human and political aspects of data. We label this approach ‘hard city sensing’ (HCS) and we argue that the rise of the ‘digital city’ offers the empirical foundation for more humanistic approaches. Driven by the ambition to untangle data-driven urbanism from HCS, this paper reviews two decades of scholarship that has used digital traces as an empirical ground for understanding urban phenomena. The review identifies four distinct ways of working with digital traces of which three pave the way for new ways of problematizing the city. Instead of abandoning the idea of data-driven urbanism, we propose the framework of 'soft city sensing' (SCS) as way to re-engage with it with inspiration from these pioneering works. However, this requires a willingness to revisit central epistemological commitments that currently serve as standards for how to “properly” do data projects. We therefore urge qualitative urban scholars to ponder the possibilities of furthering their urban interest by ‘thinking with algorithms’ while retaining their interpretative ambitions just as we identify a need for urban decion-makers to expand their criteria for what serves as valid data inputs to urban planning.

Black Box Testing pada Aplikasi Pencatatan Peminjaman Buku Menggunakan Boundary Value Analysis

The book borrowing recording application can record activities related to borrowing books in the library, such as recording a track record of borrowing a book. However, applications that have been made in such a way need to go through a test flow to find out how far the performance and quality of the application itself is. Therefore, it is necessary to have a test to find out how the quality is. So, the purpose of this research is to carry out a testing on the book lending recording application to identify whether the application is in accordance with user needs or not. The test, which is one of the stages of the Software Development Life Cycle (SDLC) methodology carried out on this book lending recording application, applies Black Box Testing with the Boundary Value Analysis method which focuses on aspects of input validation, observing input content, and input results. The test results show that in the book borrowing recording system, discrepancies were found in the input process in the loan registration feature and book borrowing form so that it needed to be corrected before being used by the user.

Damage detection for tethers of submerged floating tunnels based on convolutional neural networks

A submerged floating tunnel, consisting of a tunnel and tethers, is effective as a sea-crossing transportation infrastructure element in deep-water environments. Instead of fixed columns, the tunnel is positioned by the tethers. This means that a significant structural performance degradation in the mooring can directly induce a change in the structural state; moreover, the failure of the tethers will eventually lead to structural instability. Therefore, structural health monitoring is essential for the tethers, as well as for the main tunnel segments. Unfortunately, there are limitations to the applicable sensors for measuring the structural responses required to evaluate the structural state and for estimating the structural damage to the tethers, owing to the environmental characteristics. Therefore, it is necessary to develop and apply an effective damage detection method to secure structural safety. Accordingly, in this study, an advanced damage detection method is proposed for the tethers of submerged floating tunnels based on the convolutional neural network (CNN). The damage detection estimation model is based on a convolutional neural network framework consisting of input, output, and hidden layers for training, validation, testing, and application. The model is trained using structural response data obtained by a hydrodynamics-based time-domain analysis considering various waves and tether damage cases. For successful training, the time-domain structural response data are converted to discretized image data. The accuracy of the proposed CNN-based damage detection models for the various damage rates and noise levels was evaluated. The accuracy of the CNN–S-16-Model which uses 16 sensors with 0–5% level noise signals ranges from 98.4 to 100.0% for 50% damage, from 97.6 to 100.0% for 30% damage, and from 92.0 to 100.0% for 15% damage to the tethers. The noise level significantly affected the damage detection accuracy for the relatively low damage rate cases. Therefore, rational noise filtering is required to enhance the accuracy for minor damage cases.

IIDMCC: An Innovation Idea Discovery Model Using Online Customers Complaint Messages

&lt;p&gt;Online customers&amp;rsquo; complaints have attracted increasing attention to innovation developers. By applying text mining and classification-oriented data mining techniques, an innovation idea discovery model using online customers&amp;rsquo; complaint messages (IIDMCC) was proposed and implemented in this article. Methods included text mining to derive bags of words, sparsity exclusion to produce a term matrix, and supervised classification data mining to reveal decision rules. The IIDMCC showed 90.63% prediction accuracy based on 14720 complaint messages collected from official forum and online communities of a case company in the mobile phone sector from Taiwan. Validation of data inputs, method, and outputs was conducted via case company specialists. The article concludes that analyses of online complaint messages may potentially contribute to the exploration and discovery of innovation ideas. The paper demonstrates the use of mining open textual data in general and complaint messages in particular in the domain of knowledge discovery in databases.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Not all datasets are born equal: On heterogeneous tabular data and adversarial examples

Recent work on adversarial learning has mainly focused on neural networks and domains in which those networks excel, such as computer vision and audio processing. Typically, the data in those domains is homogeneous, whereas domains with heterogeneous tabular datasets remain underexplored, despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data and the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proven challenging, and a generic attack method has not yet been proposed. However, this study argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data, such as images. To support this claim, a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces is introduced. The framework defines distribution-aware constraints to preserve the consistency of the adversarial examples and then incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets, we focus on ℓ0 perturbations and demonstrate their applicability in real life. The effectiveness of the suggested approach is demonstrated using three datasets from different content domains. The results show that despite the constraints imposed on the input validity in heterogeneous datasets, machine learning models trained using such data are still susceptible to adversarial examples.

Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory

This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems.

Simulation Might Change Your Results: A Comparison of Context-Aware System Input Validation in Simulated and Physical Environments

Simulation Might Change Your Results: A Comparison of Context-Aware System Input Validation in Simulated and Physical Environments

Exploring How to Apply Secure Software Design Principles

Secure design principles (SDPs) are employed to be a solution against many types of attacks. However, it has been shown that software developers are not familiar with the notion of SDPs or do not know how to implement them in the design stage. This paper tries to bridge this gap by applying SDPs to a real-world software project, electronic promotion system (ePS), and commenting on the contribution of each SDP. Saltzer and Schroeder’s eight principles, along with three additional principles proposed by others, are chosen to be applied to ePS. The results indicate that most of the principles enumerated here were instrumental and applied in the ePS’s design. Most of the eleven SDPs, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">economy of mechanism, fail-safe defaults, least privilege, least common mechanisms, sound authentication, defense in depth</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input validation</i> were implemented on ePS to a great extent. Others, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">separation of privileges</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">psychological acceptability</i> , were applied to a limited extent. The remaining two principles, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete mediation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">open design</i> , did not play a vital role, as ePS by itself satisfies these two principles. Some contradictions and interrelations among the SDPs when they were applied were also debated. Taking into account the integration of ePS with other enterprise systems in the same organization, it was felt placing SDPs in a general context would be beneficial and sufficient. This work will bridge the gap between software practitioners and state-of-the-art research on software SDPs.

Model experiments for Czochralski crystal growth processes using inductive and resistive heating

The Czochralski (CZ) growth technique is widely applied in crystal growth, using both induction and resistance heaters. In this work, a novel model experiment platform with comprehensive in-situ measurement capability is introduced. Growth experiments with the model material tin applying both heating concepts are performed and analyzed, e.g., in terms of the maximum achievable crystal diameter. Strong asymmetries in the magnetic field of the induction heater are measured and temperature distribution on the resistance heater is found to be non-uniform. Furthermore, significant losses are observed in the power supplies of the resistance heater. The heating efficiency of both concepts is compared considering different insulation geometries. The obtained results show the capability of model experiments for design optimization and will provide valuable input for further validation of numerical simulations.

Web Security: Emerging Threats and Defense

Web applications have become a widely accepted method to support the internet for the past decade. Since they have been successfully installed in the business activities and there is a requirement of advanced functionalities, the configuration is growing and becoming more complicated. The growing demand and complexity also make these web applications a preferred target for intruders on the internet. Even with the support of security specialists, they remain highly problematic for the complexity of penetration and code reviewing methods. It requires considering different testing patterns in both codes reviewing and penetration testing. As a result, the number of hacked websites is increasing day by day. Most of these vulnerabilities also occur due to incorrect input validation and lack of result validation for lousy programming practices or coding errors. Vulnerability scanners for web applications can detect a few vulnerabilities in a dynamic approach. These are quite easy to use; however, these often miss out on some of the unique critical vulnerabilities in a different and static approach. Although these are time-consuming, they can find complex vulnerabilities and improve developer knowledge in coding and best practices. Many scanners choose both dynamic and static approaches, and the developers can select them based on their requirements and conditions. This research explores and provides details of SQL injection, operating system command injection, path traversal, and cross-site scripting vulnerabilities through dynamic and static approaches. It also examines various security measures in web applications and selected five tools based on their features for scanning PHP, and JAVA code focuses on SQL injection, cross-site scripting, Path Traversal, operating system command. Moreover, this research discusses the approach of a cyber-security tester or a security developer finding out vulnerabilities through dynamic and static approaches using manual and automated web vulnerability scanners.

Penerapan Keamanan Owasp Terhadap Aplikasi GTFW Pada Website Universitas Battuta

In this study, we will test the penetration of GTFW-based applications that have been carried out, then obtain the results as a session management category vulnerability test, namely 3 that did not pass, namely cookie attributes, CSRF, and session timeout, then the vulnerability of the input validation category there was 1 that did not pass, namely stored XSS. The xplorin application has a cookie attribute vulnerability with a Medium vulnerability risk level. The xplorin application has a CSRF vulnerability with a Medium vulnerability risk level. The xplorin application has a session timeout vulnerability with a Low vulnerability risk level. The xplorin application has a stored XSS vulnerability with a Low risk level of vulnerability.