Header Files Research Articles

An OpenSees Surrogate Constitutive Model for High-Damping Rubber Based on Machine Learning.

The complex mechanical properties of high-damping rubber (HDR), a commonly used seismic isolation material in buildings and bridges, present a significant challenge in civil engineering. In a previous study, the authors proposed an HDR constitutive model that combines a Gated Recurrent Unit (GRU) and an attention mechanism, offering novel insights into the mechanical properties of HDR. The constitutive model was simplified first to facilitate the deployment of the proposed constitutive model within the finite element analysis environment. Then, the simplified constitutive model was converted into a uniaxial material format suitable for use within the open system for earthquake engineering simulation (OpenSees). In OpenSees, the uniaxial material was named HDRGA material, and the code for the HDRGA material header and source files was written. Finally, an HDR surrogate constitutive model was developed in OpenSees. To validate the precision of the HDRGA material in characterizing the mechanical attributes of HDR, a two-node model and a single-pier model were devised, and their accuracy was verified through a comparative analysis of test results and nonlinear time history calculation results, respectively. The results demonstrate that the developed HDRGA material is capable of performing well under earthquakes.

An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

A Robust CNN for Malware Classification against Executable Adversarial Attack

Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP), and spatial pyramid pooling (SPP), are proposed. Second, we designed an executable adversarial attack to construct adversarial malware by changing the meaningless and unimportant segments within the Portable Executable (PE) header file. Finally, to consolidate the GMP-based CNN, a header-aware loss algorithm based on the attention mechanism is proposed to defend the executive adversarial attack. The experiments showed that the GMP-based CNN achieved better performance in malware detection than other CNNs with around 98.61% accuracy. However, all CNNs were vulnerable to the executable adversarial attack and a fast gradient-based attack with a 46.34% and 34.65% accuracy decline on average, respectively. Meanwhile, the improved header-aware CNN achieved the best performance with an evasion ratio of less than 5.0%.

Traffic spills the beans: A robust video identification attack against YouTube

Traffic-based video identification attack is a side-channel attack that allows an attacker to identify transmitted online videos from encrypted network traffic. Existing attacks face two types of challenges: 1) YouTube's improved streaming technology renders them invalid or impacted; 2) real-world diverse traffic makes them underperform. In this paper, we provide the first study of YouTube's technical improvements and reveal its three key features: i) the mechanism that replaces the MPD file, ii) variable combined segments, and iii) various buffering processes. Based on the above analysis, we propose a Robust and easily scalable Video Identification Attack against YouTube called RoVIA. RoVIA extracts video fingerprints from video file headers and reference sequences from video traffic. RoVIA uses the reference sequence to generate candidate sequences in the pre-built video fingerprint library and achieves video identification by measuring sequence similarity. Experimental results show that RoVIA achieves open-world accuracy of 0.967, 0.924, and 0.863 in open-world, fixed-quality, and poor network experiments. Furthermore, RoVIA's scalability allows the target classes to be adjusted by updating the fingerprint library without collecting new samples and retraining parameters. This study aims to demonstrate the vulnerabilities of YouTube streaming technology and the potential consequences of its malicious use. We also propose two YouTube-friendly countermeasures to help developers build effective defenses.

Forensic Operations for Recognizing SQLite Content (FORC): An Automated Forensic Tool for Efficient SQLite Evidence Extraction on Android Devices

Mobile forensics is crucial in reconstructing various everyday activities accomplished through mobile applications during an investigation. Manual analysis can be tedious, time-consuming, and error-prone. This study introduces an automated tool called Forensic Operations for Recognizing SQLite Content (FORC), specifically designed for Android, to extract Simple Query Language Table Database Lightweight (SQLite) evidence. SQLite is a library that serves as a container for mobile application data, employing a zero-configuration, serverless, self-contained, and transactional SQL database engine. While some SQLite files possess extensions such as .db, .db3, .sqlite, and .sqlit3, others have none. The lack of file extensions may result in missing evidence that could unveil the truth. The proposed tool utilizes both the file extensions and headers of the SQLite data to recognize and identify SQLite data generated or modified by a mobile application. The FORC tool’s capability was evaluated using the Chrome application as a case study, and a comparison between FORC and other tools was conducted. The results suggest that FORC significantly simplifies mobile forensic analysis.

Version 1.2.0 — pyfao56: FAO-56 evapotranspiration in Python

The pyfao56 software package is a Python-based implementation of the standardized evapotranspiration (ET) methodologies described in Irrigation and Drainage paper No. 56 of the Food and Agriculture Organization of the United Nations, commonly known as FAO-56. This update improved pyfao56 by (1) enhancing the “forecast” module which obtains seven-day weather forecasts from the National Weather Service (NWS), (2) incorporating measured soil water content profiles to estimate soil water depletion in the dynamic and maximum root zones, (3) adding visualization tools that plot measured and simulated soil water depletion and ET versus time, (4) updating the format of standard file headers to include timestamps, simulation options, and user defined metadata, and (5) incorporating minor improvements to the functionality of the core pyfao56 modules. These updates arose from efforts to incorporate pyfao56 into a computational workflow for maize irrigation scheduling research at the Limited Irrigation Research Farm (LIRF) in Greeley, Colorado during the 2023 growing season.

Portobello - Quantum embedding in correlated materials made accessible

We present a platform for quantum embedding methods, Portobello, which facilitates efficient software development and ease of use by assimilating legacy electronic structure code into the modern software ecosystem and abstracting quantum embedding methods and their common operations (like projection and embedding). DFT+Gutzwiller (DFT+G) and DFT+DMFT are abstracted and then implemented within the framework as a proof of concept, and we discuss how their combination enables more predictive DFT+DMFT, more efficient material design, and the exploration of regimes outside the domain of one method but within the domain of another. The platform, its architecture and capabilities, and useful software paradigms and patterns which are uncommon in physics codes are presented here. Finally, DFT+G and DFT+DMFT calculations of NiO are presented to demonstrate how combining multiple quantum embedding methods empowers analysis. Program summaryProgram Title: PortobelloCPC Library link to program files:https://doi.org/10.17632/5p4nggbktd.1Developer's repository link:https://www.physics.rutgers.edu/~adler/portobello.htmlLicensing provisions: GPLv3Programming language: Python, Fortran, C++, CUDANature of problem: Abstracting quantum embedding methods and integrating them with legacy (Fortran) code in order to develop a flexible and maintainable modern platform for strongly correlated electron calculation is difficult, due to the structure of legacy code, the barriers between modern programming languages and Fortran, and the need for high-level abstractions which are best supported with modern languages and tools.Solution method: A metadata repository of C++ header files (schema) is established which defines the complex data structures upon which the abstractions in Portobello are built. An automatic code-generator translates this schemas into Fortran and Python structures which may be stored or loaded from file or memory seamlessly and in either language.Additional comments including restrictions and unusual features: DFT+G and DFT+DMFT are implemented seamlessly to facilitate use, comparison, and extension. Symmetry analysis, including symmetry adapted bases, is available for projection. Multiple (inequivalent) impurities and sub-shell calculations (e.g., one can project onto just the eg orbitals of a correlated d-shell) are supported. Optics, ARPES, thermoelectric power, free energies are implemented.

Improving ransomware detection based on portable executable header using xception convolutional neural network

All malware are harmful to computer systems; however, crypto-ransomware specifically leads to irreparable data loss and causes substantial economic prejudice. Ransomware attacks increased significantly during the COVID-19 pandemic, and because of its high profitability, this growth will likely persist. To respond to these attacks, we apply static analysis to detect ransomware by converting Portable Executable (PE) header files into color images in a sequential vector pattern and classifying these via Xception Convolutional Neural Network (CNN) model without transfer learning, which we call Xception ColSeq. This approach simplifies feature extraction, reduces processing load, and is more resilient against evasion techniques and ransomware evolution. The proposed method was evaluated using two datasets. The first contains 1000 ransomware and 1000 benign applications, on which the model achieved an accuracy of 93.73%, precision of 92.95%, recall of 94.64%, and F-measure of 93.75%. The second dataset, which we created and have made available, contains 1023 ransomware, grouped in 25 still active and relevant families, and 1134 benign applications, on which the proposed method achieved an accuracy of 98.20%, precision of 97.50%, recall of 98.76%, and F-measure of 98.12%. Furthermore, we refined a testing methodology for a particular case of zero-day ransomware attacks detection—the detection of new ransomware families—by adding an adequate amount of randomly selected benign applications to the test set, providing representative evaluation performance metrics. These results represent an improvement over the performance of the current methods reported in the literature. Our advantageous approach can be applied as a technique for ransomware detection to protect computer systems from cyber threats.

The Satellite Images Matching and Mosaic Techniques

The Matching and Mosaic of the satellite imagery play an essential role in many remote sensing and image processing projects. These techniques must be required in a particular step in the project, such as remotely change detection applications and the study of large regions of interest. The matching and mosaic methods depend on many image parameters such as pixel values in the two or more images, projection system associated with the header files, and spatial resolutions, where many of these methods construct the matching and mosaic manually. In this research, georeference techniques were used to overcome the image matching task in semi automotive method. The decision about the quality of the technique can be considered if the error value is less than half a pixel. The projection-based method was used to ensure the mosaic process. The test images are satellite imagery with medium spatial resolutions; these images were processed to ensure the results. In matching techniques, the different sensor images (different in resolutions) were investigated using image resize and sampling. The results were obtained using many remote sensing packages and written programs in Matlab environmental.

Swarm Optimization and Machine Learning Applied to PE Malware Detection towards Cyber Threat Intelligence

Cyber threat intelligence includes analysis of applications and their metadata for potential threats. Static malware detection of Windows executable files can be done through the analysis of Portable Executable (PE) application file headers. Benchmark datasets are available with PE file attributes; however, there is scope for updating the data and also to research novel attribute reduction and performance improvement algorithms. The existing benchmark dataset contains non-PE header attributes, and few ignored attributes. In this work, a critical analysis was conducted to develop a new dataset called SOMLAP (Swarm Optimization and Machine Learning Applied to PE Malware Detection) with a value addition to the existing benchmark dataset. The SOMLAP data contains 51,409 samples that include both benign and malware files, with a total of 108 pure PE file header attributes. Further research was carried out to improve the performance of the Malware Detection System (MDS) by feature minimization using swarm optimization tools, viz., Ant Colony Optimization (ACO), Cuckoo Search Optimization (CSO), and Grey Wolf Optimization (GWO) wrapped with machine learning tools. The dataset was evaluated, and an accuracy of 99.37% with an optimized set of 12 features (ACO) proves the efficiency of the dataset, its attributes, and the algorithms used.

Entropy Based Method for Malicious File Detection

Ransomware is by no means a recent invention, having existed as far back as 1989, yet it still poses a real threat in the 21st century. Given the increasing number of computer users in recent years, this threat will only continue to grow, affecting more victims as well as increasing the losses incurred towards the people and organizations impacted in a successful attack. In most cases, the only remaining courses of action open to victims of such attacks were the following: either pay the ransom or lose their data. One commonly shared behavior by all crypto ransomware strains is that there will be attempts to encrypt the victims’ files at a certain point during the ransomware execution. This paper demonstrates a technique that can identify when these encrypted files are being generated and is independent of the strain of the ransomware. Previous research has highlighted the difficulty in differentiating between compressed and encrypted files using Shannon entropy, as both file types exhibit similar values. Among the experiments described in this study, one showed a unique characteristic for the Shannon entropy of encrypted file header fragments, which was used to differentiate between encrypted files and other high entropy files such as archives. The Shannon entropy of encrypted file header fragments has a unique characteristic in one of the tests discussed in this study. This property was used to distinguish encrypted files from other files with high entropy, such as archives. To overcome this drawback, this study proposed an approach for test case generation by enhancing the entropy-based threat tree model, which would improve malicious file identification. The file identification was enhanced by combining three entropy algorithms, and the test case was generated based on the threat tree model. This approach was then evaluated using accuracy measurements: True Positive, True Negative, False Positive, False Negative. A promising result is expected. This method solves the challenge of leveraging file entropy to distinguish compressed and archived files from ransomware-encrypted files in a timely manner.

Circuit Design of Counting Electronic Scale Based on STC89C51 Single Chip Microcomputer

The main purpose of electronic scales is to measure, which can also be said to be for trading. Electronic scales have their relevant numerical measurement value restrictions when they are used. Therefore, they can only be used effectively within the measurement value range when they are used. As long as the weight does not exceed the measurement limit, they can be used at will. This measurement method greatly improves the measurement speed and the user's trading speed. STC89C51 single chip microcomputer as an 8-bit single chip microcomputer, first of all, the price is relatively low. The number of pins of 51 single chip microcomputer is up to 40. After subtracting some power supplies and special pins, 32 pins can be used, and all 32 pins can be used as input and output pins. Therefore, the performance price ratio of 51 single chip microcomputer is relatively high. At the same time, the application circuit of 51 single chip microcomputer is relatively simple, and only the crystal oscillator circuit and reset circuit are needed to achieve work. Besides, 51 single chip microcomputer also has serial communication, timer, external interrupt and other on-chip resources, which can meet the special requirements of some systems. Finally, in terms of programming, the 51 single-chip microcomputer can be used only after loading the header file, without configuring the input and output ports, and is also very convenient in programming. Therefore, STC89C51 single chip microcomputer is used as the basis and Proteus and KILL software are used to realize the design of electronic scales.

Static Analysis and Machine Learning-based Malware Detection System using PE Header Feature Values

Advances in information and communications technology (ICT) are improving daily convenience and productivity, but new malware threats continue to surge. This paper proposes a malware detection system using various machine learning algorithms and portable executable (PE) Header file static analysis method for malware code, which has recently changed in various forms. Methods/Statistical analysis: This paper proposes a malware detection method that quickly and accurately detects new malware using static file analysis and stacking methods. Furthermore, using information from PE headers extracted through static analysis can detect malware without executing real malware. The features of the pe_packer used in the proposed research method were most efficient in experiments because the extracted data were processed in various ways and applied to machine learning models. So, we chose pe_packer information as the shape data to be used for the stacking model. Detection models are implemented based on additive techniques rather than single models to detect with high accuracy. Findings: The proposed detection system can quickly and accurately classify malware or ordinary files. Moreover, experiments showed that the proposed method has a 95.2% malware detection rate and outperforms existing single model-based detection systems. Improvements/Applications: The proposed research method applies to detecting large new strains of malware.

Comparison study of hardware architectures performance between FPGA and DSP processors for implementing digital signal processing algorithms: Application of FIR digital filter

The objective of this project is to make a detailed study on the Hardware architectures of DSP and FPGA then to carry out a comparison between the two platforms in order to achieve a better implementation while keeping to increase the performances and obtain a better signal processing quality. For this raison, we wanted to make a digital FIR filter of 40 order bandpass and a symmetrical parallel architecture of this FIR filter. Matlab's FDATOOL is used to design this filter. This tool allows us to generate the VHDL code of the filter to implement it on Altera Cyclone III FPGA which is placed on the Texas Instruments TSW6011 board, this tool also allows us to generate all the coefficients of our filter in a header file C in order to use them in a program of the FIR filter written in C which will then be loaded on the DSP TMS320C6713. This achievement allows us to distinguish between different hardware architectures and make a comparison in terms of execution speed and power consumption.

Creating a General-Purpose Procedural Language for Programming in Kannada

Abstract: To build a general-purpose procedural programming language in Kannada and the corresponding header file that understands the language and pre-processes the code typed in Kannada into C++ programming language which can then be compiled by a C++ compiler and produce corresponding output in Kannada. The entire compilation of the language will be built in a single and Integrated Development Environment (IDE) i.e. Microsoft Visual Studio which can be downloaded by anyone and start learning to code in Kannada by including the header file. Any programming logic can be typed entirely in Kannada, the syntax is created such that it is similar to C-syntax. Any person knowing Kannada and with little effort on understanding the language’s syntax can start coding in Kannada without any prior knowledge in English or programming. This paper aims mainly at providing its users a programming experience in a regional language “Kannada”. The users can edit, complier and thus observe the results also in Kannada. The user has to abide by a set of syntactic rules and thus program accordingly for better results.

A Reliable JPEG Quantization Table Estimator

JPEG compression is a commonly used method of lossy compression for digital images. The degree of compression can be adjusted by the choice of a quality factor QF. Each software associates this value to a quantization table, which is an 8 x 8 matrix used to quantize the DCT coefficients of an image. We propose a method for recovering the JPEG quantization table relying only on the image information, without any metadata from the file header; thus the proposed method can be applied to an uncompressed image format to detect a previous JPEG compression. A statistical validation is used to decide whether significant quantization traces are found or not, and to provide a quantitative measure of the confidence on the detection.

Design of Chained Document HTML Generation Technique Based on Blockchain for Trusted Document Communication

Digital document communication between an enterprise and a customer is becoming a primary form of communication rather than the traditional physical document communication. A PDF document, the most popular document format, provides an identical document layout regardless of OS or device and has a content integrity verification feature with a digital signature. However, it has a bad user experience, such as low readability on a mobile device. On the other hand, an HTML document has a weakness in verifying the content integrity even though it is the primary document format and provides a good user experience on mobile devices. There are certified document services using blockchain technology, but it is still vulnerable to verifying content integrity. Furthermore, research on the document HTML has proposed the trusted document generation technique by HTML conformance and digital signature; however, this research does not provide content delivery verification, and there is a file size overhead. In this paper, we have developed the chained document HTML by defining HTML conformance, digital signature, and blockchain technology. First, the chained document HTML has to embed all resources and does not allow loading content on-demand. Second, the file is signed by a digital signature, and the signature value is added in the file header. Lastly, the metadata to verify the content integrity is inserted in a blockchain node. We have created the chained document HTML generation and verification experiment environment by Ethereum and Python. We have confirmed that the chained document HTML provides content and delivery integrity verification in the research. We expect the chained document HTML will be widely used in document communication between an enterprise and a customer, especially if the document has sensitive personal information that might have a legal dispute.

Forensic Recovery of File System Metadata for Digital Forensic Investigation

File system forensics is one of the most important elements in digital forensic investigations. To date, various file system forensic methods, such as analysis of tree structure and the recovery of deleted file data, have been studied. Among these file system forensic methods, the recovery of file system metadata is a key technique that makes digital forensic investigations possible by recovering metadata when it is not possible to obtain metadata in a regular manner because the file system structure is damaged due to an accident/disaster or cyber terrorism. Previous studies mainly focused on recovering record or entry data, which are the basic units of metadata, using carving techniques via a fixed values or values capable of range prediction at the beginning of the data. However, no studies have been conducted on metadata without such fixed values or values capable of range prediction. $LogFile, which is a metadata file of the New Technology File System (NTFS) that is one of the most used file systems at present, contains very important metadata that provide a history of all file system operations during a specific period. However, since there is no fixed value or a value capable of range prediction at the start position of the record, which is the basic unit of $LogFile, there have been no studies on recovery using record units, and only recovery by file and page have been possible. If the file header or page header of $LogFile is damaged, existing recovery methods cannot properly recover the metadata; in such cases, a record-level recovery method is required to recover the metadata. In this context, we investigated the mechanisms of record storage through a detailed analysis of the $LogFile structure and proposed a recovery method for records without fixed values. Our proposed method was implemented as a tool and verified through comparative experiments with existing forensic tools that recover $LogFile data. The experimental results showed that the proposed recovery method was able recover all the data that existing tools are unable to recover in situations where the $LogFile data were damaged. The implemented tools are released free of charge to contribute digital forensic community. Finally, we explained what important role $LogFile played in solving real-world cases and confirm the importance of recovering $LogFile data in situations where file systems may be damaged due to accidents and disasters.

Cardiac Disease Prediction using Supervised Machine Learning Techniques.

Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.

The Teaching Analysis and Design of the Construction of C Language Multi-Document Project Based on Keil

The establishment of C language multi-file engineering is a difficult point to deepen the learning of C language, and is also the basis for learning STM32 programming. This article uses the project creation process of 8 LED lights as an example to establish the source program files and header files of each function and hardware. Guide students from the modularization of functions to the modularization of source files, teaching analysis and design of the establishment of multi-file engineering, so that more students can deeply understand and master the creation of multi-file engineering, laying a solid foundation for the deepening of embedded learning.