Local File Research Articles

Security Analysis of Web Open-Source Projects Based on Java and PHP

During website development, the selection of suitable computer language and reasonable use of relevant open-source projects is imperative. Although the two languages, PHP and Java, have been extensively investigated in this context, there are not many security test reports based on their open-source projects. In this article, we conducted separate security analyses on web-related open-source projects based on PHP and Java. To this end, different open-source frameworks and services are used to design websites used to test experimental attacks on 12 popular open-source filters available on GitHub, as well as investigate the use of Lightweight Directory Access Protocol (LDAP) in the Firefox browser environment. Using malicious payloads published by Open Web Application Security Project (OWASP) and others, Cross-site Scripting (XSS), Local File Inclusion (LFI), SQL injection, and LDAP injection are performed on the test targets. The experimental results reveal that although PHP-based open-source projects are more vulnerable to attacks than Java-based ones, there is significant room for improvement. Finally, a whitelist-based filtering scheme is proposed. This scheme filters the inline attributes of label elements so that the filter has an excellent detection rate of malicious payloads while having an excellent pass rate of benign payloads. Effective references and suggestions for web developers are also included to aid the selection of open-source web projects, and feasible solutions to improve filter performance are proposed.

Android-Based Audio Video Navigation System Forensics: A Case Study

Vehicle digital forensics includes the process of collecting and analysing digital data stored in vehicles to find evidence related to traffic accidents or crime scenes. Through this process, we can reconstruct digital events by recognizing various information such as driver behaviour, driving patterns, vehicle destinations, and smartphones connected to a vehicle. Recently, many vehicle digital forensic studies have been conducted, but few of them have dealt with Android-based infotainment (or audio video navigation, AVN) systems. While many AVN systems adopt Android as the operating system, digital forensics of Android-based AVN systems is not easy. This is because Android-based AVN systems support various storage devices and data formats and have various data sources, making consistent data collection or analysis difficult. In this article, we perform digital forensics on four AVN systems: two Android 4.2.2 Jellybean-based and two Android 4.4.2 KitKat-based, aiming to develop a data acquisition and analysis method appropriate for each Android version. As a data collection method for the AVN system forensics, logical data acquisition is performed on the Jellybean-based systems and physical data acquisition on the KitKat-based systems. For the collected data, we identify and analyse Bluetooth data, navigation data, and system logs individually. Next, we investigate the differences in the storage structure and file location of major digital artefacts depending on the Android versions and show the limitations of the individual data analysis. Finally, we construct a timeline for the driver’s activities by integrating and analysing the diverse artefacts which consist of Bluetooth data, navigation data, and system logs.

An Active File Mode Transition Mechanism Based on Directory Activation Ratio in File Synchronization Service

In this paper, we propose an active file mode change mechanism in which the file synchronization system of cloud storage automatically changes files in a directory of a client to the online or local mode by considering tradeoff between local storage usage and file access time according to directory activation ratio. When the directory activation ratio rises above a certain threshold, the proposed active file mode change mechanism selects online mode files in this directory based on file access delay time and local storage usage and changes them to the local mode to reduce file access delay of active IoT clients. When the directory activation ratio falls below the threshold, the active file mode change mechanism selects the local mode files based on the last access time and local storage usage and changes them to the online mode to increase available local storage. Experimental results show that the proposed active file mode change mechanism can control when and how much the client can reduce and increase the local storage usage and the file access delay by changing file mode parameters according to the requirements of various IoT devices.

UK Consultation on Transfer Pricing Documentation

The OECD Local File and Master File format is about to become compulsory in the United Kingdom for approximately 3,500 businesses that are members of the largest groups. Such businesses will also be required to complete a “summary audit trail” questionnaire, though this will come into effect later, to allow for further consultation on this.

FastqToGenCounts: Converting Fastq Files to Gene Counts Matrices

Abstract FastqToGeneCounts is a computational pipeline built on Snakemake to process and analyze bulk RNA sequencing data to determine gene expression. It can handle raw data from the Gene Expression Omnibus or local FastQ files and runs on a supercomputing cluster. The primary function of FastqToGeneCounts is to align FastQ files to a reference genome. To ensure accurate outputs, it also offers trimming, quality control, and contaminant screening options. One of the benefits of using FastqToGeneCounts is that it addresses the issues commonly encountered with traditional alignment workflows. These include the need for manual file naming and directory setup, which can lead to errors and problems resuming failed workflows. Additionally, these workflows often require many resources requested at once, leading to long “wait times” for cluster access. Modifying parameters can also be challenging, as most settings are not localized to a single file. In contrast, FastqToGeneCounts integrates with Snakemake to improve resume-ability, minimize resource usage, and provide easy parameter modification. Jobs can be resumed from a failed state, only the minimum required resources are requested to reduce waiting time, and a single YAML file can be used to configure parameters within the pipeline. FastqToGeneCounts has been used to analyze four immature natural killer datasets and has successfully determined gene expression in these samples. It is capable of analyzing a single raw RNA-seq input file in approximately 15 compute minutes, and additional files do not increase runtime due to Snakemake’s high interoperability with SLURM. FastqToGeneCounts removes poor-quality reads from downstream analysis and presents results in an easy-to-read report.

Reproducibility as a service

AbstractRecent studies demonstrated that the reproducibility of previously published computational experiments is inadequate. Many of these published computational experiments never recorded or preserved their computational environment, including packages installed in the language, libraries installed on the host system, and file locations. Researchers have created reproducibility tools to help mitigate this problem, but these tools assume the experiment currently executes. Thus, these tools do not facilitate reproducibility of the large number of published experiments. This situation is not improving; researchers continue to publish without using reproducibility tools. We define a framework to distinguish between actions taken by a researcher to facilitate reproducibility in the presence of a computational environment and actions taken by a researcher to enable reproduction of an experiment when that environment has been lost to clarify the gap between what existing reproducibility tools are capable of and what is required to reproduce published experiments. The difference between these approaches lies in the availability of a computational environment. Researchers that provide access to the original computational environment perform proactive reproducibility, while those who do not enable only retroactive reproducibility. We present Reproducibility as a Service (RaaS), which is, to the best of our knowledge, the first reproducibility tool explicitly designed to facilitate retroactive reproducibility. We demonstrate how RaaS fixes many common errors found in R scripts on Harvard's Dataverse and preserves a recreated computational environment. Finally, we discuss how a retroactive reproducibility service such as RaaS is also helpful as an ‘artifact evaluation assistant’ in a journal's publication pipeline.

Degradation reduces greenhouse gas emissions while weakening ecosystem carbon sequestration of Moso bamboo forests

Moso bamboo (Phyllostachys heterocycla cv. Pubescens) is well known for its high capacity to sequester atmospheric carbon, which has a unique role to play in combating global warming. Many Moso bamboo forests are gradually degrading due to rising labor costs and falling prices for bamboo timber. However, the mechanisms of carbon sequestration of Moso bamboo forest ecosystems in response to degradation are unclear. In this study, a space-for-time substitution approach was used to select Moso bamboo forest plots with the same origin and similar stand types, but different years of degradation, and four degradation sequences, continuous management (CK), 2 years of degradation (D-I), 6 years of degradation (D-II) and 10 years of degradation (D-III). A total of 16 survey sample plots were established based on the local management history files. After a 12-month monitoring, the response characteristics of soil greenhouse gases (GHG) emissions, vegetation, and soil organic carbon sequestration in different degradation sequences were evaluated to reveal the differences in the ecosystem carbon sequestration. The results indicated that under D-I, D-II, and D-III, the global warming potential (GWP) of soil GHG emissions decreased by 10.84 %, 17.75 %, and 31.02 %, while soil organic carbon (SOC) sequestration increased by 2.82 %, 18.11 %, and 4.68 %, and vegetation carbon sequestration decreased by 17.30 %, 33.49 %, and 44.76 %, respectively. In conclusion, compared to CK, the ecosystem carbon sequestration was reduced by 13.79 %, 22.42 %, and 30.31 %, respectively. This suggests that degradation reduces soil GHG emissions but weakens the ecosystem carbon sequestration capability. Therefore, in the background of global warming and the strategic goal of carbon neutrality, restorative management of degraded Moso bamboo forests is critically needed to improve the carbon sequestration potential of the ecosystem.

클라우드 스토리지 시스템에서 능동적 파일 모드 변경을 통한 동기화 성능 분석

In this paper, we propose an active file mode change mechanism that actively changes the file mode of the files in the directory to online or local mode according to the activation ratio of the client directory using cloud storage. When the directory activation ratio rises above a certain threshold, the proposed active file mode change mechanism selects online mode files and changes them to the local mode. When the directory activation ratio falls below the threshold, the proposed mechanism selects the local mode files and changes them to the online mode. In order to analyze the performance considering the trade-off relationship between local storage usage and file access latency of local and online mode files, we define file mode performance index and analyzes the performance of the active file mode change mechanism through experiments. As a result of the experiment, the proposed mechanism improved performance compared to the existing manual file mode change method.

Going fast on a small-size computing cluster

Fast turnaround times for LHC physics analyses are essential for scientific success. The ability to quickly perform optimizations and consolidation studies is critical. At the same time, computing demands and complexities are rising with the upcoming data taking periods and new technologies, such as deep learning. We present a show-case of the HH→bbWW analysis at the CMS experiment, where we process \U0001d4aa(1 − 10)TB of data on 100 threads in a few hours. This analysis is based on the columnar NanoAOD data format, makes use of the NumPy ecosystem and HEP specific tools, in particular Coffea and Dask. Data locality, especially IO latency, is optimized by employing a multi-level caching structure using local file storage and on-worker SSD caches. We process thousands of events simultaneously within a single thread, thus enabling straightforward use of vectorized operations. Resource intensive computing tasks, such as GPU accelerated DNN inference and histogram aggregation in the \U0001d4aa(10)GB regime, are offloaded to dedicated workers. The analysis consists of hundreds of distinctly different workloads and is steered through a workflow management tool ensuring reproducibility throughout the development process up to journal publication.

Data Processing and Management at PMEL: A 50-Year Perspective

Over the last 50 years, the landscape of marine data management has been transformed. Previously, each research project held its data privately and managed them as local files on disk; today, it is standard practice to share data collaboratively over the internet, often integrated with web tools that provide a global community of scientists with ready access to data analysis and visualization. NOAA Pacific Marine Environmental Laboratory (PMEL) developers and data managers have made and continue to make pivotal contributions toward this evolution. This article examines contributions that include a community-wide standard for metadata storage (e.g., climate and forecast [CF] metadata conventions), a widely used desktop computer tool (PyFerret), a pioneering web server providing visualization and analysis of distributed data (Live Access Server), tailor-made data management systems for uncrewed ocean platforms, and new developments in applications of machine learning to data quality control. We also describe the evolution of in-house PMEL data management, from PMEL developed tools to an open-science, interoperable data approach.

DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction

Defect prediction is proposed to assist practitioners effectively prioritize limited Software Quality Assurance (SQA) resources on the most risky files that are likely to have post-release software defects. However, there exist two main limitations in prior studies: (1) the granularity levels of defect predictions are still coarse-grained and (2) the surrounding tokens and surrounding lines have not yet been fully utilized. In this paper, we perform a survey study to better understand how practitioners perform code inspection in modern code review process, and their perception on a line-level defect prediction. According to the responses from 36 practitioners, we found that 50% of them spent at least 10 minutes to more than one hour to review a single file, while 64% of them still perceived that code inspection activity is challenging to extremely challenging. In addition, 64% of the respondents perceived that a line-level defect prediction tool would potentially be helpful in identifying defective lines. Motivated by the practitioners’ perspective, we present DeepLineDP, a deep learning approach to automatically learn the semantic properties of the surrounding tokens and lines in order to identify defective files and defective lines. Through a case study of 32 releases of 9 software projects, we find that the risk score of code tokens varies greatly depending on their location. Our DeepLineDP is 17%-37% more accurate than other file-level defect prediction approaches; is 47%-250% more cost-effective than other line-level defect prediction approaches; and achieves a reasonable performance when transferred to other software projects. These findings confirm that the surrounding tokens and surrounding lines should be considered to identify the fine-grained locations of defective files (i.e., defective lines).

Исследование статистики развития нейронных сетей и их роли в сфере видеопроизводств

The development of technology these days is amazing in its rapidity. Computer and network technologies have come a long way from local file sharing services to a global repository that absorbs the knowledge of many generations of humanity. However, now a new milestone in technology development is gaining momentum - artificial neural networks, which are something different than the prototypes of artificial intelligence shown to us in popular culture - films, TV series and books. These are assistants that are developing every day, capable of performing a wide range of tasks assigned by a person. Neural networks are becoming more and more rooted in our lives, and we use them without even realizing that they are branches of artificial intelligence. The list of them is quite large, starting from voice assistants aimed at quickly searching for information on the Internet. Due to competition in the technology market, almost every large IT company owns its own voice assistant, which is distributed on devices produced by the companies - personal computers, mobile phones and other smart devices. Among the most influential, it is worth noting the voice assistants ―Alice‖, developed by Yandex, ―Cortana‖ - developed by Microsoft, and ―Siri‖ - a voice assistant for devices created by Apple. Also, in addition to voice assistants that recognize voice commands, there are neural network generators that have found their niche in various branches of modern Internet media, from generating text to creating original images based on user requests. Video content is one of the fundamental pillars of media culture around the world and to this day, the majority of the population finds it preferable due to receiving more information in less time. However, now in view of the development of neural network technologies, the question arises: will actively learning neural networks replace full-fledged video production workers? The article will conduct a study of statistics on the use of neural networks for working with media content, and will also answer the question posed above.

Implementation of ID-based Audit Protocols to Enhance Security and Productivity

Cloud storage has gained increasing popularity, as it helps cloud users arbitrarily store and access the related outsourced data. Numerous public audit buildings have been presented to ensure data transparency. However, modern developments have mostly been constructed on the public key infrastructure. To achieve data integrity, the auditor must first authenticate the legality of the public key certificate, which adds to an immense workload for the auditor, in order to ensure that data integrity is accomplished. The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server. One of the main problems for individuals, though, is how to detect data integrity on a term where people have a backup of local files. Meanwhile, a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved. In this work, a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols. The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity. The idea has already been proved safe; its safety is very relevant to the traditional presumption of the Computational Diffie–Hellman security assumption.

Development of a method for processing log files using clustering

A log file is a document that keeps track of all events that occur on a website or server. Many log files are very large, so they can be regularly written over outdated content, or entire collections of log files with names, including a date, for example, can be created. In the event of technical problems, site inaccessibility, virus infection, hacker attacks and Distributed Denial of Service (DDoS) attacks, the resource administrator can use the information in log to find the cause, which makes it easier and faster to eliminate unwanted incidents. The paper analyzes the definition, types, location, use and examples of log files. Data are transferred to the MySQL database using the Squid.db database. Clustering is performed using a database. The study highlights clustering, analyzes metrics, and determines the proximity of clusters and objects in clusters in Euclidean space. Experiments are conducted and the results are satisfactory. For example, data are transferred to the MySQL database using the Squid.db database. Since the Squid proxy server is a cache proxy server, it stores resources, and the work is done quickly on the next request. Data are clustered using a compiled table of databases transferred to MySQL via Squid proxy. In this case, unnecessary entries are deleted from the table, which significantly speeds up data processing. The application of clustering method in problem solving is fast and simple. For the problem stated, the degree of closeness of clusters and objects in clusters in Euclidean space is determined. Experiments are conducted using obtained results.

Contemporaneous sample data tracking for the generation of genome edited cell lines

It is difficult to capture the large numbers of steps and details that often characterize research in the biomedical sciences. We present an approach that is based on commercial spreadsheet software so it is easily adaptable by the experimentalist. The approach is designed to be compatible with an experimentalist's workflow and allows the capture in real time of detailed information associated, in this use case, with laboratory actions involved in the process of editing, enriching and isolating clonal gene-edited pluripotent stem cell (PSC) lines. Intuitive features and flexibility allow an experimentalist without extensive programming knowledge to modify spreadsheets in response to changes in protocols and to perform simple queries. The experimental details are collated in a table format from which they can be exported in open standard formats (e.g., Extensible Markup Language (XML) or Comma Separated Values (CSV) for ingestion into a data repository supporting interoperability with other applications. We demonstrate a sample- and file-naming convention that enables the automated creation of file directory folders with human readable semantic titles within a local file system. These operations facilitate the local organization of documentation and data for each cell line derived from each transfection in designated folder/file locations. This approach is generalizable to experimental applications beyond this use case.

An Introduction to Cordra

Cordra is a digital object server that can function as a key infrastructural piece in FAIR DO (findable, accessible, interoperable and reusable digital object) implementations. Cordra manages JSON records and payloads as typed digital objects identified by handles. Cordra is neither a database nor an indexer, but it integrates the two and provides a unified interface. Cordra is intended to support both quick prototyping as well as production systems. For prototyping, Cordra makes it easy to get up and running rapidly with a digital object server. A potential Cordra administrator can download Cordra and very quickly have a server which supports creation, search, and retrieval of digital objects with resolvable identifiers. The server supports Digital Object Interface Protocol (DOIP) and HTTP APIs out of the box, as well as an immediately usable prototype user interface. Cordra saves substantial development time as it comes with ready-made functionality ranging from user authentication and access control to information validation, enrichment, storing, and indexing. By default, Cordra is configured to store objects on the local file system of the machine and use embedded Apache Lucene for indexing. Simply by editing type definitions in Cordra's user interface, the administrator can start changing the behavior of the APIs and user interface in real time for experimentation, including adding custom operations. For production use, Cordra allows intensive extension and customization of the processes underlying the digital object server: how digital objects are stored and indexed, how they are validated and enriched, how users authenticate, when and to whom to give access to objects, and what custom operations can be performed. In production Cordra is run at scale, supporting high reliability and performance; among other options Cordra supports MongoDB and Amazon S3 for storage, and Elasticsearch and Apache Solr for indexing. By definition of the underlying types and operations, Cordra is intended to serve directly as the API backend for a production application. This talk will cover basic Cordra features as well as customization/configuration basics. Examples of current use will be shown, including the use of the Digital Object Interface Protocol (DOIP), for which Cordra serves as a reference implementation. Current users of Cordra include the Derivatives Service Bureau (DSB), which uses Cordra as part of its backend to manage the automated generation of International Securities Identification Numbers (ISINs) for OTC derivatives in the financial services sector; and the British Standard Institute (BSI) whose Identify service for construction product manufacturers aims to assign a Universal Persistent Identification Number (UPIN) "for every product that is specified and incorporated in a building structure". The DSB, Cordra users since 2017, has a production system with over 80 million identified digital objects which receives millions of searches each month. BSI.Identify has a system where Cordra's DOIP interface is directly accessible as the service's public API.

Malware Detection Method Based on File and Registry Operations Using Machine Learning

Malware (Malicious Software) is any software which performs malicious activities on computer-based systems without the user's consent. The number, severity, and complexity of malware have been increasing recently. The detection of malware becomes challenging because new malware variants are using obfuscation techniques to hide themselves from the malware detection systems. In this paper, a new behavioral-based malware detection method is proposed based on file-registry operations. When malware features are generated, only the operations which are performed on specific file and registry locations are considered. The file-registry operations divided into five groups: autostart file locations, temporary file locations, specific system file locations, autostart registry locations, and DLLs related registry locations. Based on the file-registry operations and where they performed, the malware features are generated. These features are seen in malware samples with high frequencies, while rarely seen in benign samples. The proposed method is tested on malware and benign samples in a virtual environment, and a dataset is created. Well-known machine learning algorithms including C4.5 (J48), RF (Random Forest), SLR (Simple Logistic Regression), AdaBoost (Adaptive Boosting), SMO (Sequential Minimal Optimization), and KNN (K-Nearest Neighbors) are used for classification. In the best case, we obtained 98.8% true positive rate, 0% false positive rate, 100% precision and 99.05% accuracy which is quite high when compared with leading methods in the literature.

Microservices Enabled E-Commerce Web Application

Abstract: Microservices are emerging as a new computing paradigm which is a suitable complementation of cloud computing. Microservices decompose traditional monolithic applications into a set of fine-grained services which can be independently developed, tested and deployed. In this work we have developed an e-commerce web application using microservices architecture. The drawbacks of monolithic architecture are overcome by microservices in building the e-commerce web application. The web application is developed using MERN stack technology. The services developed in the application are containerized using Docker containers. The containerized services are then managed using Kubernetes which is an orchestration platform. Skaffold is used in the application to sync local code files with the cold files in Docker. The events emitted by services are managed using an event bus called NATS streaming server.

Irrigant flow in the root canal during ultrasonic activation: A numerical fluid-structure interaction model and its validation.

AimThe aim of the study was (a) to develop a three‐dimensional numerical model combining the oscillation of a tapered ultrasonic file and the induced irrigant flow along with their two‐way interaction in the confinement of a root canal. (b) To validate this model through comparison with experiments and theoretical (analytical) solutions of the flow.MethodologyTwo partial numerical models, one for the oscillation of the ultrasonic file and another one for the irrigant flow inside the root canal around the file, were created and coupled in order to take into account the two‐way coupled fluid–structure interaction. Simulations were carried out for ultrasonic K‐files and for smooth wires driven at four different amplitudes in air or inside an irrigant‐filled straight root canal. The oscillation pattern of the K‐files was determined experimentally by Scanning Laser Vibrometry, and the flow pattern inside an artificial root canal was analysed using high‐speed imaging together with Particle Image Velocimetry. Analytical solutions were obtained from an earlier study. Numerical, experimental and analytical results were compared to assess the validity of the model.ResultsThe comparison of the oscillation amplitude and node location of the ultrasonic files and of the irrigant flow field showed a close agreement between the simulations, experiments and theoretical solutions.ConclusionsThe model is able to predict reliably the file oscillation and irrigant flow inside root canals during ultrasonic activation under similar conditions.

Power-Efficient Deep Neural Network Accelerator Minimizing Global Buffer Access without Data Transfer between Neighboring Multiplier—Accumulator Units

This paper presents a novel method for minimizing the power consumption of weight data movements required by a convolutional operation performed on a two-dimensional multiplier–accumulator (MAC) array of a deep neural-network accelerator. The proposed technique employs a local register file (LRF) at each MAC unit in a manner such that once weight pixels are read from the global buffer into the LRF, they are reused from the LRF as many times as desired instead of being repeatedly fetched from the global buffer in each convolutional operation. One of the most evident merits of the proposed method is that the procedure is completely free from the burden of data transfer between neighboring MAC units. It was found from our simulations that the proposed method provides a power saving of approximately 83.33% and 97.62% compared with the power savings recorded by the conventional methods, respectively, when the dimensions of the input data matrix and weight matrix are 128 × 128 and 5 × 5, respectively. The power savings increase as the dimensions of the input data matrix or weight matrix increase.