File Name Research Articles

CLASSIFICATION OF POTATO LEAF DISEASES USING CONVOLUTIONAL NEURAL NETWORK

Potatoes are an agricultural product that has the fourth highest content of wheat flour after corn, wheat, and rice. Although potatoes play a critical role in agriculture, this crop is susceptible to various diseases and pests. There are several potato leaf diseases that are not yet known to farmers. Dry spot potato leaf disease (late blight) and late blight. If not treated, this disease on potato leaves will spread to the stem and reduce crop yields, causing crop failure. By using technology in the form of digital image processing, this problem can be overcome. This research proposes an appropriate method for detecting disease in potato leaves. Classification will be carried out in three classes, namel, Early Blight, Healthy and Late Blight using the Deep Learning method of Convolutional Neural Network (CNN). The data used comes from an online dataset via the kaggle.com page with the file name Potato Disease Leaf Dataset (PLD) totaling 3251 training datasets which are then divided into training, testing, and validation. The processes carried out are image pre-processing, image augmentation, then image processing using a Convolutional Neural Network (CNN). In the classification process using the CNN method with RMSprop optimizer, the accuracy was 97.53% with a loss value of 0.1096.

Identification of data wiping tools based on deletion patterns in ReFS $Logfile

Data wiping tools permanently delete files by repeatedly overwriting data on a digital device, making file recovery impossible. Unlike the conventional deletion methods, which merely remove the file system pointer to the data, these tools are designed to entirely and irretrievably erase the data. This method can be exploited to obliterate evidence of a crime. Given the growing prevalence of such tools, a comprehensive analysis of permanent deletion behavior is essential, especially concerning the Resilient File System (ReFS). In this study, we propose a method for detecting user behavior concerning data wiping tools and algorithms in ReFS 3.7. Our approach relies on the fact that file modifications are logged in the redo record of the $Logfile, and that the opcode value of the redo record varies depending on the data wiping tool used. Since opcodes were only analyzed up to version 3.4, we analyzed the newly updated opcodes. Initially, we selected the 12 most commonly used data wiping tools for our research. In the pattern analysis phase, we applied the algorithms supported by each tool, generating a distinct deletion pattern for each one. This was accomplished by utilizing consecutive opcodes to formulate the patterns and monitor transitions in file and directory names. The patterns discerned in the $Logfile allowed us to determine which data wiping tool was deployed. The proposed methodology simplifies the identification of not only which data wiping tool has been used, but also the specific deletion behavior exhibited. We developed a tool incorporating the proposed method. Our subsequent verification confirmed the effectiveness of our methodology and tools in accurately detecting the use of comprehensive deletion tools. These findings contribute valuable insights to the acquisition of digital evidence of user deletion behavior in ReFS. Our proposed methodology will help digital forensic examiners in the detection and identification of data wiping tools' behavior.

Forensic Examination of Ceph

The concept of Software Defined Storage (SDS) has become very popular over the last few years. It is used in public, private, and hybrid clouds to store enterprise, private, and other kinds of data. Ceph is an open source software that implements an SDS stack. This article analyzes the data found on storage devices (Object Store Devices (OSDs)) used to store Ceph BlueStore data from a data forensics point of view. The Object Store Device (OSD) data is categorized using the model proposed by Carrier into the five categories file system, content, metadata, file name, and application category. It then describes how the different data can be connected to present useful information about the content of an OSD and presents the implementation of a forensic software tool for OSD analysis based on Ceph 12.2.4 luminous .

Short text classification approach to identify child sexual exploitation material

Producing or sharing Child Sexual Exploitation Material (CSEM) is a severe crime that Law Enforcement Agencies (LEAs) fight daily. When the LEA seizes a computer from a potential producer or consumer of the CSEM, it analyzes the storage devices of the suspect looking for evidence. Manual inspection of CSEM is time-consuming given the limited time available for Spanish police to use a search warrant. Our approach to speeding up the identification of CSEM-related files is to analyze only the file names and their absolute paths rather than their content. The main challenge lies in handling short and sparse texts that are deliberately distorted by file owners using obfuscated words and user-defined naming patterns. We present two approaches to CSEM identification. The first employs two independent classifiers, one for the file name and the other for the file path, and their outputs are then combined. Conversely, the second approach uses only the file name classifier to iterate over an absolute path. Both operate at the character n-gram level, whereas novel binary and orthographic features are presented to enrich the text representation. We benchmarked six classification models based on machine learning and convolutional neural networks. The proposed classifier has an F1 score of 0.988, which can be a promising tool for LEAs.

Using file and folder naming and structuring to improve automated detection of child sexual abuse images on the Dark Web

Increasing dissemination of child sexual abuse material (CSAM), especially on the Dark Web, has necessitated greater reliance on automated detection tools. These tools typically match images and videos to known CSAM databases, which is an ineffective method for identifying unknown CSAM. To identify potential complimentary methods, we analysed 162 unique known images, displayed 7289 times on 988 Dark Web websites, to determine if patterns in file/folder naming and structuring tendencies existed on websites. Overall, websites prioritised organisation (ease of access) over obfuscation (security) and hosted almost all images they displayed. File/folder names were commonly alphanumeric, however, there was evidence of sequence file naming patterns. Webpages displaying CSAM were explicitly named, often using underage and/or incest-related keywords. Structuring patterns revealed presence of website copies (mirrors) which can impede effective CSAM removal. Recommendations for supplementing automated detection techniques are discussed.

Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies

Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

UniDec Processing Pipeline for Rapid Analysis of Biotherapeutic Mass Spectrometry Data.

Recent advances in native mass spectrometry (MS) and denatured intact protein MS have made these techniques essential for biotherapeutic characterization. As MS analysis has increased in throughput and scale, new data analysis workflows are needed to provide rapid quantitation from large datasets. Here, we describe the UniDec processing pipeline (UPP) for the analysis of batched biotherapeutic intact MS data. UPP is built into the UniDec software package, which provides fast processing, deconvolution, and peak detection. The user and programming interfaces for UPP read a spreadsheet that contains the data file names, deconvolution parameters, and quantitation settings. After iterating through the spreadsheet and analyzing each file, it returns a spreadsheet of results and HTML reports. We demonstrate the use of UPP to measure the correct pairing percentage on a set of bispecific antibody data and to measure drug-to-antibody ratios from antibody-drug conjugates. Moreover, because the software is free and open-source, users can easily build on this platform to create customized workflows and calculations. Thus, UPP provides a flexible workflow that can be deployed in diverse settings and for a wide range of biotherapeutic applications.

NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query.

The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing such queries include, in part, the broad use of undefined study variables with limited or no annotations that make it difficult to understand the data available without significant interaction with the original authors. Using the Brain Imaging Data Structure (BIDS) to organize neuroimaging data has made querying across studies for specific image types possible at scale. However, in BIDS, beyond file naming and tightly controlled imaging directory structures, there are very few constraints on ancillary variable naming/meaning or experiment-specific metadata. In this work, we present NIDM-Terms, a set of user-friendly terminology management tools and associated software to better manage individual lab terminologies and help with annotating BIDS datasets. Using these tools to annotate BIDS data with a Neuroimaging Data Model (NIDM) semantic web representation, enables queries across datasets to identify cohorts with specific neuroimaging and clinical/behavioral measurements. This manuscript describes the overall informatics structures and demonstrates the use of tools to annotate BIDS datasets to perform integrated cross-cohort queries.

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.

Gmx_qk: An Automated Protein/Protein-Ligand Complex Simulation Workflow Bridged to MM/PBSA, Based on Gromacs and Zenity-Dependent GUI for Beginners in MD Simulation Study.

Open-source MD simulation tools provide academics and low-income countries with the ability to compete in drug discovery advancements. Gromacs is a well-known and established MD simulation tool, among others. Although command-line tools offer full flexibility to users, they require expertise and familiarity with the UNIX operating system. In this context, we have developed an automated bash workflow that enables users with minimal knowledge of UNIX or command-line tools to run protein/protein-ligand complex simulations bridged to MM/PBSA calculations. The workflow provides information to the user using Zenity widgets and requires minimal intervention, such as energy minimization, simulation duration, and output file naming. It initiates MD simulations within a few seconds (energy minimization, NVT, NPT, and MD) after taking input files and parameters, which takes 20-30 min in a command-line-based protocol. The single workflow also helps users to produce reproducible research results with fewer errors. The workflow is available at the GitHub repository: https://github.com/harry-maan/gmx_qk.

Intelligent Innovation Platform Design of Chinese Film and Television Music Based on Multi-Dimensional Information Fusion Algorithm

Experienced changes in recording techniques and musical styles. With the development of the times, digital technology is constantly developing and progressing. In the aspect of film and television music creation, the application of digital technology has been innovated accordingly. Using multi-source information fusion to build a music feature system to solve the problem of music recommendation cold start Users provide personalized music recommendations. The optimized high-concurrency Cuckoo Hashing algorithm is implemented on the DPDK platform, and the algorithm is optimized for large page memory, batch processing and preprocessing combined with DPDK features. Music information retrieval is expanded from the traditional retrieval based on keywords such as file name and artist name. At the same time, the evaluation of the example model of the system also verifies that the multi-information source fusion method proposed in this paper can optimize the precision and recall rate of the system.

The Night Albums: Visibility and the Ephemeral Photograph

The Night Albums: Visibility and the Ephemeral Photograph

Loyal Consumers: Efforts to Improve and Maintain Consumer Databases Using Youtube Optimization Techniques

This study tries to test the ranking model on youtube search. Youtube brings up search results based on the number of visits and video recommendations available. The method used in this research begins by collecting 10 keywords that appear in a youtube search. Of the 10 results will be used as the image file name to be used. The 10 image files are then turned into a video. The link generated from the upload process is then registered with a free ping service on the internet. For testing, day parameters are used, namely 1 day, 2 days and 3 days. Within 3 days the video has appeared on the first page of a Google search with keywords that match the names of the image files.

Geographic areas of responsibility among PCI-capable hospitals in the Philippines

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The archipelagic nature of the Philippines is unique and the geographic areas of responsibility for ST elevation myocardial infarction patients should be defined. Methodology The list of registered PCI capable hospitals and the total members of adult interventional cardiologists per island from the Philippine Society of Cardiac Catheterization Incorporation as of January 2022 was used. Their characteristics were described using frequency and percentage. The web application Map Developers was utilized to locate the exact location of each PCI-capable center and a 120km radius at the center of each PCI-capable hospital was drawn. The 120km radius was based on the average of 60km/hour from the Land Transportation & Traffic code multiplied by 2 hours based on the maximum expected delay from STEMI diagnosis to primary PCI. The map of Philippine provinces was obtained from the Creative Commons Attribution-Share Alike 4.0 international file name Ph administrative map blank. The maps of the geographic area of responsibility and provinces were interposed using MS PowerPoint version 2201 and categorized as covered, partially covered, and not covered. Results The analysis included a total of 3 islands with 82 provinces, 244 adult interventional cardiologists (IC) and 54 PCI-capable hospitals. The region registered the highest percentage of IC (85.7%) and PCI-capable hospitals (78.9%) followed by Visayas (8.6%) and (11.5%), and Mindanao (5.7%) and (9.6%) respectively. Most PCI-capable hospitals are privately (79.6%) versus government owned (20.4%). Most percentage of PCI-capable hospitals are in Metro Manila (46.3%), followed by Cebu (9.26%), Laguna and Pampanga (5.57%), Benguet, Davao del Sur, Misamis Oriental, Nueva Ecija and Iloilo (3.7%). Only 46.4% of the provinces are covered, 34.1% are partially covered and 19.5% are not covered. Conclusion In the Philippines, most PCI-capable hospitals are privately owned and centrally located on an island. There may be a need to add PCI-capable hospitals that are government owned, equally distributed, and strategically placed in partially and non-covered provinces. In addition, due to its archipelagic nature, STEMI patients in other provinces that are not covered may benefit from rapid fibrinolysis.

Lead-DBS v3.0: Mapping deep brain stimulation effects to local anatomy and global networks

Following its introduction in 2014 and with support of a broad international community, the open-source toolbox Lead-DBS has evolved into a comprehensive neuroimaging platform dedicated to localizing, reconstructing, and visualizing electrodes implanted in the human brain, in the context of deep brain stimulation (DBS) and epilepsy monitoring. Expanding clinical indications for DBS, increasing availability of related research tools, and a growing community of clinician-scientist researchers, however, have led to an ongoing need to maintain, update, and standardize the codebase of Lead-DBS. Major development efforts of the platform in recent years have now yielded an end-to-end solution for DBS-based neuroimaging analysis allowing comprehensive image preprocessing, lead localization, stimulation volume modeling, and statistical analysis within a single tool. The aim of the present manuscript is to introduce fundamental additions to the Lead-DBS pipeline including a deformation warpfield editor and novel algorithms for electrode localization. Furthermore, we introduce a total of three comprehensive tools to map DBS effects to local, tract- and brain network-levels. These updates are demonstrated using a single patient example (for subject-level analysis), as well as a retrospective cohort of 51 Parkinson's disease patients who underwent DBS of the subthalamic nucleus (for group-level analysis). Their applicability is further demonstrated by comparing the various methodological choices and the amount of explained variance in clinical outcomes across analysis streams. Finally, based on an increasing need to standardize folder and file naming specifications across research groups in neuroscience, we introduce the brain imaging data structure (BIDS) derivative standard for Lead-DBS. Thus, this multi-institutional collaborative effort represents an important stage in the evolution of a comprehensive, open-source pipeline for DBS imaging and connectomics.

Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

Cloud computing (CC) is an advanced technology that provides access to predictive resources and data sharing. The cloud environment represents the right type regarding cloud usage model ownership, size, and rights to access. It introduces the scope and nature of cloud computing. In recent times, all processes are fed into the system for which consumer data and cache size are required. One of the most security issues in the cloud environment is Distributed Denial of Service (DDoS) attacks, responsible for cloud server overloading. This proposed system ID3 (Iterative Dichotomiser 3) Maximum Multifactor Dimensionality Posteriori Method (ID3-MMDP) is used to overcome the drawback and a relatively simple way to execute and for the detection of (DDoS) attack. First, the proposed ID3-MMDP method calls for the resources of the cloud platform and then implements the attack detection technology based on information entropy to detect DDoS attacks. Since because the entropy value can show the discrete or aggregated characteristics of the current data set, it can be used for the detection of abnormal data flow, User-uploaded data, ID3-MMDP system checks and read risk measurement and processing, bug rating file size changes, or file name changes and changes in the format design of the data size entropy value. Unique properties can be used whenever the program approaches any data error to detect abnormal data services. Finally, the experiment also verifies the DDoS attack detection capability algorithm.

A study of ‘calf-path’ in file naming in institutional repositories in India

This study examined the file naming practices in 39 institutional repositories. There is evidence that calf-path exists infile naming among the curators of institutional repositories in India. The study showed that no standard or logic seems tohave been followed by repositories in the naming of the files, except by the National Digital Library of India (NDLI) and theCSIR-National Institute of Science Communication and Policy Research (CSIR-NIScPR). The study also examined thecomposition of the filenames, which shows that the author names (11.2%), titles (11.9%), journal title with volume and issuenumbers (21.1%) form the basis for the formation of filenames. It is suggested that digital repository managers have to givemore attention to name files in the institutional repository in the interest of uniformity and consistency.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics. The situation becomes worse in the case of files downloaded into systems from the Internet. Currently, most users either have to change file names manually or leave a meaningless name of the files, which increases the time to search required files and results in redundancy and duplications of user files. Currently, no significant work is done on automated file labeling during the organization of heterogeneous user files. A few attempts have been made in topic modeling. However, one major drawback of current topic modeling approaches is better results. They rely on specific language types and domain similarity of the data. In this research, machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus. A different file labeling technique has also been used to get the meaningful and `cohesive topic of the files. The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

Numerical and Experimental Data of the Implementation of Logic Gates in an Erbium-Doped Fiber Laser (EDFL)

In this article, the methods for obtaining time series from an erbium-doped fiber laser (EDFL) and its numerical simulation are described. In addition, the nature of the obtained files, the meaning of the changing file names, and the ways of accessing these files are described in detail. The response of the laser emission is controlled by the intensity of a digital signal added to the modulation, which allows for various logical operations. The numerical results are in good agreement with experimental observations. The authors provide all of the time series from an experimental implementation where various logic gates are obtained.

From pleasure to punding: Distinct patterns of anhedonia and impulsivity linked to motivational disturbances in Parkinson disease

Introduction Apathy and impulse control disorders (ICD) are common comorbid motivational syndromes in Parkinson disease (PD). This study aimed to determine if patients with these motivational disturbances exhibit different patterns of anhedonia and trait impulsivity. Methods Sixty-four non-demented patients with PD completed questionnaires assessing apathy and ICD symptoms, which were used to classify participants into one of the following groups: apathy only, ICD only, both, and neither. Participants also completed multidimensional measures of anhedonia and trait impulsivity, which were compared across groups defined by motivational status. Results Individuals with both apathy and ICD had significantly greater symptoms of positive and negative urgency than all other groups and had significantly greater consummatory anhedonia and lack of premeditation and perseverance than those with ICD only and neither. Patients with apathy only also reported significantly greater anticipatory anhedonia than those with ICD only and the neither group. There were no significant between-group differences in sensation seeking. Conclusion Distinct patterns of impulsivity and anhedonia characterize unique behavioral phenotypes of motivational disturbances in PD and may reflect important differences in the underlying neurobiological mechanisms. Clinicians should be aware that motivational disturbances may be more severe in cases where apathy co-occurs with one or more ICD. HIGHLIGHTS Highlights are mandatory for all submissions except letters. They consist of a short collection of bullet points that convey the core findings of the article and should be submitted in a separate file in the online submission system. Please use “Highlights” in the file name and include 3–5 bullet points (maximum 85 characters, including spaces, per bullet point). See https://www.elsevier.com/highlights for examples.