Static Files Research Articles

Mobilizing underutilized storage nodes via job path: A job-aware file striping approach

Users’ limited understanding of the storage system architecture prevents them from fully utilizing the parallel I/O capability of the storage system, leading to a negative impact on the overall performance of supercomputers. Therefore, exploring effective strategies for utilizing parallel I/O capabilities is imperative. In this regard, we conduct an analysis of the workload on two production supercomputers’ Object Storage Targets (OSTs) and study the potential inefficient I/O patterns for high performance computing jobs. Our research findings indicate that under the traditional stripe settings that most supercomputers use to ensure stability, the real-time load on OSTs is severely unbalanced. This imbalance results in I/O requests that fail to fully utilize the available OSTs. To tackle this issue, we propose a job-aware optimization approach, which includes static and dynamic file striping. Static file striping optimizes all user jobs, whereas dynamic file striping employs clustering of job names and job paths to extract similarities among jobs and predict partially stripe-optimizable jobs for users. Additionally, a stripe recovery mechanism is employed to mitigate the negative impact of stripe misconfigurations. This approach appropriately adjusts the file stripe layout based on the job’s I/O pattern, allowing for better mobilization of underutilized OSTs to enhance parallel I/O capabilities. Through experimental verification, the number of OSTs that jobs can use has been increased, effectively improving the parallel I/O performance of the job without significantly affecting operational stability.

A Phanerozoic gridded dataset for palaeogeographic reconstructions

Global Plate Models are widely used in the Earth Sciences to reconstruct the past geographic position of geological and palaeontological samples. However, the application of Global Plate Models to retrieve ‘palaeocoordinates’ is not trivial. Different Global Plate Models exist which vary in their complexity, spatiotemporal coverage, reference frame, and intended use. Consequently, careful consideration of which models are appropriate for any given research question is required. Here, we document and provide access to reconstruction datasets for five Global Plate Models in the palaeomagnetic reference frame. These datasets provide ‘true’ palaeolatitudes for three discrete global grids reconstructed at one-million-year intervals throughout the Phanerozoic (540–0 Ma), offering three key benefits for the Earth Science community: (1) allow users to look up palaeocoordinates for their samples (e.g. fossil occurrences) through simple indexing without having to learn additional software packages; (2) provide palaeocoordinates which have been generated consistently with thorough documentation; (3) provide static files which preserve model output and which can be used to evaluate palaeogeographic differences between Global Plate Models.

A Robust Approach to Malware Detection through Ensemble learning

Abstract: With the proliferation of digital threats in today's interconnected world, the need for efficient and effective automated malware detection systems has become paramount. Traditional signature-based methods often fail to keep pace with the evolving landscape of malware, necessitating the development of more sophisticated techniques. In this paper, we propose a robust approach to automated malware detection leveraging ensemble learning techniques. Ensemble learning, which combines the predictions of multiple base models, has demonstrated remarkable success in various domains, including cybersecurity. Our approach harnesses the diversity of ensemble methods to enhance the detection accuracy and robustness against adversarial attacks. By integrating diverse base classifiers such as decision trees, random forests, support vector machines, and neural networks, our ensemble model learns to effectively discriminate between benign and malicious software samples. Furthermore, we introduce novel feature engineering strategies tailored to capture the intricate characteristics of malware. These features encompass a wide range of attributes, including static file properties, dynamic behavioural patterns, and frequency-based representations. Leveraging these rich feature sets, our ensemble model can generalize well across different types of malware families and variants.

Optimistic online caching for batched requests

In this paper, we investigate ‘optimistic’ online caching policies, distinguished by their use of future request predictions derived, for example, from machine learning models. Traditional online optimistic policies, grounded in the Follow-The-Regularized-Leader (FTRL) algorithm, incur a higher computational cost compared to classic policies like Least Frequently Used (LFU) and Least Recently Used (LRU). This is due to each cache state update necessitating the resolution of a constrained optimization problem. To address this problem, we introduce and analyze the ‘batched’ version of two distinct FTRL-based optimistic policies. In this approach, the cache updates occur less frequently, thereby amortizing the update cost over time or over multiple requests. Rather than updating the cache with each new request, the system accumulates a batch of requests before modifying the cache content. First, we present a batched version of the Optimistic Bipartite Caching (OBC) algorithm, that works for single requests, then we introduce a new optimistic batched caching policy, the Per-Coordinate Optimistic Caching (PCOC) algorithm, derived from the per-coordinate-based FTRL. We demonstrate that these online algorithms maintain ‘vanishing regret’ in the batched case, meaning their average performance approaches over time that of an optimal static file allocation, regardless of the sequence of file requests. We then compare the performance of these two strategies with each other and against optimistic versions of LFU and LRU. Our experimental results indicate that this batched optimistic approach outperforms traditional caching policies on both stationary and real-world file request traces.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BackgroundWith the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain. ObjectiveIn this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making. MethodsSpecifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk. ResultThe proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes. ConclusionThe experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

Framework for identifying network attacks through packet inspection using machine learning

Abstract In every network, traffic anomaly detection system is an essential field of study. In the communication system, there are various protocols and intrusions. It is still a testing area to find high precision to boost the correct distribution ratio. Many authors have worked on various algorithms such as simple classification, K-Means, Genetic Algorithm, and Support Vector Machine approaches, and they presented the efficiency and accuracy of these algorithms. In this article, we have proposed a feature extraction technique known as “k-means clustering,” which has its roots in signal processing and is employed to divide a set of n observations into k clusters, each of which has its origin from the observation with the closest mean. K-Means method is applied in this study to investigate the stream and its implementation and applications using Python and the dataset on the KDDcup99. The effectiveness of the outcome indicates the planned work’s efficiency in relation to other widely available alternatives. Apart from the applied method, a web-based framework is designed, which can inspect an actual network traffic packet for identifying network attacks. Instead of using a static file for testing the network attack, a web page-based solution uses database to collect and test the information. Real-time packet inspection is provided in the proposed work for identifying new attacks.

A Malware Detection Approach Based on Deep Learning and Memory Forensics

As cyber attacks grow more complex and sophisticated, new types of malware become more dangerous and challenging to detect. In particular, fileless malware injects malicious code into the physical memory directly without leaving attack traces on disk files. This type of attack is well concealed, and it is difficult to find the malicious code in the static files. For malicious processes in memory, signature-based detection methods are becoming increasingly ineffective. Facing these challenges, this paper proposes a malware detection approach based on convolutional neural network and memory forensics. As the malware has many symmetric features, the saved training model can detect malicious code with symmetric features. The method includes collecting executable static malicious and benign samples, running the collected samples in a sandbox, and building a dataset of portable executables in memory through memory forensics. When a process is running, not all the program content is loaded into memory, so binary fragments are utilized for malware analysis instead of the entire portable executable (PE) files. PE file fragments are selected with different lengths and locations. We conducted several experiments on the produced dataset to test our model. The PE file with 4096 bytes of header fragment has the highest accuracy. We achieved a prediction accuracy of up to 97.48%. Moreover, an example of fileless attack is illustrated at the end of the paper. The results show that the proposed method can detect malicious codes effectively, especially the fileless attack. Its accuracy is better than that of common machine learning methods.

Design and realization of pharmaceutical survey data acquisition and analysis system based on principal component analysis algorithm

With the rapid development of electronic communication technology, data acquisition systems have been widely used in all aspects of people's daily lives. Especially in the medical field, there is an urgent need for a pharmaceutical survey data collection and analysis system. In the massive pharmaceutical survey data, the principal component analysis algorithm is used to condense the attribute variables of the original data, which greatly improves the efficiency of data collection and analysis. Based on this, this article will study the pharmacy survey data collection and analysis system based on principal component analysis algorithm. This text first carries on the system function structure design, designs the system development framework from the hardware layer, the interface layer, and the middle layer. This paper designs the functional modules of the system in detail, including account management, file management, and report management. Finally, the system found through testing that the average delay for obtaining diagnostic reports, previous inspections, static files, and querying patient information were all about 40ms. This shows that the system runs at a high speed and can meet practical applications.

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.

Improving the quality of national organic seed databases to increase the use of organic seed and propagation materials in Europe

The purpose of the European national organic seed databases is to list the varieties for which organically produced seeds and vegetative propagation material is available on the market. The correct functioning of the databases can facilitate the use and distribution of organic seed by improving the transparency of the organic seed market. Each EU Member State can establish its own database, resulting in different specific technical solutions among countries. This study evaluated the quality of organic seed databases in place in the EU countries (including the United Kingdom and Switzerland) through an online survey. An Information Systems Success Model was used as a framework of our evaluation. The respondents assessed the Ease of use, Comprehension and Completeness of the databases. Based on 537 responses obtained from farmers, seed suppliers and certification bodies, our analysis revealed that the Completeness of the databases was evaluated as the most critical aspect. Interactive fully computerised databases should be preferred to the provision of static files as they positively influence perceived completeness. In addition, the support provided by the database managers on how to use databases and a straightforward administrative process for the registration of seed offers or the management of derogations can increase databases comprehensibility and ease of use.

GIS based methodology to analyse the public transport supply: Hungarian case studies

The paper aims to introduce a new way of comparing the efficiency of public transport operations based on publicly available data. It draws on four main sources, the Hungarian Central Statistical Office, public transport provider data, GTFS and OSM map layers. Methodologically, it combines the use of the GTFS format and corresponding static timetable component files, Thiessen polygons and empirical selection of relative indicators. As places of research, three comparable Hungarian cities have been selected by their population size; Pécs, Szeged and Miskolc. The comparison consists of 8 quantitative indicators that cover six major geographical aspects of transport operation (accessibility in terms of proximity, capacity, connectivity, density, frequency and velocity of vehicles). The analysis does not consider the mode of public transport, thus opening up the possibility of an independent comparison of efficiency regardless of various infrastructure characteristics. The results show that Miskolc and Pécs achieved the best values in four indicators. On the contrary, the city of Szeged, despite its diverse structure of transport modes, does not have an advantage in any aspect. The relatively loosely anchored methodology leaves room for an extension to include economic, environmental, and other specific factors.

Static file analysis to detect rootkits in the system

Rootkits allow attackers to steal data from your computer, exploit its processing power, and monitor user activities. There are two main ways to detect a rootkit in the system: the first implies detecting a rootkit on a running computer, the other – detecting a rootkit in a data image (or on a non-functioning computer). The paper provides practical examples, thus allowing readers to repeat the experiments without too much difficulty.

Development of bus transit system control measures with open transit data

The purpose of this study is to analyse the accuracy of the static schedule of bus transit network in Delhi using real-time data available from Delhi’s Open Transit Data (OTD) platform. To access and organise the data, an algorithm that can convert real-time data into a General Transit Feed Specification (GTFS) format, needs to be designed. Further, this study intends to develop a methodology, which can convert raw data of bus locations into link travel times, which consequently, helps in identifying problematic links. As researchers continue to make use of the data available via GTFS, they may well be aware of the fact that such data may differ systematically from actual transit operations. Continuous improvement of the accuracy of the GTFS static file would benefit its users.

Quantitative comparison of data-driven gating and external hardware gating for 18F-FDG PET-MRI in patients with esophageal tumors

BackgroundRespiratory motion during PET imaging reduces image quality. Data-driven gating (DDG) based on principal component analysis (PCA) can be used to identify respiratory signals. The use of DDG, without need for external devices, would greatly increase the feasibility of using respiratory gating in a routine clinical setting. The objective of this study was to evaluate data-driven gating in relation to external hardware gating and regular static image acquisition on PET-MRI data with respect to SUVmax and lesion volumes.MethodsSixteen patients with esophageal or gastroesophageal cancer (Siewert I and II) underwent a 6-min PET scan on a Signa PET-MRI system (GE Healthcare) 1.5–2 h after injection of 4 MBq/kg 18F-FDG. External hardware gating was done using a respiratory bellow device, and DDG was performed using MotionFree (GE Healthcare). The DDG raw data files and the external hardware-gating raw files were created on a Matlab-based toolbox from the whole 6-min scan LIST-file. For comparison, two 3-min static raw files were created for each patient. Images were reconstructed using TF-OSEM with resolution recovery with 2 iterations, 28 subsets, and 3-mm post filter. SUVmax and lesion volume were measured in all visible lesions, and noise level was measured in the liver. Paired t-test, linear regression, Pearson correlation, and Bland-Altman analysis were used to investigate difference, correlation, and agreement between the methods.ResultsA total number of 30 lesions were included in the study. No significant differences between DDG and external hardware-gating SUVmax or lesion volumes were found, but the noise level was significantly reduced in the DDG images. Both DDG and external hardware gating demonstrated significantly higher SUVmax (9.4% for DDG, 10.3% for external hardware gating) and smaller lesion volume (− 5.4% for DDG, − 6.6% for external gating) in comparison with non-gated static images.ConclusionsData-driven gating with MotionFree for PET-MRI performed similar to external device gating for esophageal lesions with respect to SUVmax and lesion volume. Both gating methods significantly increased the SUVmax and reduced the lesion volume in comparison with non-gated static acquisition. DDG resulted in reduced image noise compared to external device gating and static images.

Automating Test Case Identification in Java Open Source Projects on GitHub

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are written in real projects. In this paper, the following was investigated: (i) the denotation of the word "test" in different natural languages; (ii) whether the number of occurrences of the word "test" correlates with the number of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3M GitHub projects. We analyzed 20,340 test cases in 803 classes manually and 170k classes using an automated approach. The results show that: (i) there exists a weak correlation (r = 0.655) between the number of occurrences of the word "test" and the number of test cases in a class; (ii) the proposed algorithm using static file analysis correctly detected 97% of test cases; (iii) 15% of the analyzed classes used main() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very complex due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions, and to mine tests to improve program comprehension in the future.

Video object detection algorithm based on dynamic combination of sparse feature propagation and dense feature aggregation

In comparison with static image object detection, focusing on video objects has greater research significance in realizing intelligent monitoring and automatic anomaly detection. However, it may be challenging to apply the most advanced image recognition networks to video data, as the number of static frame files represented in a video is often huge, thereby causing the problem of the slow evaluation speed, in addition to other issues, such as motion blur, low resolution, occlusion, and object deformation. In the present study, to mitigate these deficiencies, we applied sparse feature propagation to improve the detection speed and dense feature aggregation to refine the detection accuracy. Moreover, we utilized the key frame scheduling strategy relying on the consistency of feature information. Implementing these technologies allowed steadily improving the detection speed and accuracy to achieve high performance. To verify the applicability of the optimized video detection strategy proposed in this paper, we selected the part of the video data in the ImageNet VID training dataset. Then, the other part of this dataset was used to conduct the experiments, including the calculation and comparison of mean average precision (MAP) and frames per second (FPS).

An intelligent computational framework for the definition and identification of the womenswear silhouettes

Purpose The current studies on the clothing silhouette are very limited. The purpose of this paper is to propose an innovative framework to intelligently identify the womenswear silhouette with the latest computer technologies. To clearly define the womenswear silhouette, an accurate numerical definition is proposed. Design/methodology/approach The study first processes and segments the useful parts on the static catwalk image data files following existing graphic extraction approaches. Six basic alphabetical womenswear silhouette types are selected and numerically defined. Then, the proposed framework automatically classifies the six basic womenswear silhouettes considering the different slopes between three main clothing parts. Six clothing situations are discovered according to different designs and the detailed cases are systematically categorized. In addition, aspects influencing the judgment of the clothing silhouettes such as the skin, the background, the drastic change points are also considered. The proposed silhouette definition and identification framework is novel and proved accurate. Findings The proposed definition and identification framework of womenswear silhouettes have been proved a viable approach that is fully compatible with the current computer technologies. The validation study shows that the presented identification procedure has a desirable accuracy over 90 percent. Originality/value The proposed methodology develops brand new standards to numerically define and identify the womenswear silhouette, which was not available in the past. Besides, the measurement, the identification and the classification procedures are fully validated by the image data collected from 14 world famous brands over 11 consecutive seasons. It is shown that the proposed numerical framework of the womenswear silhouettes is a robust one, considering all of the observed design variabilities.

Three Techniques for Enhancing Chaos-Based Joint Compression and Encryption Schemes.

In this work, three techniques for enhancing various chaos-based joint compression and encryption (JCAE) schemes are proposed. They respectively improved the execution time, compression ratio, and estimation accuracy of three different chaos-based JCAE schemes. The first uses auxiliary data structures to significantly accelerate an existing chaos-based JCAE scheme. The second solves the problem of huge multidimensional lookup table overheads by sieving out a small number of important sub-tables. The third increases the accuracy of frequency distribution estimations, used for compressing streaming data, by weighting symbols in the plaintext stream according to their positions in the stream. Finally, two modified JCAE schemes leveraging the above three techniques are obtained, one applicable to static files and the other working for streaming data. Experimental results show that the proposed schemes do run faster and generate smaller files than existing JCAE schemes, which verified the effectiveness of the three newly proposed techniques.

PENGELOLAAN DAN PENATAAN ARSIP AKTIF DAN INAKTIF

A file is a record of activities in a variety of forms and media in accordance with the development of technology, information and communication which was made and received by the person who create the files in the implementation of social life, state and nation. Based on the functions and uses of file, files can be devided into dynamic and static file. Improper file handling would give some effects to the process of the activities in its organization or institutions. The records and archieves need management’s attention, qualified human resources and adequate place as well. File regulation in Indonesia are regulated in Law number 43 of 2009.

Pengamanan dan Penyelamatan Arsip Statis di Dinas Kearsipan dan Perpustakaan Provinsi Sumatera Barat

The writing of this paper aims to discuss Static Archive Security and Rescue in the Archives and Library Services of Sumatra Province. This study aims to; (1) Describe the security of static archives in the Department of Archives and Library of West Sumatra Province. (2) Describe the static archive rescue at the West Sumatra Province Archives and Library Service. (3) Describe the obstacles to static safety and rescue in the Department of Archives and Library of West Sumatra Province. (4) Describe efforts in static safety and rescue in the Department of Archives and Library of West Sumatra Province. The method used is descriptive method. Data was collected through observation and direct interviews with officers in the Archives and Library Service of West SumatraProvince. Based on the results of the research, Static Archive Safety and Rescue in the Archives and Library Service of West Sumatra Province are as follows: First, physical security and rescue of archives in the Archives and Library Service of West Sumatra Province is carried out lamination process, fumigation regulates moisture/dryness and air temperature room. In order to secure and record static archives in terms of information, it can be done by controlling every person who will access the static archive must go through a predetermined procedure, store static files in a special room that is maintained. The two constraints faced in securing and rescuing the static archive of the Archives and Libraries of West Sumatra Province are the lack of human resources in securing and holding static archives, the lack of adequate infrastructure for securing and rescuing static archives and lack of awareness of Opd and Skpd to report archives static to the Archives and Library Service of West Sumatra Province.The three efforts made to overcome the obstacles faced in securing and saving the static archive of the Archives and Library Service of West Sumatra Province by providing training programs and apprenticeship staff to national archives. Then complete and maintain facilities and infrastructure to secure and record static archives and send letters to OPD and SKPD to submit static files to them to the Archives and Libraries of West Sumatra Province.