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Data-intensive applications executing on NVM-based storage systems experience serious bottlenecks when moving data between DRAM and NVM. We advocate for the use of the long-existing but recently neglected on-chip DMA to expedite data movement with three contributions. First, we explore new latency-oriented optimization directions, driven by a comprehensive DMA study, to design a high-performance DMA module, which significantly lowers the I/O size threshold to observe benefits. Second, we propose a new data movement engine, Fastmove, that coordinates the use of the DMA along with the CPU with DDIO-aware strategies, judicious scheduling, and load splitting such that the DMA's limitations are compensated, and the overall gains are maximized. Finally, with a general kernel-based design, simple APIs, and DAX file system integration, Fastmove allows applications to transparently exploit the DMA and its new features without code change. We run three data-intensive applications MySQL, GraphWalker, and Filebench atop NOVA, ext4-DAX, and XFS-DAX, with standard benchmarks like TPC-C, and popular graph algorithms like PageRank. Across single- and multi-socket settings, compared to the conventional CPU-only NVM accesses, Fastmove introduces to TPC-C with MySQL 1.13–2.16× speedups of peak throughput, reduces the average latency by 17.7–60.8%, and saves 37.1–68.9% CPU usage spent in data movement. It also shortens the execution time of graph algorithms with GraphWalker by 39.7–53.4%, and introduces 1.01–1.48× throughput speedups for Filebench. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, distributed systems have become an indispensable part of the field of information storage and management. In the process of large-scale data processing, it is an important issue to compress or replay files to ensure their integrity. In order to solve the problem of large-scale computing resources and data storage, the distributed file system emerged as a new system structure. The purpose of this paper to is study the key technologies of the distributed memory file system of high-performance computers is to improve the capability and efficiency of the distributed system. This paper mainly uses the experimental method and the comparative method to analyze the key technology of the distributed memory file system of the high-performance computer. Experimental results show that the maximum bandwidth value of DFMS in file memory processing can reach more than 2000, and the value becomes more stable as the file increases. [ABSTRACT FROM AUTHOR]

The imbalanced I/O load on large parallel file systems affects the parallel I/O performance of high-performance computing (HPC) applications. One of the main reasons for I/O imbalances is the lack of a global view of system-wide resource consumption. While approaches to address the problem already exist, the diversity of HPC workloads combined with different file striping patterns prevents widespread adoption of these approaches. In addition, load-balancing techniques should be transparent to client applications. To address these issues, we propose Tarazu, an end-to-end control plane where clients transparently and adaptively write to a set of selected I/O servers to achieve balanced data placement. Our control plane leverages real-time load statistics for global data placement on distributed storage servers, while our design model employs trace-based optimization techniques to minimize latency for I/O load requests between clients and servers and to handle multiple striping patterns in files. We evaluate our proposed system on an experimental cluster for two common use cases: the synthetic I/O benchmark IOR and the scientific application I/O kernel HACC-I/O. We also use a discrete-time simulator with real HPC application traces from emerging workloads running on the Summit supercomputer to validate the effectiveness and scalability of Tarazu in large-scale storage environments. The results show improvements in load balancing and read performance of up to 33% and 43%, respectively, compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

Digital service platforms provided by academic support departments in Macao assist academic staff and students in various areas such as registry, student affairs, academic activities, and research. As the number of undergraduate students increases and new departments are established, academic staff often face the challenge of dealing with paperwork that contains similar content but different formats. This situation results in redundancies and a waste of time. This paper presents our endeavors to simplify administrative procedures in higher education by automating restructured documentation and developing secondary file systems. The paper presents two case studies: Scenario One focuses on streamlining the publication system for academic staff who submit papers in different formats. At the same time, Scenario Two aims to simplify the daily paperwork process for academic staff. Both cases involve transforming the distribution of administrative documents, transitioning from a standardized form with guidelines to a customized form with concise tips. This approach allows academic staff to handle only the necessary information, which may not be available in the database or requires verification. The case studies serve to demonstrate the effectiveness of this administrative simplification. [ABSTRACT FROM AUTHOR]

Unikernels are specialized operating system (OS) kernels optimized for a single application or service, offering advantages such as rapid boot times, high performance, minimal memory usage, and enhanced security compared to general-purpose OS kernels. Unikernel applications must remain compatible with the runtime environment of general-purpose kernels, either through binary or source compatibility. As a result, many Unikernel projects have prioritized system call compatibility over performance enhancements. In this paper, we explore the design principles of Unikernel file systems and introduce a new file system tailored for Unikernels named ULFS (Ultra Lightweight File System). ULFS provides system call services akin to those of general-purpose OS kernels but achieves superior performance and security with significantly fewer system resources. Specifically, ULFS is developed as a lightweight file system embracing Unikernel design principles. It streamlines system calls, removes unnecessary locks, and omits permission checks for multiple users, utilizing a non-hypervisor architecture. This approach significantly reduces the memory footprint of the file system and enhances performance. Through measurement studies, we assess the performance and memory requirements of various file systems from major Unikernel projects. Our findings demonstrate that ULFS surpasses several existing Unikernel file systems, including Rumpvfs, Ramfs-u, Ramfs-q, 9pfs, and Hcfs. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the personal information management (PIM) behaviours of social science faculty in Africa. The study examined the experiences and encounters of selected social scientists in Africa in organising and finding and re-finding of the information they previously created or stored. More specifically, the study sought to examine how faculty keep and refind information, the files and folders in which they store the information. Also, the study examined the nature and characteristics of faculty information spaces with particular respect to electronic documents including emails and paper documents. Design/methodology/approach: Sample survey research design and a mixed methods approach consisting of qualitative and quantitative were used. Data was collected using a discursive technique, an interview schedule and a questionnaire. Data analysis was conducted using factorial analysis of mixed data design, guided by a combination of category and codes identification using NVivo and Statistical Package for the Social Sciences (SPSS) 17. Principal component analysis (PCA) of factor analysis was executed to identify key components. Findings: Eleven issues, namely, time, infrastructure, importance of the information, folder/file management, document characteristics and organisational context played significant roles in the PIM behaviours of the respondents. Others were importance of the information, document overload, memory, workload and computer literacy. PCA extracted four major components, namely, document overload, time, computer literacy and importance of the information. Research limitations/implications: An expansion in the number of faculty involved in this study would probably yield a more reliable outcome. Extending the study to cover Africa would also yield a more applicable result. Practical implications: The key PIM issues identified in this study, namely, document overload, time, computer literacy and importance of the information should constitute the focus of continuous information literacy education aimed at improving PIM social scientists' faculty in Africa. Social implications: Improved PIM of social science faculty will result to improved research productivity and good health. Originality/value: PIM of social scientists has not been examined in the literature, and yet it is crucial for further understanding their learning and information behaviours, and improving their productivity. The design and administration of a questionnaire constructed based on codes extracted from qualitative and discursive sessions to the same respondents from whom the qualitative data was collected makes the findings very strong. A further deployment of factorial analysis of mixed data design to handle qualitative data makes the contribution of the study very significant. [ABSTRACT FROM AUTHOR]

As the importance of email increases, the amount of malicious email is also increasing, so the need for malicious email filtering is growing. Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques, we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering. Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbor(KNN), and Decision Tree) in terms of execution time and accuracy. Malicious email was filtered with MapReduce programming using the Naïve Bayes technique, which is a supervised machine learning method, in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied. According to the results of a comparison of the accuracy and predictive error rates of the two methods, the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method. [ABSTRACT FROM AUTHOR]

Cluster computing has become an inevitable part of data processing as the huge volume of data being produced from different sources like online social media, IoT, mobiledata, sensor data, black box data and so on increases in an exponentially fast manner. Distributed File System defines different methods to distribute, read and eliminate the files among different cluster computing nodes. It is found that popular distributed file systems such as Google File System and Hadoop Distributed File System store metadata centrally. This creates a chance for a Single Point of Failure that arises the need for backup and alternative solutions to recover the metadata on the failure of the metadata server. Also, the name node server is built using expensive and reliable hardware. For small and medium clusters, it is not cost effective to maintain expensive name node server. Even though cheap commodity hardware may substitute the name node functionality, they are prone to hardware failure. This paper proposes a novel distributed file system to distribute files over a cluster of machines connected in a Peer-to-Peer network. The most significant feature of the file system is its capability to distribute the metadata using distributed consensus, using hash values. Although the distributed metadata is visible to the public, the methodology ensures that it is immutable and irrefutable. As part of the in-depth research, the proposed file system has been successfully tested in the Google Cloud Platform. Also, the basic operations like read, write, and delete on Distributed File System with distributed metadata are compared with that of Hadoop Distributed File System based on distribution time on the same cluster setup. The novel distributed file system provides better results compared to the existing methodologies. [ABSTRACT FROM AUTHOR]

Recently, decentralized file systems are widely used to overcome centralized file systems' load asymmetry between nodes and the scalability problem. Due to the lack of a metadata server, decentralized systems require more RPC requests to control metadata processing between clients and servers, which adversely impacts the I/O performance and traffic imbalance by increasing RPC latency. In this paper, we propose an efficient I/O scheme to reduce the RPC overhead in decentralized file systems. Instead of sending a single RPC request at a time, we enqueued the RPCs in the global queue and merged them into larger RPC requests, thus avoiding excessive RPC latency overheads. The experimental results showed that our scheme improves write and read performance by up to 13% and 16%, respectively, compared with those of the original. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article focuses on the Austrian archival institution Diocesan Archive St. Pölten's engagement in preserving electronic data and the need to standardize the document flow in the Diocese of St. Pölten before implementing a digital archive. Topics discussed include the transition from analog to electronic records management through the Document Management System (DMS), the optimization of business processes, and the establishment of specialized teams.

Document clustering is frequently used in applications of natural language processing, e.g. to classify news articles or create topic models. In this paper, we study document clustering with the common clustering pipeline that includes vectorization with BERT or Doc2Vec, dimension reduction with PCA or UMAP, and clustering with K-Means or HDBSCAN.We discuss the interactions of the different components in the pipeline, parameter settings, and how to determine an appropriate number of dimensions. The results suggest that BERT embeddings combined with UMAP dimension reduction to no less than 15 dimensions provides a good basis for clustering, regardless of the specific clustering algorithm used. Moreover, while UMAP performed better than PCA in our experiments, tuning the UMAP settings showed little impact on the overall performance. Hence, we recommend configuring UMAP so as to optimize its time efficiency. According to our topic model evaluation, the combination of BERT and UMAP, also used in BERTopic, performs best. A topic model based on this pipeline typically benefits from a large number of clusters. [ABSTRACT FROM AUTHOR]

Existing nonvolatile memory (NVM)-based file systems can fully leverage the characteristics of NVM to obtain better performance than traditional disk-based file systems. It has the potential capacity to efficiently manage metadata and perform fast metadata operations. However, most NVM-based file systems mainly focus on managing file metadata (inode), while pay little attention to directory metadata (dentry), which also has a noticeable impact on the file system performance. Besides, the traditional journaling technique that guarantees metadata consistency may not yield satisfactory performance on NVM-based file systems. To solve these problems, in this article we propose a fast and low overhead metadata operation mechanism, called FLOMO. It first adopts a novel slotted-paging structure in NVM to reorganize dentry for efficiently performing dentry operations, and utilizes the red–black tree in DRAM to accelerate dentry lookup and the search process of dentry deletion. Moreover, FLOMO presents a selective journaling scheme for metadata updates, which partially logs the changes related to dentry in the proposed slotted page, thereby, mitigating the redundant journaling overhead. To verify FLOMO, we implement it in a typical NVM-based file system, the persistent memory file system (PMFS). Experimental results show that FLOMO accelerates the metadata operations in PMFS by 34.4% $\sim 59$ %, and notably reduces the journaling overhead for metadata, shortening the latency by 59% on average. For real-world applications, FLOMO has higher throughput compared with PMFS, PMFS without journal, and NOVA, achieving up to $2.1\times $ , $1.1\times $ , and $1.3\times $ performance improvement, respectively. [ABSTRACT FROM AUTHOR]

The ever-growing sizes and frequent updating of mobile applications cause high network and storage cost for updating. Hence, emerging mobile systems often employ incremental update algorithms, typically HDiffPatch, to upgrade mobile applications. However, we find that existing incremental update algorithms not only generate a significant amount of redundant data accesses, but also lacks of consistency guarantees for the whole package of application. In this paper, we present a novel Consistent and Efficient Incremental Update (CEIU) mechanism for upgrading mobile applications. Firstly, CEIU reduces the memory consumption and file access of incremental updates by reusing the indexes of blocks in the old image rather than copying the blocks. Secondly, CEIU employs a two-level journaling mechanism to ensure the consistency of the whole package and subfiles of the new image. We implement the proposed mechanism in Linux kernel based on TI-LFAT file system and evaluate it with real-world applications. The experimental results show that the proposed mechanism can reduce 30%–80% memory footprints in comparison with HDiffPatch, the state-of-the-art incremental update algorithm. It also significantly reduces the recovery time when power failure or system crash occurs. [ABSTRACT FROM AUTHOR]

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain's decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions. [ABSTRACT FROM AUTHOR]

This annotation describes the HMML Authority File (HAF), an open access database of authority records used by the Hill Museum & Manuscript Library that currently focuses on name authorities related to the Eastern Christian and Islamic manuscript traditions, but will eventually include all of HMML's authority files. It explains the project's history, describes how the file is populated, and discusses the best methods for searching and accessing the rich data stored in the file. As the file is a Beta project, the annotation also discusses its shortcomings and their intended solutions, as well as other future directions. [ABSTRACT FROM AUTHOR]

Compared with traditional files, electronic personnel files have the characteristics of the economy, environmental protection, convenience, and sharing and are gradually replacing traditional paper files. However, the development of electronic archives is still in its infancy, and there are still many problems, including the professional quality of personnel, information management, and the security of electronic archives storage. As an emerging technology, blockchain technology has the characteristics of decentralization, immutability, and traceability. This paper applies blockchain technology in the management of electronic archives, overcomes the internal distortion and insecurity of electronic archives, and designs an electronic archives management module based on blockchain technology. Through the application in some schools, the effectiveness and practicability of the algorithm are proved. [ABSTRACT FROM AUTHOR]

Cloud computing plays a significant role in effective data handling based on the increase in data usage in various realtime applications. Data auditing is performed on certain files and the authenticator with deduplication. It addresses the problem of key management to deduce the file content based on the malicious activities performed on the cloud. So, based on effective auditing of the integrity of the data and authenticator, the data is checked properly and minimizes the overhead of cloud storage overhead. In this work, the cloud audit and authenticator approach is proposed based on a certain file system, which makes the malicious user get authenticate the data auditing verification as the existing algorithm has low security based on entropy. They propose a data auditing approach that integrates with file management and the authenticator of data deduplication. The proposed approach performs the authenticator process and new form of file tag, which helps guarantee effective security based on the random generation of message key. The proposed approach achieves minimum computational overhead based on the authenticator and data block generation in the performance analysis. Then the security verification is performed on various attacks, such as brute force attack, a man-in-the-middle attack, etc., to check whether the approach is safe or unsafe against the attacks. [ABSTRACT FROM AUTHOR]

Mobile network operators store an enormous amount of information like log files that describe various events and users' activities. Analysis of these logs might be used in many critical applications such as detecting cyber attacks, finding behavioral patterns of users, security incident response, and network forensics. In a cellular network, call detail records (CDRs) is one type of such logs containing metadata of calls and usually includes valuable information about contacts such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call), and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper's primary goal is to study subscribers' behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and neural network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies. [ABSTRACT FROM AUTHOR]

In file systems, a single write system call can make multiple modifications to data and metadata, but such changes are not flushed in an atomic way. To retain the consistency of file systems, conventional approaches guarantee crash consistency in exchange for sacrificing system performance. To mitigate the performance penalty, non-volatile memory (NVM) technologies are believed to be a good candidate for this purpose owing to their low latency and byte-addressibility. However, none of the prior proposals that exploit NVM provide both scalability and strict ordering of modifications. In this article, we propose montage, a crash consistency framework for file systems that consists of two parts. First, montage splits NVM space into multiple staging areas and synchronizes the flushing of modifications these to the storage device. Second, montage uses the pipelined architecture to speed up data flushing to storage. We apply montage to two journaling file systems (ext4 and JFS) and evaluate them on a multicore server with high-performance storage. The evaluation results demonstrate that our design exhibits better performance by a wide margin than recent NVM-based journaling file systems. [ABSTRACT FROM AUTHOR]

The proprietary software investments in the data integration field are incrementing, and the progresses are visible in the possibility to directly open in a GIS environment a 3D software data format. Still, this is limited to the integration between the proprietary data formats and standards, ArcGIS environment shapefile multipatch and Revit 3D model, by using a proprietary software (ArcGIS). This study takes advantage of the lesson-learnt results in the proprietary data integration field, wanting to replicate a similar result using the IFC open standard, which is not directly openable by a GIS interface and needs to overcome a conversion that in most of the cases leads to semantic and geometric losses. So, an IFC-to-shapefile data conversion was performed, stressing (i) the way information is stored in the attribute table to query the geometries and perform geoprocessing, by (ii) implementing workarounds to keep the Revit instances' shared parameters in the IFC file, (iii) meanwhile having a high Level of Detail of the HBIM. The research performed the IFC-to-shapefile data conversion through FME (Feature Manipulation Engine), benefitting of the flexibility of the shapefile format and of the IFC' possibility to keep a high LOD in the export phase. Both allowed to properly query and manage the elements of an HBIM in a GIS (ArcGIS environment), and, using relational attributes table, retrieve the information contained in each Revit instance' property panel, as the shared parameters that implement the BIM Level of Information (LOI). [ABSTRACT FROM AUTHOR]

The IEEE P2731 working group is in the process of developing a functional model so that virtually any BCI system could be described according to it. In this paper, we want to stimulate the discussion for the definition and selection of the information that should be stored in a file for effectively allowing the sharing of BCI data and tools amongst researchers. Establishing these requirements and procedures will accelerate BCI development and lay the foundation for accessible and scalable BCI technology as well as provide the foundations for the definition of a standard file format. [ABSTRACT FROM AUTHOR]

Throughout the ages, safely preserving and transmitting data that have extraordinary importance for humanity has increased its importance with rapid advances in computer technology. Steganography stores hidden data within the files, which are unnoticed by third parties, so it provides secure transmission of data to the receiver. In this study, a steganography-based GUI design has been carried out, which ensures that the agricultural data is safely stored and communicated to the other party. We used LSB one-bit, two-bit, three-bit substitution and PVD algorithms with GUI for stages of agricultural data hiding and extracting at cover images. We also provided extra security using the embedded key and shifting operations on the hidden data before hiding data the cover image. In short, we confused the hidden data in the cover image so that malicious people can't understand. In experimental studies, performance analysis was evaluated by comparing various criteria as similarity ratio (Structural Similarity Index Measure, SSIM), stego image quality (Peak Signal-to-Noise Ratio, PSNR) and data hiding capacity (Payload). [ABSTRACT FROM AUTHOR]

Mitchell-Schuitevoerder, Rosemary 2020 A Project-Based Approach to Translation Technology London and New York Routledge xxxv, 164 9780367138844 I A Project-Based Approach to Translation Technology i is probably the most updated, adaptable, and accessible resource for translators to effectively implement translation technologies in every step of the translation workflow. Chapter 1 covers the basic principles of CAT tools and discusses how translators, language service providers (LSPs) and clients interact with these tools. Mitchell-Schuitevoerder not only mentions computer literacy skills as essential for learning about CAT tools but specifically highlights how these skills can ensure a successful CAT tool operation. Chapter 3 deals with the integration of machine translation (MT) into CAT tools and focuses on the efficient use of MT in the translation workflow. [Extracted from the article]

HDFS is a popular open-source system for scalable and reliable file management, which is designed as a general-purpose solution for distributed file storage. While it works well for medium or large files, it will suffer heavy performance degradations in case of lots of small files. To overcome this drawback, we propose here a system to enhance HDFS with a distributed true full-text search system SAES of 100% recall and precision ratios. By indexing the meta data of each file, e.g., name, size, date and description, files can be quickly accessed by efficient searches over metadata. Moreover, by merging many small files into a large file to be stored with better space and I/O efficiencies, the negative performance impacts caused by directly storing each small file individually are avoided. An experimental study is conducted for function and performance tests on both realistic and artificial data. The experimental results show that the system works well for file operations such as uploading, downloading and deleting. Moreover, the RAM consumption for managing massive small files is dramatically reduced, which is critical for good system performance. The proposed system could be a potential storage solution for massive small files. [ABSTRACT FROM AUTHOR]

The Program is a mediator as all IDEs are mediator programs, in the sense that IDEs like Eclipse, Eric IDE, and net beans are considered as a mediator between a programmer and Interpreter or the programming language compiler. In fact, in this paper we can say that all things the programs do can be done with a simple text editor and we can see the Program output by direct contact with interpreter or compiler. The only difference between the Program provided by me and other IDEs is that the environment is special for building the extension. It means that instead of being an interface between user and interpreter or compiler, standards provided by other applications are usually different. The program tries to fill in the absence of an Efficient IDE in the field of building an extension. On the other hand, it reduces the cost of extension standard providers for presenting an IDE specific to that standard. In fact, in the second target the program provided will be a platform that has the ability to mount the next standards. Standard providers should only conduct a Part of coding. A constant part is common in all standards. We should find a solution for them in all IDEs, such as file management and information management that have been implemented in constant part of the IDE. The variable and non-similar part is implemented as classes. If needed to implement, it should be inherited from them and some of their functions must be overridden. [ABSTRACT FROM AUTHOR]

Various techniques have been used in distributed file systems for data availability and stability. Typically, a method for storing data in a replication technique-based distributed file system is used, but due to the problem of space efficiency, an erasure-coding (EC) technique has been utilized more recently. The EC technique improves the space efficiency problem more than the replication technique does. However, the EC technique has various performance degradation factors, such as encoding and decoding and input and output (I/O) degradation. Thus, this study proposes a buffering and combining technique in which various I/O requests that occurred during encoding in an EC-based distributed file system are combined into one and processed. In addition, it proposes four recovery measures (disk input/output load distribution, random block layout, multi-thread-based parallel recovery, and matrix recycle technique) to distribute the disk input/output loads generated during decoding. [ABSTRACT FROM AUTHOR]

The infectious disease prevention information system has the problems that data is difficult to circulate and share between monitoring systems.To solve these problems,this paper proposed an infectious disease data sharing model based on delegated proof of stake consensus algorithm combined with blockchain technology.By virtue of the characteristics of the blockchain,such as decentralization,non-tampering and collective maintenance,it used double-chain as the blockchain architecture,which improved the work efficiency.Combined with inter-planetary file system,it obtained large capacity storage space,which solved the space problem faced by block data storage and ensured the stability and sharing security of data storage.This paper proposed a consensus algorithm for disease prevention and control to realize the efficient operation.In addition,by comparing with other data sharing models,the advantages of this model in data storage and sharing security are highlighted. [ABSTRACT FROM AUTHOR]

Summary: In recent years, Docker container technology is being applied in the field of cloud computing at an explosive speed. The scheduling of Docker container resources has gradually become a research hotspot. Existing big data computing and storage platforms apply with traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. In this paper, we propose an improved container scheduling algorithm for big data applications named Kubernetes‐based particle swarm optimization(K‐PSO). Experimental results show that the proposed K‐PSO algorithm converges faster than the basic PSO algorithm, and the running time of the algorithm is cut in about half. The K‐PSO container scheduling algorithm and algorithm experiment for big data applications are implemented in the Kubernetes container cloud system. Our experimental results show that the node resource utilization rate of the improved scheduling strategy based on K‐PSO algorithm is about 20% higher than that of the Kube‐scheduler default strategy, balanced QoS priority strategy, ESS strategy, and PSO strategy, while the average I/O performance and average computing performance of Hadoop cluster are not degraded. [ABSTRACT FROM AUTHOR]

As a popular consortium blockchain platform, Hyperledger Fabric has received increasing attention recently. When executing transactions on such platform, it usually costs a lot of time and hardly to achieve high efficiency. Although efficiently handling transactions can be leveraged to support various use-cases, it presents significant challenges as data on Hyperledger Fabric is organized on file-system and exposed via limited API. We tackle the problem in two ways: conditional queries and data storage. In this paper, we propose the following novel methods. To improve the performance of conditional queries on Hyperledger Fabric, we use all attributes of the query to create composite keys before executing it. In order to achieve further performance improvements, we build an index called AUP in the second method, where we also study the update of AUP during transactions. To speed up data storage on Hyperledger Fabric, We create a cache for the data in the block header. The extensive experiments conducted on the real-world dataset demonstrate that the proposed methods can achieve high performance in terms of efficiency and memory cost. Finally, We implement a prototype system. [ABSTRACT FROM AUTHOR]

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper entitled Centralized E-document Repository System (CERS) is developed and designed to manage documents and records repositories in an organization to provide better office productivity in both file management, sharing, and distribution that does not compromise the security of the document. An organization that provides efficient and effective file management will result in a better and more productive person who will only search, access, read, modify and store documents and records within their workplace instead of consuming and burning their time roaming around and consolidating one's documents and records. The system will not only provide a time-efficient for personnel in each office but also minimize a spaceconsuming file cabinet and storage that sometimes provides a difficult time accessing and retrieving files in a well-mannered process. With the integration of a database provided by the system, users can assure a better storage space for their files with enhanced security and a better backup and disaster recovery for both documents and records. [ABSTRACT FROM AUTHOR]

Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly seen in many areas such as genetics, genomics, biomedical imaging, and high-frequency finance. In this research, we implement an R package called biglasso that tackles this challenge. biglasso utilizes memory-mapped files to store the massive data on the disk, only reading data into memory when necessary during model fitting, and is thus able to handle out-of-core computation seamlessly. Moreover, it's equipped with newly proposed, more efficient feature screening rules, which substantially accelerate the computation. Benchmarking experiments show that our biglasso package, as compared to existing popular ones like glmnet, is much more memory-and computation-efficient. We further analyze a 36 GB simulated GWAS data set on a laptop with only 16 GB RAM to demonstrate the out-of-core computation capability of biglasso in analyzing massive data sets that cannot be accommodated by existing R packages. [ABSTRACT FROM AUTHOR]

This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. To accommodate current researchers, a section giving descriptions of publicly available datasets is included, outlining their characteristics and shortcomings when used for IDS evaluation. Related surveys are organized and described. All sections are accompanied by tables concisely organizing the literature and datasets discussed. Finally, challenges, trends, and broader observations are throughout the survey and in the conclusion along with future directions of IDS research. Overall, this survey was designed to allow easy access to the diverse types of data available on a host for sensing intrusion, the progressions of research using each, and the accessible datasets for prototyping in the area. [ABSTRACT FROM AUTHOR]

Two studies explore whether people's digital filing behaviors are affected by emotional factors when engaging in personal information management (PIM). Cognitive science research shows that people's information categorization behaviors are mood-dependent, so that positive moods induce larger, more inclusive organizational categories, whereas negative moods elicit more fine-grained organization. However, such mood-dependent organization has not been directly studied in the context of PIM. Our first, naturalistic study examines relations between people's overall filing habits and a personality trait, neuroticism, which is commonly associated with negative mood. Our results reveal the expected mood-dependency effects; participants who report more prevalent negative mood patterns when surveyed, also create different organizational structures. Overall, they make more folders that contain fewer files and store these in deeper folder structures. The second, experimental study directly manipulated mood and examined how this affects organization of a controlled file collection. Again as predicted, participants experiencing negative moods created significantly more folders containing fewer files, and there was a trending effect of negative mood on folder depth. We discuss theoretical and practical implications for PIM arising from these novel results. [ABSTRACT FROM AUTHOR]

In the era of data-intensive computing, large-scale applications, in both scientific and the BigData communities, demonstrate unique I/O requirements leading to a proliferation of different storage devices and software stacks,many ofwhich have conflicting requirements. Further, newhardware technologies and system designs create a hierarchical composition that may be ideal for computational storage operations. In this article, we investigate how to support a wide variety of conflicting I/O workloads under a single storage system. We introduce the idea of a Label, a new data representation, and, we present LABIOS: a new, distributed, Labelbased I/O system. LABIOS boosts I/O performance by up to 17 x via asynchronous I/O, supports heterogeneous storage resources, offers storage elasticity, and promotes in situ analytics and software defined storage support via data provisioning. LABIOS demonstrates the effectiveness of storage bridging to support the convergence of HPC and BigData workloads on a single platform. [ABSTRACT FROM AUTHOR]

The aim of the present paper is to provide an overview of the recently introduced root canal single file preparation system, One Shape (Micro Méga, Besançon, France). In contrast to some other single file systems, the One Shape file is used in continuous rotation, as known from many former multiple file rotary NiTi systems for root canal preparation. The unique design of the One Shape instrument incorporates a variety of different cross sections along the active length of the file, which offers an optimal and improved cutting action in three zones of the root canal. Each instrument has been electropolished to enhance cutting efficiency. One Shape's flexibility and unique downward movement ensures a highly effective apical progression. Minimal fatigue along the length of the entire file virtually eliminates the risk of accidental instrument separation. The One Shape instrument is actually available with a safety tip in size 25 at the tip with a continuous 0.06 taper. It is delivered in a sterile blister, as done by many other manufacturers, and should be used for one tooth only and then discarded. It is designed for a maximum of three or four canals in one tooth and should not be sterilised, because the cutting efficiency decreases severely. Practitioners can use their existing endodontic motor with the One Shape instrument. Conclusions: One Shape is optimally designed to prepare the majority of root canal configurations. One Shape is one of the few single file instruments used in continuous clockwise rotation for a quick and probably safe root canal preparation. [ABSTRACT FROM AUTHOR]

Great progress has been made over the years in the technology of rotary nickel-titanium file systems. Nevertheless, both the oldest and the newest systems all use the same principle of a rotating blade with flutes, designed to carry off or contain the cut material and/or debris. While this concept may provide adequate results in narrow, straight canals with round cross sections, it has substantial limitations when oval or curved canals are concerned. Such canals are very common. The Self-Adjusting File (SAF) system is based on a new and different concept that is aimed at overcoming the limitations of current rotary file technology. This review aims to familiarise the reader with the new concept and technology of the SAF system and to individually discuss the major endodontic challenges that are yet to be conquered by rotary file technology. The ways by which the new SAF system overcomes these challenges will be explained, based on 32 research papers that were published over the past 3 years. A new concept for root canal cleaning and shaping will be presented and justified: the concept of 'minimally invasive endodontics', which has been made possible by the new SAF technology. [ABSTRACT FROM AUTHOR]

In 2011 the Reciproc System was introduced to the market by VDW (Munich, Germany) as a single file reciprocating system. It is claimed that root canals can be completely prepared with only one Reciproc instrument. This case series describes the treatment of four teeth needing root canal treatment by using the Reciproc System. The illustrated cases show that root canal treatment as well as endodontic retreatment can be safely carried out with Reciproc instruments. [ABSTRACT FROM AUTHOR]

Root canal preparation with only one nickel-titanium instrument used in reciprocation has been recently introduced. New endodontic systems using this concept, such as Reciproc and WaveOne, have also been developed. The purpose of this review was to identify publications regarding the evaluation, to present comprehensive and critical summaries of current knowledge, and to provide an update of the single file reciprocating concept. [ABSTRACT FROM AUTHOR]

Small‐angle X‐ray scattering (SAXS) is a widely used method for investigating biological macromolecules in structural biology, providing information on macromolecular structures and dynamics in solution. Modern synchrotron SAXS beamlines are characterized as high‐throughput, capable of collecting large volumes of data and thus demanding fast data processing for efficient beamline operations. This article presents a fully automated and high‐throughput SAXS data analysis pipeline, SAS‐cam, primarily based on the SASTBX package. Five modules are included in SAS‐cam, encompassing the data analysis process from data reduction to model interpretation. The model parameters are extracted from SAXS profiles and stored in an HTML summary file, ready for online visualization using a web browser. SAS‐cam can provide the user with the possibility of optimizing experimental parameters based on real‐time feedback and it therefore significantly improves the efficiency of beam time. SAS‐cam is installed on the BioSAXS beamline at the Shanghai Synchrotron Radiation Facility. The source code is available upon request. [ABSTRACT FROM AUTHOR]

In this paper, a blockchain-based secure data sharing mechanism is proposed for Vehicular Networks (VNs). Edge service providers are introduced along with ordinary nodes to efficiently manage service provisioning. The edge service providers are placed in the neighborhood of the ordinary nodes to ensure smooth communication between them. The huge amount of data generated by smart vehicles is stored in a distributed file storage system, known as Interplanetary File System (IPFS). It is used to tackle the issues related to data storage in centralized architectures, such as data tampering, lack of privacy, vulnerability to hackers, etc. Monetary incentives are given to edge vehicle nodes to motivate them for accurate and timely service provisioning to ordinary nodes. In response, ordinary nodes give reviews to the edge nodes against the services provided by them, which are further stored in a blockchain to ensure integrity, security and transparency. Smart contracts are used to automate the system processes without the inclusion of an intermediate party and to check the reviews given to the edge nodes. To optimize gas consumption and to enhance the system performance, a Proof of Authority (PoA) consensus mechanism is used to validate the transactions. Moreover, a caching system is introduced at the edge nodes to store frequently used services. Furthermore, both security and privacy are enhanced in the proposed system by incorporating a symmetric key cryptographic mechanism. A trust management mechanism is also proposed in this work to calculate the nodes' reputation values based upon their trust values. These values determine the authenticity of the nodes involved in the network. Eventually, it is concluded from the simulation results that the proposed system is efficient for VNs. [ABSTRACT FROM AUTHOR]

Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 "Challenges and Opportunities of User-Level File Systems for HPC" and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices. [ABSTRACT FROM AUTHOR]

With the convergence of high-performance computing (HPC), big data and artificial intelligence (AI), the HPC community is pushing for "triple use" systems to expedite scientific discoveries. However, supporting these converged applications on HPC systems presents formidable challenges in terms of storage and data management due to the explosive growth of scientific data and the fundamental differences in I/O characteristics among HPC, big data and AI workloads. In this paper, we discuss the driving force behind the converging trend, highlight three data management challenges, and summarize our efforts in addressing these data management challenges on a typical HPC system at the parallel file system, data management middleware, and user application levels. As HPC systems are approaching the border of exascale computing, this paper sheds light on how to enable application-driven data management as a preliminary step toward the deep convergence of exascale computing ecosystems, big data, and AI. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the general trends in the area of dividend policy which will help to identify fertile research streams in this area with a potential for further investigation. Design/methodology/approach: To conduct a systematic literature review, the authors use a three-step methodology to collect resources and thus evaluate the research work done in the area of dividend policy. First, the necessary data are extracted from the Scopus database using the relevant keywords. The initial search results are then narrowed down to include only English language journal publications which are stored in the file. Finally, this file is used as primary data for data analysis. Data analysis is done using bibliometric and network analysis tools to recognize the trends in dividend policy to help researchers identify emergent areas for future work to be done. Findings: This study reveals that research in the area of dividend policy is rapidly expanding since 2005; affiliation statistics show that majority of the publications are done in the USA and the UK; and many questions linked to dividend decision remain unanswered, especially in respect of emerging markets. Originality/value: There is a need to organize the literature and understand the different areas that have been explored by many researchers. This study attempts to recognize the important research studies, determine the current areas of research attention, provide an understanding for current research interest and provide guidelines for future studies in the area of dividend policy. [ABSTRACT FROM AUTHOR]

Background: Recent advances in genome sequencing technologies and the cost drop in high-throughput sequencing continue to give rise to a deluge of data available for downstream analyses. Among others, evolutionary biologists often make use of genomic data to uncover phenotypic diversity and adaptive evolution in protein-coding genes. Therefore, multiple sequence alignments (MSA) and phylogenetic trees (PT) need to be estimated with optimal results. However, the preparation of an initial dataset of multiple sequence file(s) (MSF) and the steps involved can be challenging when considering extensive amount of data. Thus, it becomes necessary the development of a tool that removes the potential source of error and automates the time-consuming steps of a typical workflow with high-throughput and optimal MSA and PT estimations. Results: We introduce LMAP_S (Lightweight Multigene Alignment and Phylogeny eStimation), a user-friendly command-line and interactive package, designed to handle an improved alignment and phylogeny estimation workflow: MSF preparation, MSA estimation, outlier detection, refinement, consensus, phylogeny estimation, comparison and editing, among which file and directory organization, execution, manipulation of information are automated, with minimal manual user intervention. LMAP_S was developed for the workstation multi-core environment and provides a unique advantage for processing multiple datasets. Our software, proved to be efficient throughout the workflow, including, the (unlimited) handling of more than 20 datasets. Conclusions: We have developed a simple and versatile LMAP_S package enabling researchers to effectively estimate multiple datasets MSAs and PTs in a high-throughput fashion. LMAP_S integrates more than 25 software providing overall more than 65 algorithm choices distributed in five stages. At minimum, one FASTA file is required within a single input directory. To our knowledge, no other software combines MSA and phylogeny estimation with as many alternatives and provides means to find optimal MSAs and phylogenies. Moreover, we used a case study comparing methodologies that highlighted the usefulness of our software. LMAP_S has been developed as an open-source package, allowing its integration into more complex open-source bioinformatics pipelines. LMAP_S package is released under GPLv3 license and is freely available at https://lmap-s.sourceforge.io/. [ABSTRACT FROM AUTHOR]

Metadata performance in distributed file systems (DFS) is critical, due to the following trends: (a) the growing size of modern storage systems is expected to exceed billions of files and most files are small; (b) over half of the file accesses are metadata operations. In this work, we present SMeta, a metadata prefetching method that is seamlessly integrated into DFS for easy-of-use and significantly scales the metadata performance. Previous prefetching proposals primarily focus on mining groups of files that tend to be accessed together from the access history. Nevertheless, our study discovered that these solutions likely miss a huge number of correlated files whose co-occurrence frequency is not high enough. Unlike access correlations, we take a novel and completely different approach to explore explicit data correlations by understanding the reference relationships between files encoded in some forms of hyperlinks, which naturally exist in many applications. To embrace this new concept, SMeta explores correlations upon files are written via a light-weight pattern matching algorithm, stores correlations in the reserved extended attributes of file metadata to avoid changes in DFS APIs, and collapses multiple I/O rounds for accessing metadata of the target file and its data-correlated files into one round. A cost-efficient adaptive feedback mechanism is introduced to improve prefetching accuracy. We implemented SMeta atop of Ceph and evaluated it using synthetic and real system workloads. Compared to baselines, SMeta provides better metadata performance in terms of latency, throughput and scalability. [ABSTRACT FROM AUTHOR]

• We introduce a semantic prediction loss to learn effective cross-modal hash codes. • We propose cross-modal attention block to extract more semantic-rich cues for cross-modal samples. • Experiments on three benchmarks show the effectiveness of our proposed method. Cross-modal hashing has received intensive attention due to its low computation and storage efficiency in cross-modal retrieval task. Most previous cross-modal hashing methods mainly focus on extracting correlated binary codes from the pairwise label, but largely ignore the semantic categories of cross-modal data. On the other hand, human perception exploits category information to connect cross-modal samples. Inspired by this fact, we propose to embed category information into hash codes. More specifically, we introduce semantic prediction loss into our framework to enhance hash codes with category supervision. In addition, there always exists a large gap between features from different modalities (e.g. text and images), leading cross-modal hashing to link irrelevant features for retrieval task. To address this issue, this paper proposes Dual-Supervised Attention Network for Deep Hashing (DSADH) to learn the cross-modal relationship via an elaborately-designed attention mechanism. Our cross-modal network applies cross-modal attention block to efficiently encode rich and relevant features to learn compact hash codes. Extensive experiments on three challenging benchmarks demonstrate that our proposed method significantly improves the retrieval results. [ABSTRACT FROM AUTHOR]