Replica Of Data Research Articles

Quantum force sensing by digital twinning of atomic Bose-Einstein condensates

High sensitivity detection plays a vital role in science discoveries and technological applications. While intriguing methods utilizing collective many-body correlations and quantum entanglements have been developed in physics to enhance sensitivity, their practical implementation remains challenging due to rigorous technological requirements. Here, we propose an entirely data-driven approach that harnesses the capabilities of machine learning, to significantly augment weak-signal detection sensitivity. In an atomic force sensor, our method combines a digital replica of force-free data with anomaly detection technique, devoid of any prior knowledge about the physical system or assumptions regarding the sensing process. Our findings demonstrate a significant advancement in sensitivity, achieving an order of magnitude improvement over conventional protocols in detecting a weak force of approximately 10−25N. The resulting sensitivity reaches 1.7(4)×10−25N/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.7(4)\ imes 1{0}^{-25}\\,{{{{{{{\\rm{N}}}}}}}}/\\sqrt{{{{{{{{\\rm{Hz}}}}}}}}}$$\\end{document}. Our machine learning-based signal processing approach does not rely on system-specific details or processed signals, rendering it highly applicable to sensing technologies across various domains.

Selecting an optimum kinetic model for gas–solid adsorption based on statistical approaches and a model selection criterion

Adsorption heat transformation (AHT) techniques can help to safeguard the environment by reducing the use of fossil fuels. When constructing an adsorption-based dynamic heat pump system, analyzing the kinetic characteristics of the adsorbate–adsorbent pair is important. The recently established time-adapted linear driving force (LDF) and commonly used models such as LDF, modified LDF, and Fickian diffusion (FD) models are available in the literature for analyzing the kinetic characteristics, making it challenging for researchers to select the optimum one for simulating the complete real system. This study applies a non-parametric statistical approach known as bootstrapping for simulating the replica of experimental kinetics data for all studied pairs. The kinetics models mentioned above are used to correlate the bootstrap samples. The estimated values of the statistical parameters are compared, and the optimum kinetics model for gas–solid physical adsorption is proposed. The parameters of the model are estimated using the generalized reduced gradient (GRG) non-linear optimization method. For each bootstrap sample, the optimum model is selected based on the minimum values, which means less information loss, of a set of information-based model selection criteria (ICs). The time-adapted LDF incurs smaller values of IC compared to other kinetics models. The p-value of the proportion test for water adsorption onto AQSOA-Z01, RD silica gel, and Al-Fum is very small, which is less than 0.01. So, the test is significant at a 1% level, which implies that time adapted LDF model is significantly optimum. This is the first time that a rigorous statistical optimization technique has been used to identify the optimum kinetic model for gas–solid adsorption. The current findings are critical for a comprehensive analysis of an adsorption system and its design.

EBSF: Node Characteristics-Based Block Allocation Plans for Efficient Blockchain Storage

The heavy storage problem has become a key obstacle to the application of blockchain to the actual business environments, because each node needs to keep a complete replica of blockchain data. The data volume grows undesirably large in practice. It prevents the widely used devices, e.g., tablets and mobile phones, to join blockchain systems due to their limited storage and computing resource. Previous work addressed the storage issue by allowing participating nodes to only keep a fraction of the entire transaction set, e.g., sharding. However, existing studies focus on transaction placement with the minimum cross-shard communications. These studies neglect the node characteristics (e.g., storage capacity, cost, and response capability), which impacts the storage performance adversely. In this paper, we propose EBSF, a block storage framework that achieves efficient block storage by constructing a block allocation plan based on node characteristics. Blockchain nodes are organized into committees such that nodes in a committee work together to maintain the entire blockchain data. We formulate the block allocation plan problem that assigns each block to at least one node in a committee. The goal is to minimize the total cost while reaching the threshold of the response capability of each block. We prove the NP-hardness of the problem and propose heuristic algorithms. We also propose two strategies to handle the dynamic scenario of new blocks. Extensive evaluation shows the efficiency and effectiveness of the proposed framework.

LRFM model for customer purchase behaviour using K-Means algorithm

The COVID-19 pandemics have a major collision on every aspect of life, including how people shop for their requirements. As the pandemic has reshaped life as we know, it’s also initiated many trends – but the biggest of these trends may be online shopping. The shift toward online shopping was happening before the pandemic, but according to new statistics from IBM, the COIVD-19 has accelerated consumers shift toward online shopping by 5 years. The chief idea of the article is to inspect if the situation is approaching people to purchase things online and the continuation of shopping things online even after the end of pandemic. The information for the article has been gathered by circulating the survey on social networks. The questionnaire is comprised of 12 different questions, and 615 people responded to it. This work is based on LRFM (Length, Recency, Frequency, and Monetary) replica and separation of data based on the questionnaire using K-Means algorithm. Silhouette analysis helps to decide the extent of division among clusters. The results of the survey has a termination that people are fond of purchasing products online through the lockdown and people too agreed that the rate of online shopping will increase in the future when this pandemic is over.

Image Compression with LZW (Lossless) On Different Image Formats

Image compression is used to reduce bandwidth or storage requirement in image application. Mainly two types of image compression: lossy and lossless image compression. A Lossy Image Compression removes some of the source information content along with the redundancy. While the Lossless Image Compression technique the original source data is reconstructed from the compressed data by restoring the removed redundancy. The reconstructed data is an exact replica of the original source data. Many algorithms are present for lossless image compression like Huffman, rice coding, run length, LZW. LZW is referred to as a substitution or dictionary-based encoding algorithm. The algorithm builds a data dictionary of data occurring in an uncompressed data stream. Patterns of data (substrings) are identified in the data stream and are matched to entries in the dictionary. If the substring is not present in the dictionary, a code phrase is created based on the data content of the substring, and it is stored in the dictionary. The phrase is then written to the compressed output stream. In this paper we see the effect of LZW algorithm on the png, jpg, png, gif, bmp image formats.

A Survey on Datacenter Placement and Replication Approach for Cloud

Cloud computing is a trending technology due to its high availability and services. Cloud provides a platform for the large set of users to put their data and host for their processing requirements. But the main challenge is to provide a reliable storage for the users. To provide reliability cloud uses concept of replication in datacenters. But in case of natural calamity it is critical to save at least one replica of data; which will not be possible if all replicas are made in disaster prone locations. Apart from this network usage, energy consumption and service level agreement (SLA) should also be kept in mind. In this paper a survey is done on intelligent replication techniques which will keep all performance parameters in consideration. There is SLA violation and service can be improved considerably if data placement and replication algorithms are better.

Intensive Convergence Encryption for Secure Deduplication in Cloud

Deduplication is used for eliminating the duplicate copy of same data and providing security to them. Replica of same data is obtained by encrypting the same information with different key. Surplus amount of data is being stored in the cloud environment to shrink the data in storage space convergence encryption technique is used. In convergence encryption technique the key will be generated from the file by the hash value so that same file will turn out same cipher text. When the cipher text is indistinguishable it will be identified as a duplicate. The main weak spot of the convergence encryption comes from the fact that key for a given data part can be generated by anyone in a deterministic way. This allows an attacker to easily recuperate the plain text from the key so it critically compromises user’s confidentiality. In this paper encrypted text by convergence encryption is made complicated by the bit reversal and RC4 algorithm. so that the data will be more secure in the cloud storage and prevented from the attacker. Deduplication manager will be used for the purpose of detecting the duplicate file and manage the keys. By this even though the Attacker knows the cipher text he cannot obtain the original text from encrypted data it will make the encryption more efficient and make the data secure.

A Fault-Tolerant Mobile Computing Model Based On Scalable Replica

The most frequent challenge faced by mobile user is stay connected with online data, while disconnected or poorly connected store the replica of critical data. Nomadic users require replication to store copies of critical data on their mobile machines. Existing replication services do not provide all classes of mobile users with the capabilities they require, which include: the ability for direct synchronization between any two replicas, support for large numbers of replicas, and detailed control over what files reside on their local (mobile) replica. Existing peer-to- peer solutions would enable direct communication, but suffers from dramatic scaling problems in the number of replicas, limiting the number of overall users and impacting performance. Roam is a replication system designed to satisfy the requirements of the mobile user. Roam is based on the Ward Model, replication architecture for mobile environments. Using the Ward Model and new distributed algorithms, Roam provides a scalable replication solution for the mobile user. We describe the motivation, design, and implementation of Roam and report its performance. Replication is extremely important in mobile environments because nomadic users require local copies of important data. Model, a new replication architecture that has been designed especially with mobility in mind, though of course it applies equally well to stationary environments (1). The Ward Model provides a new replication paradigm that is neither strictly peer nor client-server; rather, it is a hybrid model of the two that allows everyone to be effectively a peer while maintaining good scalability in the number of replicas. Additionally, Roam provides a number of new distributed algorithms. For instance, Roam contains new and improved distributed algorithms for garbage collection and for dynamic management of the version vector, the main data structure behind most optimistic replication systems. In this paper we present an approach for data replication that considers the mobility of clients: The data sets which have to be replicated to mobile clients depend often on dynamic parameters like location of the client or time. Therefore, we introduce a generic model for specifying fragments of the global database with respect to such parameters. The increasing availability of mobile devices and wireless

Exploiting the relationships among several binary classifiers via data transformation

The structural resemblance among several existing classifiers has motivated us to investigate their underlying relationships. By exploring into the mapping solutions of these classifiers, we found that they can be linked by simple feature data scaling. In other words, the key to these relationships lies upon how the replica of feature data are being scaled. This finding leads us directly to an exploration of novel classifiers beyond existing settings. Based on an extensive empirical evaluation, we show that the proposed formulation facilitates a tuning capability beyond existing settings for classifier generalization.

Review of Data Replication Techniques for Mobile Computing Environment

With the tremendous growth of the portable computing devices and wireless communication, the mobile computing has become extremely popular. The wireless enabled portable computing devices with massive storage capacity and high-end processing capabilities have started to make the extensive use of mobile databases already. The rising popularity in these computing paradigms demands that the mobile computing be reliable enough to ensure the continuous data availability and integrity. However mobility comes at the cost of bandwidth, limited power, security and interference. In modern mobile computing systems data replication has been adopted as an efficient means to ensure the data availability and integrity as well as an effective means to achieve the fault tolerance. Data replication not only ensure the availability of the data but also reduce the communication cost, increase data sharing and increase the safety of the critical data. Furthermore, it also determine when and where (location) to place the replica of data, controlling the number of data replicas over a network for efficient utilization of the network resources. In this study we survey the research work in data replication for mobile computing. We reviewed some of the existing data replication techniques proposed by the research community for mobile computing environment for efficient management of data replicas.

Normal approximation through data replication when estimating DisPMO, DePMO, left‐side and right‐side Sigma levels from non‐normal data

PurposeThis paper seeks to present a way of estimating DisPMO, DePMO, left‐side and right‐side Sigma levels (as the “mutations” of DPMO and Sigma level when applied on customer satisfaction measurements), where all critical attributes (CTQs) contain data sets that are non‐normally distributed.Design/methodology/approachThe calculation of DisPMO, DePMO, left‐side and right‐side Sigma levels is based on dynamic‐multiple CTQs without the need for assuming 1.5 Sigma shift from the mean. Using step‐wise multiple regression, CTQs are then the attributes that significantly influence overall customer satisfaction. This further developed method no longer takes normality assumption for granted, which means that, prior to calculating DisPMO, DePMO, left‐side and right‐side Sigma levels, the data should be proven as being normally distributed. To fulfil the assumption of normality, the primary data are being “replicated” by first generating random numbers that follow normal standard distribution and then adjusting (re‐calculating) these random numbers with the mean, standard deviation, and the skewness of the primary data. Simulation technique is then applied to generate a larger amount of secondary data as the basis for estimating DisPMO, DePMO, left‐side and right‐side Sigma levels.FindingsThe application of the method in a Swedish house‐building construction project suggests that: the use of multiple CTQs may reduce the risk for under‐/overestimation of Sigma levels, and DisPMO and DePMO are each other's “mirror” and both of them should be considered when calculating Sigma levels. The calculated Sigma levels suggest that the developer's performance is still quite far below Six Sigma level of performance.Originality/valueUsing the replica of the primary data as a way of approaching normality may be regarded as the main contribution of the paper in addressing one of the challenges in Six Sigma theory.

Benchmarks and implementation of the ALICE high level trigger

The ALICE High Level Trigger combines and processes the full information from all major detectors in a large computer cluster. Data rate reduction is achieved by reducing the event rate by selecting interesting events (software trigger) and by reducing the event size by selecting sub-events and by advanced data compression. Reconstruction chains for the barrel detectors and the forward muon spectrometer have been benchmarked. The HLT receives a replica of the raw data via the standard ALICE DDL link into a custom PCI receiver card (HLT-RORC). These boards also provide a FPGA co-processor for data-intensive tasks of pattern recognition. Some of the pattern recognition algorithms (cluster finder, Hough transformation) have been re-designed in VHDL to be executed in the Virtex-4 FPGA on the HLT-RORC. HLT prototypes were operated during the beam tests of the TPC and TRD detectors. The input and output interfaces to DAQ and the data flow inside of HLT were successfully tested. A full-scale prototype of the dimuon-HLT achieved the expected data flow performance. This system was finally embedded in a GRID-like system of several distributed clusters demonstrating the scalability and fault-tolerance of the HLT.

Efficient topology-aware overlay network

Peer-to-peer (P2P) networking has become a household word in the past few years, being marketed as a work-around for server scalability problems and "wonder drug" to achieve resilience. Current widely-used P2P networks rely on central directory servers or massive message flooding, clearly not scalable solutions. Distributed Hash Tables (DHT) are expected to eliminate flooding and central servers, but can require many long-haul message deliveries. We introduce Mithos, an content-addressable overlay network that only uses minimal routing information and is directly suitable as an underlay network for P2P systems, both using traditional and DHT addressing. Unlike other schemes, it also efficiently provides locality-aware connectivity, thereby ensuring that a message reaches its destination with minimal overhead. Mithos provides for highly efficient forwarding, making it suitable for use in high-throughput applications. Paired with its ability to have addresses directly mapped into a subspace of the IPv6 address space, it provides a potential candidate for native deployment. Additionally, Mithos can be used to support third-party triangulation to quickly select a close-by replica of data or services.