Data Block Research Articles

Applying machine learning in the investigation of the link between the high-velocity streams of charged solar particles and precipitation-induced floods.

This study explores a possible link between solar activity and floods caused by precipitation. For this purpose, discrete blocks of data for 89 separate flood events in Europe in the period 2009-2018 were used. Solar activity parameters with a time lag of 0-11days were used as input data of the model, while precipitation data in the 12days preceding the flood were used as output data. The level of randomness of the input and output time series was determined by correlation analysis, while the potential causal relationship was established by applying machine learning classification predictive modeling. A total of 25 distinct machine-learning algorithms and four model ensembles were applied. It was shown that in 81% of cases, the designed model could explain the occurrence or absence of precipitation-induced floods 9days in advance. Differential proton flux in the 0.068-0.115MeV and integral proton flux > 2.5MeV were found to be the most important factors for forecasting precipitation-induced floods. The study confirmed that machine learning is a valuable technique for establishing nonlinear relationships between solar activity parameters and the onset of floods induced by precipitation.

IMPLEMENTATION IN SСAS KIDYM OF SOFTWARE DETERMINATION OF DIMENSIONS OF UNKNOWNS IN PROBLEMS OF MECHANICS BASED ON ENERGY RELATIONS

The paper presents the results of the study of an algorithm for processing measurement units of physical and geometric quantities in the problems of mechanics implemented in the specialized computer algebra system (SCAS) KiDyM. The KiDyM software complex is used for solving problems of mechanics of engineering and scientific orientation of any complexity. For the completeness of the initial data preparation process for such tasks, the use of measurement units in the initial and resulting data is essential. The output data of KiDyM tasks have a rather slow appearance due to their ana- lytical form. To provide assistance to the user, KiDyM programs have a special diagnostic block of output data. Therefore, the implementation of the dimensions used in KiDyM data pursues the additional goal of increasing the diagnostic capabilities of the system. Dimensional formulas are imple- mented here as ordinary analytical expressions, which enables the built-in computer algebra system to form expressions of data measurement units during their computer analytical transformations in the process of solving problems of dynamics, statics and kinematics. Thus, working out the meas- urement units in the SCAS KiDyM includes reading the values of the variables with the dimensions specified in formula form the initial data, diagnos- ing the correctness of dimensionising and compliance of the dimensions with the data, storing them in instances of the "variable" and "element" classes, calculating the dimensions according to data conversion formulas to obtain calculation results. The paper demonstrates how to efficiently es- tablish dimensions of geometrical and physical quantities using energy relations between coordinates and characteristics of elements of the mechanical model. For the demonstration of the logic of the implemented algorithm, simple compact examples from educational problems of kinematics, statics, and dynamics of flat and spatial systems are provided. The article shows how KiDyM constructs the necessary equations for solving tasks, and how units of measurement for equation components and their solutions can be obtained. Since dimensions are represented as formulas here, their simplifica- tion – reduction and substitution with derivative units – can be easily implemented. Moreover, since the variables involved in dimension formulas and the variables of the task itself, within KiDyM, are situated in different data spaces, they can have the same names without any confusion.

Two dimensional parallel sequence spread spectrum for wireless communications

A novel two-dimensional parallel sequence spread spectrum (2D-PSSS) structure for the physical layer of wireless communication systems is introduced. The proposed 2D-PSSS system partitions a data block into two groups of bits, representing the two dimensions. The first dimension represents the selected code from a predefined set of Gold codes. While the second dimension determines a shift value applied to the chosen code. The shifted Gold code will represent the data block. By design, the 2D-PSSS system overcomes the challenge of inter-code interference encountered in conventional PSSS systems without introducing additional computational complexity. Moreover, the 2D-PSSS system offers reduced encoder computational complexity compared to PSSS systems, enhancing its suitability for a wide range of wireless communication applications. The performance of the 2D-PSSS system in terms of bit error rate has been evaluated in additive white Gaussian noise (AWGN) channel and under different fading channel conditions.

Abstract P460: Attack of the (Survey) Bots: How We Determined That Our Anonymous Survey Was Full of False Data

Introduction: Given the nursing shortage in the US and increased focus on reducing burnout, more research on nurses' and nursing assistants’ strategies to mitigate the adverse effects of night shifts and rotating schedules is needed. However, recruitment challenges limit study sample sizes and, consequently, the generalizability of results. Social media platforms provide opportunities to reach a broader sample, yet data quality assessment requires further investigation. Purpose: This study examined the feasibility of using X (formerly Twitter) to recruit nurses and nursing assistants throughout the US to complete a survey on work schedules, fatigue countermeasures use, and sleep and general health. Methods: The anonymous survey was advertised on X with the following tweet: “Nurses and nursing assistants: How does your work schedule and sleep affect your health? What do you do to lessen your fatigue when working? Let us know by completing the Scheduling, Sleep, and Health Survey and a chance for an Amazon gift card: [survey link]. Please RT & share! #Nurse #NurseLife.” Inclusion criteria were: 1) age ≥ 18; 2) being a registered nurse, licensed practical nurse, or nursing assistant; and 3) worked ≥ 36 hours/week during the last 4 weeks. The survey included questions on sociodemographics, shift type and schedule, fatigue countermeasure use, mealtimes/location when working, and sleep health and several PROMIS measures (General Health, Fatigue, and Cognitive Function). After survey completion, respondents were directed to a new survey to provide their contact information to be entered to win a $50 Amazon gift card, with 20 randomly selected recipients. The survey incorporated data verification questions to detect potentially bot-generated or suspicious responses. Results: In under 24 hours, 512 surveys were recorded (495 contained complete data) before deactivating the survey link. Responses were categorized as: 1) likely genuine (n = 2); 2) likely bot-generated (n = 472); and 3) uncertain (i.e., possible but not likely such as worked 6 or 7 shifts in the last 7 days; n = 19). Identifying likely bot-generated responses involved evaluating four components from the data verification questions: 1) inconsistencies in mealtimes/locations compared to reported shift type in the past week (n = 243); 2) failure to meet the criterion for hours worked (i.e., shift length and number of shifts totaling < 36 hours in the last 7 days; n = 143); 3) discrepancies in reported shifts between the past week and the previous 4 weeks (n = 61); and 4) responses with large blocks of identical data (n = 27). Conclusions: Since over 95% of the responses were likely from bots, it may not be feasible to use X for valid, anonymous survey data collection. Targeted recruitment methods within a healthcare system or specialty groups (e.g., operating room or critical care nurses) may provide more valid, but less generalizable, data.

CLOUD BASED DUPLICATION REMOVAL SYSTEM

Deduplication involves eliminating duplicate or redundant data to reduce stored data volume, commonly used in data backup, network optimization, and storage management. However, traditional deduplication methods have limitations with encrypted data and security. The primary objective of this project is to develop new distributed deduplication systems that offer increased reliability. In these systems, data chunks are distributed across the Hadoop Distributed File System (HDFS), and a robust key management system is utilized to ensure secure deduplication with slave nodes. Instead of having multiple copies of the same content, deduplication removes redundant data by retaining only one physical copy and referring other instances to that copy. The granularity of deduplication can vary, ranging from an entire file to a data block. The MD5 and 3DES algorithms are used to enhance the deduplication process. The proposed approach in this project is the Proof of Ownership (POF) of the file. With this method, deduplication can effectively address the issues of reliability and label consistency in HDFS storage systems. The proposed system has successfully reduced the cost and time associated with uploading and downloading data, while also optimizing storage space. Key Words: Cloud computing, data storage, file checksum algorithms, computational infrastructure, duplication.

Health Chain Connect

Electronic medical records are medical records of patient stored digitally on the internet. Patient information is stored in the blockchain, and this information is stored as a block of data in the blockchain. Encrypted using the AES algorithm used to encrypt all patient data. Blockchain networks are used in healthcare to exchange patient information and increase the efficiency, security, and transparency of medical information sharing in healthcare. Security, decentralized and transparency, the three main features of blockchain technology, make all applications secure and inaccessible to unauthorized persons.

Survey Paper on Hospital Management System using Blockchain

Electronic health records are health information of patients that are saved digitally in a network. The information of the patient are stored in the blockchain and these details are stored in the block chain as a blocks of data. The data is encrypted by the algorithm known as AES which is used to encrypt all the data of the patients. A Blockchain network is used in the healthcare system to exchange patient data improve the performance, security, and transparency of sharing medical data in the health care system. The three main feature of blockchain technology – Security, Decentralization Transparency make any application secure and not accessible by unauthorized parties. Key Words: Health records, Blockchain, Encryption of data, Decryption of data, Security.

A Security Information Transmission Method Based on DHR for Seafloor Observation Network.

A seafloor observation network (SON) consists of a large number of heterogeneous devices that monitor the deep sea and communicate with onshore data centers. Due to the long-distance information transmission and the risk of malicious attacks, ensuring the integrity of data in transit is essential. A cryptographically secure frame check sequence (FCS) has shown great advantages in protecting data integrity. However, the commonly used FCS has a collision possibility, which poses a security risk; furthermore, reducing the encryption calculation cost is a challenge. In this paper, we propose a secure, lightweight encryption scheme for transmitted data inspired by mimic defense from dynamic heterogeneous redundancy theory. Specifically, we use dynamic keys to encrypt a data block and generate multiple encrypted heterogeneous blocks for transmission. These continuously changing encrypted data blocks increase the confusion regarding the original encoded data, making it challenging for attackers to interpret and modify the data blocks. Additionally, the redundant information from the multiple blocks can identify and recover tampered data. Our proposed scheme is suitable for resource-constrained environments where lightweight encryption is crucial. Through experimental demonstrations and analysis methods, we determine the effectiveness of our encryption scheme in reducing computational costs and improving security performance to protect data integrity.

A Model of Forming a Dynamic Structure for Establishing the Source of Messages in the Receiver's Memory

The purpose of research is increasing the speed of procedures for determining the data source in the block coupling mode, due to the analysis of the dynamic list structure of messages formed in the receiver's memory as a result of intermediate calculations.Methods. The model of forming a dynamic list structure is based on the hardware implementation of the method of limiting the set of data blocks processed by the receiver. The data package includes a special service word, the contents of which are checked for falling into the range of values formed by the receiver when each data block is received. The described restriction reduces the number of typical comparison operations of service words performed when determining the source of messages, and also reduces the likelihood of errors in determining the data source.Results. Based on the model of the formation of a dynamic tree-like list structure, distributions of a priori probabilities of the number of nodes of a certain level are obtained in case of errors in determining the data source and without them. This allows us to obtain significant a posteriori error probabilities depending on the observed number of nodes of a certain level. The criteria for making a decision on the error of determining the source based on the calculation of the number of nodes before the complete completion of the formation of the tree structure and before the stage of its analysis are formulated. This reduces the computational complexity of the procedure for determining the data source in the block coupling mode and reduces the memory costs for storing intermediate results.Conclusion. In the course of the study, it was revealed that for sequences of messages with a length of more than 20, the detection of more than 8 extraneous branches of the dynamic list structure being formed allows us to state with a 90% probability that the procedure for determining the source ended in an error. The refusal to transmit subsequent sequence messages and to perform processing operations of the tree structure allows to increase the speed of the procedure for determining the source of messages and reduce its computational complexity.

Performance Assessment of Non-Integer-Hour Data Blocks in BDS Broadcast Ephemeris

Performance Assessment of Non-Integer-Hour Data Blocks in BDS Broadcast Ephemeris

Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency

Persistent memory provides high-performance data persistence at main memory. Memory writes need to be performed in strict order to satisfy storage consistency requirements and enable correct recovery from system crashes. Unfortunately, adhering to such a strict order significantly degrades system performance and persistent memory endurance. This paper introduces a new mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering requirements at significantly lower performance and endurance loss. LOC consists of two key techniques. First, Eager Commit eliminates the need to perform a persistent commit record write within a transaction. We do so by ensuring that we can determine the status of all committed transactions during recovery by storing necessary metadata information statically with blocks of data written to memory. Second, Speculative Persistence relaxes the write ordering between transactions by allowing writes to be speculatively written to persistent memory. A speculative write is made visible to software only after its associated transaction commits. To enable this, our mechanism supports the tracking of committed transaction ID and multi-versioning in the CPU cache. Our evaluations show that LOC reduces the average performance overhead of memory persistence from 66.9% to 34.9% and the memory write traffic overhead from 17.1% to 3.4% on a variety of workloads.

Methods and means of conflict-free data exchange in the group of mobile robotic platforms

When using groups of mobile robotic platforms (MRP), problems arise related to the management of individual platforms, the organization of cooperation in the group, and the management of the group as a whole. Management of the MRP group involves managing the actions of individual platforms to achieve the group's overall goal. To ensure the management of the MRP group in such a case, it is advisable to choose a hybrid method that requires solving the problem of conflict-free data exchange and control commands between the MRPs in the group. To solve this problem, it is proposed to improve the relevant methods and tools. The scientific novelty of the obtained research results is that a method of multi-channel conflict-free data exchange has been developed, which provides a real-time mode due to the coordination of the intensity of data arrival with the intensity of access. The method of controlling the movement of a group of mobile robotic platforms has been improved, which, by taking into account the changing parameters of the platforms and the changing state of the surrounding environment, provides effective management of the MRP group in real time. The practical significance of the research results is that it is proposed to use the CSMA/CA slotted mechanism for non-time-critical traffic to improve performance, and for time-critical traffic, coordinator-controlled access using guaranteed time slots. The hybrid method of management takes into account the advantages of centralized and distributed depending on specific tasks and conditions of use. It is proposed to use a multi-channel device for conflict-free exchange using the method of time allocation of RAM resources for data exchange in hybrid control. It is shown that global low-power networks LPWANs (Low-Power Wide Area Networks) can be used to transmit small blocks of data at a low speed when exchanging with MRP. It is proposed to use the slotted CSMA/CA mechanism for the transmission of non-time-critical traffic, and for time-critical traffic, coordinator-controlled access using guaranteed time slots. It is shown that the performance of the network during the conflict-free access period CFP depends on the results of the distribution of guaranteed GTS time slots among active users. LoRa technology was selected for long-distance data exchange between MRPs, which at the MAC (Media Access Control) sublayer allows for transmission planning and communication management between end devices and gateways, avoiding collisions and optimizing network performance.

Traceability Design of Cultural and Creative Products Based on Big Data Algorithm and Blockchain Technology

At present, countries all over the world attach great importance to cultural works. These works have become an important engine of economic development and can make good contributions to economic growth. The traditional tracing control scheme of cultural creation works has some problems, such as incomplete information collection, critical point of unit tracing, information fraud, centralized data storage and so on. At the same time, there seems to be a series of problems that can be solved. This paper analyzes the current situation of the review data of cultural creation products, and puts forward the review and analysis scheme of cultural creation works based on large-scale data algorithm and block chain technology. In addition, by combining department chain technology with intranet and traditional database system, an information database about the supply chain of cultural products in crop departments is established. The processing, logistics and sales information and the information of participants are interrelated.

ImageNet classification with Raspberry Pis: federated learning algorithms of local classifiers

PurposeThis study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.Design/methodology/approachThe authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.FindingsEmpirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).Originality/valueEfficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

Pre-Cutoff Value Calculation Method for Accelerating Metric Space Outlier Detection

Outlier detection is an important data mining technique. In this article, the triangle inequality of distances is leveraged to design a pre-cutoff value (PCV) algorithm that calculates the outlier degree pre-threshold without additional distance computations. This algorithm is suitable for accelerating various metric space outlier detection algorithms. Experimental results on multiple real datasets demonstrate that the PCV algorithm reduces the runtime and number of distance computations for the iORCA algorithm by 14.59% and 15.73%, respectively. Even compared to the new high-performance algorithm ADPOD, the PCV algorithm achieves 1.41% and 0.45% reductions. Notably, the non-outlier exclusion for the first data block in the dataset is significantly improved, with an exclusion rate of up to 36.5%, leading to a 23.54% reduction in detection time for that data block. While demonstrating excellent results, the PCV algorithm maintains the data type generality of metric space algorithms.

Improving Cache Hits On Replacment Blocks Using Weighted LRU-LFU Combinations

Block replacement refers to the process of selecting a block of data or a cache line to be evicted or replaced when a new block needs to be brought into a cache or a memory hierarchy. In computer systems, block replacement policies are used in caching mechanisms, such as in CPU caches or disk caches, to determine which blocks are evicted when the cache is full and new data needs to be fetched. The combination of LRU (Least Recently Used) and LFU (Least Frequently Used) in a weighted manner is known as the "LFU2" algorithm. LFU2 is an enhanced caching algorithm that aims to leverage the benefits of both LRU and LFU by considering both recency and frequency of item access. In LFU2, each item in the cache is associated with two counters: the usage counter and the recency counter. The usage counter tracks the frequency of item access, while the recency counter tracks the recency of item access. These counters are used to calculate a combined weight for each item in the cache. Based on the experimental results, the LRU-LFU combination method succeeded in increasing cache hits from 94.8% on LFU and 95.5% on LFU to 96.6%.

Why use component-based methods in sensory science?

This paper discusses the advantages of using so-called component-based methods in sensory science. For instance, principal component analysis (PCA) and partial least squares (PLS) regression are used widely in the field; we will here discuss these and other methods for handling one block of data, as well as several blocks of data. Component-based methods all share a common feature: they define linear combinations of the variables to achieve data compression, interpretation, and prediction. The common properties of the component-based methods are listed and their advantages illustrated by examples. The paper equips practitioners with a list of solid and concrete arguments for using this methodology.

Hindi Text Summarization Using Sequence to Sequence Neural Network

Text summarizing reduces a large block of text data to a precise, short, and intelligible text that conveys the whole meaning of the actual text in a few words while maintaining the original context. Due to a lack of relevant summaries, it is hard to understand the main idea of the document. Text summarization using the abstractive technique is well-studied in English, although it is still in its infancy in Indian regional languages. In this study, we investigate the effectiveness of using a sequence-to-sequence (Seq2Seq) neural network based on attention and its optimization for text summarization for the Hindi language (HiATS), explicitly comparing the Adam and RMSprop optimizers. Our method allows the model to take the Hindi language dataset and, as output, provides a concise summary that accurately reflects the gist of the original text. The performance of the models will be evaluated using Rouge-1 and Rouge-2 metrics.

Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning.

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve performance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).

The Formation of Professionally-based Competence of Future Foreign Language Teachers through Using Digital Technologies

This scientific article is devoted to the study of the methodology for the formation of professionally-based competence of future foreign language teachers through using digital technologies. The purpose of the scientific article: to develop a methodology for the formation of professionally-based competence of future foreign language teachers through using digital technologies and to test the developed methodology on the basis of the experiment. In order to determine the theoretical significance of the scientific article, literary resources related to the topic of scientific work were analyzed and studied. The concept of “professional-based competence” was defined. The sub-competences of the professionally-based competence of future foreign language teachers were presented. The importance of using digital technologies in the formation of professionally-based competence of future foreign language teachers was described. The types of digital technologies for the formation of professionally based competence of future foreign language teachers were analyzed. The use of blockchain technology, which is part of digital technologies, in the formation of professionally-based competence of future foreign language teachers was described. A methodology for the formation of professionally-based competence of future foreign language teachers was compiled. The methodology for the formation of professionally-based competence was tested on the basis of the experiment. The experiment involved the control and experimental groups of 106 students. The methodology for the formation of professionally-based competence of future foreign language teachers was developed on the basis of a digital educational platform called “professional-based digital practicum”. A series of data blocks were published in this digital practicum. The conducted experiment was analyzed. Based on the analysis, the results of the experiment were summarized.