Minimize Data Loss Research Articles

Personalized trajectory privacy-preserving method based on sensitive attribute generalization and location perturbation

Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data.

RANCANGAN E-COMMERCE DAN PAYMENT API MIDTRANS UNTUK PRODUK UMKM (STUDI KASUS PADA RUMAH KEMASAN)

In the process of product marketing micro, small and medium enterprises on Shop Rumah Kemasan Pandeglang, still being done by marketing the product through the storefront and the goods are stored in the warehouse so that the search, counting of goods directly gets to the warehouse and recording is still using bookkeeping so that the service process for goods is slow, it often happens that goods are not found resulting in data loss, it is necessary to have a system that manages the transaction process for data collection and online sales in order to increase the number of sales and minimize data loss. The system that will be designed later is the e-commerce and payment API midtrans, this research was conducted to increase sales of micro, small and medium business products by providing a system that can be accessed by everyone anywhere and anytime so that in the system that will be built later, buyers no longer need to wait long to buy products and for payments there is no need to make cash payments just online, and for product sellers can speed up the sales process, organized data management. In the design and development stage of e-commerce applications, researchers use the Extreme Programming method, modeling using the UML which produces a software that is expected, and the language used in this development is the PHP programming language, the MySQL database and for payments based on the Midtrans payment gateway application programming interface.

Does researcher activity impact nest survival of sharp-tailed grouse?

Nest survival is a key vital rate of game birds and frequently studied to guide population management. Common scientific protocols are invasive and often involve flushing females from their nests to assess nest contents and status. Biased inference of population dynamics, and thus improper management recommendations, may result if nest survival estimates are affected by researcher activities. We evaluated whether standard nest monitoring protocols for game birds biased estimates of nest survival for sharp-tailed grouse Tympanuchus phasianellus, a common ground-nesting bird in northern grassland ecosystems in the US. We hypothesized that flushing females from nests would negatively affect estimates of daily nest survival and result in biased inferences about population growth regardless of potentially mediating environmental conditions. Our results indicated that cumulative precipitation received during the nesting period had the largest effect on nest survival. Flushing sharp-tailed grouse from nests resulted in reduced nest survival during dry periods, although differences over the entire nesting period were not statistically significant. Downward-biased estimates of nest survival for females that were flushed did not significantly bias estimates of population growth rates. With minimal data loss, we successfully monitored nests of radio-marked females without flushing and recommend that researchers carefully consider potential biases related to research techniques when determining nest monitoring protocols.

RETRACTED ARTICLE: Proactive Load Balancing Strategy Towards Intelligence-Enabled Software-Defined Network

Software-defined network provides a greater solution to many of the complex network management functionalities in a data center network (DCN). In a software-defined data center network, it is essential to manage heavy traffic, to avoid data loss, and to avoid server unavailability during heavy flows. One of the major tasks in a network is the load balancing in the available links. Due to the dynamic data traffic in the network, it is necessary to perform a deep learning of the long-term and the short-term data. Hence, this paper proposes an intelligent load balancing scenario that includes link load balancing, server load balancing, and traffic classification. An artificial neural network is needed because the data are uncertain all the time. Thus, our method provides improved throughput, minimized the flow completion time, and minimizes data loss.

Latency and Energy Efficient Bio-Inspired Conic Optimized and Distributed Q Learning for D2D Communication in 5G

The next-generation communication, i.e. fifth generation (5G), will be manifesting the advertisers in near future. The Device to Device communication would be a proportion of 5G to provide communication requirements for trillions of devices connected in a significant manner to hold immense data rate. Optimization and Machine learning are said to be the most promising mechanisms for furnishing the best solutions to master the predominant aspects and explicit parameters of 5G communication. In this work, a Bio-inspired Conic Optimized and Distributed Latency Q Learning method is proposed for D2D communication in 5G with higher energy efficiency and minimum latency. Initially, a Bio-inspired Conic Particle Swarm Optimization model is to provide resource efficiency and energy efficiency by calculating the fitness function in terms of transmission power and data loss rate through updating the position and velocity to achieve optimized D2D communication. After that, the Distributed Latency Managed Q Learning model is employed for better connectivity with minimal latency which is achieved by two factors. They are SINR function to measure probability factor when choosing corresponding actions and design of reward function assuming neighbor device and communication range. With these two functions, a latency enhanced with minimum data loss is achieved to attain better connectivity for D2D communication. At last, the neighbor device and communication range for corresponding data stream are employed to reduce data loss and latency for D2D communication. The simulation result illustrates that the BCO-DLQL method increases the energy efficiency by 7% and reduces the latency by 24% as compared to state-of-the-art works.

A Joint User Scheduling and Trajectory Planning Data Collection Strategy for the UAV-Assisted WSN

Unmanned aerial vehicles (UAVs) are usually dispatched as mobile sinks to assist data collection in large-scale wireless sensor networks (WSNs). However, when considering the limitations of UAV's mobility and communication capabilities in a large-scale WSN, some sensor nodes may run out of storage space as they fail to offload their data to the UAV for an extended period of time. To minimize the data loss caused by the above issue, a joint user scheduling and trajectory planning data collection strategy is proposed in this letter, which is formulated as a non-convex optimization problem. The problem is further divided into two sub-problems and solved sequentially. Simulation results show that the proposed strategy is more effective in minimizing data loss rate than other strategies.

Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.

Sistem Analisis Dan Perancangan Sistem Informasi Pengarsipan Persuratan Menggunakan Metode Waterfall Pada Dinas Lingkungan Hidup Berbasis Web

This writing discusses the creation of a correspondence filing information system with the aim of making it easier for the secretariat to archive letters in incoming and outgoing mail data at the Environmental Service, and minimizing data loss, and speeding up the process of retrieval of archived data at the time needed so as not to repetition of archiving so that it is faster and more precise. The development of information systems uses the programming language PHP, HTML, CSS, and Java Script and uses the MySQL database. The information system development method uses the waterfall method. The results of this research can be implemented in the form of a web-based archiving information system.

Wireless Qi-powered, Multinodal and Multisensory Body Area Network for Mobile Health.

Wireless, battery-free Body Area Networks (BAN) enable reliable long-term health monitoring with minimal intervention, and have the potential to transform patient care via mobile health monitoring. Current approaches for achieving such battery-free networks are limited in the number, capability, and positioning of sensing nodes-this is related to constraints in power supply, data rate, and working distance requirements between the wireless power source and sensing nodes. Here, we investigate a Qi-based, near-field power transfer scheme that can effectively drive wireless, battery-free, multi-node and multi-sensor BAN over long distances. This consists of a single Qi power source (such as a cellphone), a detached/untethered Passive Intermediate Relay (PIR) (facilitates power transfer from a central Qi source to multiple nodes on the body), and finally individual/detached sensing nodes placed throughout the body. Alongside this power scheme we implement the star network topology of a Gazell protocol to enable the continuous connection of one host to many sensing nodes while minimizing data loss over long temporal periods. The high-power transmission capabilities of Qi enables wireless support for a multitude of sensors (up to 12), and sensing nodes (up to 6) with a single transmitter at long distances (60 cm) and a sample rate of 20 Hz. This scheme is studied both in-vitro and in-vivo on the body.

Remodeling: improved privacy preserving data mining (PPDM)

The data provided by individuals and various organizations while using internet applications and mobile devices are very useful to generate solutions and create new opportunities. The data which is shared needs to be precise to get the quality results. The data which may contain an individual’s sensitive information cannot be revealed to the world without applying some privacy preserving technique on it. Privacy preserving data mining (PPDM) and Privacy preserving data publishing (PPDP) are some of the techniques which can be utilized to preserve privacy. There are some positives and negatives for every technique. The cons frequently constitute loss of data, reduction in the utility of data, compromised diversity of data, reduced security, etc. In this paper, the authors propose a new technique called Remodeling, which works in conjunction with the k-anonymity and K-means algorithm to ensure minimum data loss, better privacy preservation while maintaining the diversity of data. Network data security is also handled by this proposed model. In this research paper, theoretically, we have shown that the proposed technique addresses all the above-mentioned cons and also discusses the merits and demerits of the same.

Accuracy Implementation of Medical Record Management Information System with Waterfall Design System and ISO 9126

Aims: Based on the observations of researchers, some health facilities still use manual processes / have not been documented by the information system resulting in slow service, this study aims to improve health services with a medical record information system.
 Methodology: The method used in this study is an analysis of information systems with the waterfall method and accuracy testing with ISO 9126.
 Results: The results of this medical record management information system run well based on black-box testing and white box results obtained both from an average value of 82 based on the ISO 9126 scale conversion table.
 Conclusion: Based on the results of the average value obtained from the validation test carried out on 3 expert examiners, it can be concluded that the application for the validation system for the validation and distribution of this letter has met the ISO 9126 standard with an average good interpretation of a total value of 82, and according to be able to simplify the process of Patient Medical Record Data Management without neglecting the safety aspects of the validation and distribution process, minimizing data loss, simplifying the reporting process and facilitating the processing of patient medical record data.

Image compression for quality 3D reconstruction

A 3D mesh can be reconstructed from multiple viewpoint images or from a single structured light image. Lossy compression of such images by standard techniques such as JPEG at high compression ratios lead to 3D reconstruction being adversely affected by artifacts and missing vertices. In this paper we demonstrate an improved algorithm capable of high compression ratios without adversely affecting 3D reconstruction and with minimum data loss. The compression algorithm starts by applying block DCT over the input image, and the transformed data being quantized using an optimized quantization matrix. The quantized coefficients of each block are arranged as a 1D array and saved with other block’s data in a larger matrix of coefficients. The DC coefficients are subject to a first order difference whose values are referred to as residual array. The AC coefficients are reduced by eliminating zeros and saving the non-zero values in a reduced coefficients array using a mask of 0 (for a block of zeros) and 1 (for a block of non-zeros). Finally, arithmetic coding is applied to both coefficients and residual arrays. At decompression stage, the coefficients matrix is regenerated by scanning the coefficients array and examining the headers to substitute zero and non-zero data. This matrix is then added to the residual array to obtain the original DC values. The IDCT is then applied to obtain the original image. The proposed algorithm has been tested with images of varying sizes in the context of 3D reconstruction. Results demonstrate that our proposed algorithm is superior to traditional JPEG at higher compression ratios with high perceptual quality of images and the ability to reconstruct the 3D models more effectively, both for structured light images and for sequences of multiple viewpoint images.

VIDPSO: Victim item deletion based PSO inspired sensitive pattern hiding algorithm for dense datasets

Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving data mining (PPDM) includes various sensitive pattern hiding (SPH) techniques, which ensures that sensitive patterns do not get revealed when data mining models are applied on shared datasets. In the process of hiding sensitive patterns, some of the non-sensitive patterns also become infrequent. SPH techniques thus affect the results of data mining models. Maintaining a balance between data privacy and data utility is an NP-hard problem because it requires the selection of sensitive items for deletion and also the selection of transactions containing these items such that side effects of deletion are minimal. There are various algorithms proposed by researchers that use evolutionary approaches such as genetic algorithm(GA), particle swarm optimization (PSO) and ant colony optimization (ACO). These evolutionary SPH algorithms mask sensitive patterns through the deletion of sensitive transactions. Failure in the sensitive patterns masking and loss of data have been the biggest challenges for such algorithms. The performance of evolutionary algorithms further gets degraded when applied on dense datasets. In this research paper, victim item deletion based PSO inspired evolutionary algorithm named VIDPSO is proposed to sanitize the dense datasets. In the proposed algorithm, each particle of the population consists of n number of sub-particles derived from pre-calculated victim items. The proposed algorithm has a high exploration capability to search the solution space for selecting optimal transactions. Experiments conducted on real and synthetic dense datasets depict that VIDPSO algorithm performs better vis-a-vis GA, PSO and ACO based SPH algorithms in terms of hiding failure with minimal loss of data.

Design and performance of a low-cost atmospheric composition monitor for deployment in extreme environments

The Arctic is one critical environment for monitoring climate change as well as variations in background concentrations of atmospheric components. The associated logistic difficulties, though, make hard to deploy an extensive monitoring network of sensors, limiting long time-series to only sparse and costly point observations. Low-cost sensors are experiencing a widespread employment in research and monitoring applications and could be an interesting tool to deploy spatialized monitoring networks even in extreme environments. In this context, two CNR Labs (IBE and ISAC) made a long-term deployment of a prototypal low-cost sensor for atmospheric composition monitoring in the polar research village of Ny-Ålesund (Svalbard, Norway). In about one year of measurements the low-cost sensor showed: i) a good consistency in the data series with minimal data loss, ii) no significant requirements for maintenance and iii) the capability of capturing the main atmospheric trends of the Arctic lower troposphere.

The quality-control test of the digital logic for the ATLAS new small wheel read-out controller ASIC

The Read-Out Controller (ROC) ASIC is an on-detector custom real-time data packet processor for the upgraded New Small Wheel (NSW) Trigger and Data Acquisition (TDAQ) system of the ATLAS Experiment at CERN, Geneva. The ROC is a highly-configurable data concentrator that allows the optimization of bandwidth utilization, reduces the required number of data links, minimizes data loss, implements congestion and flow control mechanisms, allows data filtering, supplies phase adjustable clock signals and offers relatively larger buffer spaces for the readout system. The paper details the designed, implemented and deployed FPGA-based test setup for the quality-control of the ROC packet processing logic. The FPGA-based test setup emulates the ROC context using firmware-based input data streams emulators and output data analyzers which are controlled and monitored by a soft-core Xilinx MicroBlaze microprocessor instantiated on the same FPGA. The ROC chip is accommodated on a custom PCB which assures the power supply and the interface to the FPGA. A mathematical model of the ROC performance as a function of its configuration and input data throughput is proposed. The ROC digital design was successfully validated and its performance assessed, confirming the theoretical model. The design coverage, test procedure and partial mass-testing results are presented and analyzed.

Risk Assessment for Scientific Data

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities.

Non-verbal Communication and Touchless Activation of a Radio-Controlled Car via Facial Activity Recognition

Many smart glasses technologies are being developed to improve the working efficiency or quality of life in various fields. In some enterprises, these technologies are used to help improve the working quality and productivity and minimize data loss. In real life, smart glasses are applied as an entertainment device with augmented/virtual reality or as an assistive manipulator for the physically challenged. Thus, these technologies have mainly adopted various operating systems depending on usages, such as a touchpad, remote control, and voice recognition. However, conventional operating methods have limitations in non-verbal and noisy situations where people cannot use both hands. In this study, we present a method of detecting a facial signal for touchless activation using a transducer. We acquired a facial signal amplified by a lever mechanism using a load cell on the hinge of an eyewear. We then classified the signal and obtained their accuracy by calculating the confusion matrix with classified categories through a machine learning technique, i.e., the support vector machine. We can activate an actuator, such as a radio-controlled car, through a classified facial signal by using an eyewear-type signal transducer. Overall, our operating system can be useful for activating the actuator or transmitting a message through the classified facial activities in non-verbal situations and in situations where both hands cannot be used.

Comparative analysis on the expression of L1 loci using various RNA-Seq preparations

BackgroundRetrotransposons are one of the oldest evolutionary forces shaping mammalian genomes, with the ability to mobilize from one genomic location to another. This mobilization is also a significant factor in human disease. The only autonomous human retroelement, L1, has propagated to make up 17% of the human genome, accumulating over 500,000 copies. The majority of these loci are truncated or defective with only a few reported to remain capable of retrotransposition. We have previously published a strand-specific RNA-Seq bioinformatics approach to stringently identify at the locus-specific level the few expressed full-length L1s using cytoplasmic RNA. With growing repositories of RNA-Seq data, there is potential to mine these datasets to identify and study expressed L1s at single-locus resolution, although many datasets are not strand-specific or not generated from cytoplasmic RNA.ResultsWe developed whole-cell, cytoplasmic and nuclear RNA-Seq datasets from 22Rv1 prostate cancer cells to test the influence of different preparations on the quality and effort needed to measure L1 expression. We found that there was minimal data loss in the identification of full-length expressed L1 s using whole cell, strand-specific RNA-Seq data compared to cytoplasmic, strand-specific RNA-Seq data. However, this was only possible with an increased amount of manual curation of the bioinformatics output to eliminate increased background. About half of the data was lost when the sequenced datasets were non-strand specific.ConclusionsThe results of these studies demonstrate that with rigorous manual curation the utilization of stranded RNA-Seq datasets allow identification of expressed L1 loci from either cytoplasmic or whole-cell RNA-Seq datasets.

Integrating consortium blockchain into edge server to defense against ransomware attack

An edge server acts as a data gateway in an IoT network between IoT devices and backend servers. If the edge server is under the ransomware attack, the server’s operation would be interfered by locking its critical files, leading to a single point of failure in the IoT network. This paper proposes a blockchain solution, called Blockchain-enabled Recovery Service (BRS), to tackle the malware injection attack in edge servers. In BRS, edge servers are implemented by nodes in a blockchain network which allows IoT data distributed in edge servers over multiple data connections. It ensures data availability and consistency while improving the network throughput. A simulation test has been conducted to evaluate the performance of edge servers in the blockchain network. The result showed that the size of data record in a ledger is crucial when maximizing the efficiency of a ledger and minimizing data loss in ransomware attack are considered. Moreover, even though the blockchain network is under the attack, data availability and data recovery can be achieved.

A novel solution for a Wireless Body Sensor Network: Telehealth elderly people monitoring

Event-based data transfer through Wireless Body Sensor Networks (WBSN) for monitoring the health of the elderly has so far not been successfully implemented due to limitations arising forms unreliable data, end-to-end delay during data transmission and the high energy consumption by sensors. This paper aims to improve reliability and latency and to reduce energy consumption by sensors during data transmission in WSBN. The proposed system consists of an Enhanced Reliability, Energy-Efficient and Latency (EREEAL) algorithm to reduce data losses and end-to-end delay as well as improve the transmission reliability in WBSN by sending the sensor data during different time slots using Time Division Multiple Access (TDMA) analysis and by minimizing redundant sensitive data. The result shows that the new algorithm improves reliability to 98% over the data bits generated within 8 ∼ 12 min and reduces latency to 0.635 compared to 0.875 ms in the ‘state of the art’ system. Furthermore, the reduction in latency leads to lower power consumption by sensors, reduced to 315.638*10 3JS/bits during patient data transmission using a tele-monitoring process. The proposed system concentrates on reducing interference with data between sensors and focuses on minimizing data loss during transmission. Thus, this study provides an acceptable range of reliability with reduced delay and lower power consumption due to which doctors at a remote site can obtain reliable data value for smooth monitoring.