scholarly journals Filtering Redundant Data from RFID Data Streams

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Hazalila Kamaludin ◽  
Hairulnizam Mahdin ◽  
Jemal H. Abawajy
Keyword(s):  
Radio Frequency Identification ◽  
Data Streams ◽  
False Positive Rate ◽  
Bloom Filter ◽  
True Positive Rate ◽  
Redundant Data ◽  
Rfid Data ◽  
Positive Rate ◽  
Frequency Identification ◽  
Processing And Storage

Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.

scholarly journals A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Siye Wang ◽  
Ziwen Cao ◽  
Yanfang Zhang ◽  
Weiqing Huang ◽  
Jianguo Jiang
Keyword(s):  
Radio Frequency Identification ◽  
Spatial Data ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Bloom Filter ◽  
Data Redundancy ◽  
Filter Performance ◽  
Redundant Data ◽  
Rfid Data

The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.

Anomaly Detection Using RFID-Based Information Management in an IoT Context

2018 ◽  
Vol 30 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Raúl Parada ◽  
Joan Melià-Seguí ◽  
Rafael Pous
Keyword(s):  
Anomaly Detection ◽  
Information Management ◽  
Radio Frequency Identification ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Detection Methods ◽  
Heterogeneous Sensors ◽  
Positive Rate ◽  
Frequency Identification ◽  
Speed Up

IoT-based environments may infer anomalies based on the data processed from their heterogeneous sensors. Within the technologies evolving the IoT concept, nowadays the Radio Frequency Identification (RFID) technology is a de facto standard in areas like retail or logistics. For instance, most retailers attach RFID-labels to their items to avoid stock-out in the inventory or speed up cash processes. Besides identification, RFID provides further RF data which can be used for information management like anomaly detection (i.e. a shoplifting in a RFID loss prevention system). This manuscript presents two IoT scenarios to detect anomalies using multivariate outlier detection methods, uniquely using RFID data. This research empirically evaluates the authors' proposed methods by reproducing a RFID-enabled store, and the two proposed scenarios. The evaluation achieved a False Positive Rate around 0.1% and a True Positive Rate around 87%.

scholarly journals Fractal-Based Outlier Detection Algorithm over RFID Data Streams

2016 ◽  
Vol 12 (1) ◽  
pp. 35 ◽  
Author(s):  
Li Lian Sheng
Keyword(s):  
Outlier Detection ◽  
Radio Frequency Identification ◽  
Data Streams ◽  
Detection Algorithm ◽  
Fractal Model ◽  
Self Similarity ◽  
Fractal Models ◽  
Rfid Data ◽  
Efficiency And Effectiveness ◽  
Frequency Identification

Nowadays, Radio frequency identification (RFID) has been extensively deployed to retailing, supply chain management, object recognition, object monitoring and tracking and many other fields. Detecting outliers in RFID data streams can help us find abnormal activities and thus avoid disasters. In order to detect outliers in RFID data streams efficiently and effectively, we proposed a fractal based outlier detection algorithm. Firstly, we built a monotone searching space based on the self-similarity of fractal. Then, we proposed two piecewise fractal models for RFID data streams, and presented an outlier detection algorithm based on the piecewise fractal model. Finally, we validated the efficiency and effectiveness of the proposed algorithm by massive experiments.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

Query Processing of RFID Data with Object Trajectory in Manufacture and Logistics

2009 ◽  
Vol 16-19 ◽  
pp. 1043-1047
Author(s):  
Sun Wei ◽  
Li Hua Dong ◽  
Yao Hua Dong
Keyword(s):  
Pervasive Computing ◽  
Radio Frequency Identification ◽  
Object Identification ◽  
Data Set ◽  
Wide Range ◽  
Storage Scheme ◽  
Rfid Data ◽  
Frequency Identification ◽  
Application Data ◽  
Path Queries

In the domain of manufacture and logistics, Radio Frequency Identification (RFID) holds the promise of real-time identifying, locating, tracking and monitoring physical objects without line of sight due to an enhanced efficiency, accuracy, and preciseness of object identification, and can be used for a wide range of pervasive computing applications. To achieve these goals, RFID data has to be collected, filtered, and transformed into semantic application data. However, the amount of RFID data is huge. Therefore, it requires much time to extract valuable information from RFID data for object tracing. This paper specifically explores options for modeling and utilizing RFID data set by XML-encoding for tracking queries and path oriented queries. We then propose a method which translates the queries to SQL queries. Based on the XML-encoding scheme, we devise a storage scheme to process tracking queries and path oriented queries efficiently. Finally, we realize the method by programming in a software system for manufacture and logistics laboratory. The system shows that our approach can process the tracing or path queries efficiently.

Watch For Failing Objects: What Inappropriate Compliance Reveals About Shared Mental Models In Autonomous Cars

2021 ◽  
Vol 65 (1) ◽  
pp. 643-647
Author(s):  
Yosef S. Razin ◽  
Jack Gale ◽  
Jiaojiao Fan ◽  
Jaznae’ Smith ◽  
Karen M. Feigh
Keyword(s):  
Mental Models ◽  
Mental Model ◽  
False Positive Rate ◽  
Ground Truth ◽  
True Positive Rate ◽  
Shared Mental Models ◽  
Shared Mental Model ◽  
Autonomous Cars ◽  
Positive Rate ◽  
Dispositional Factors

This paper evaluates Banks et al.’s Human-AI Shared Mental Model theory by examining how a self-driving vehicle’s hazard assessment facilitates shared mental models. Participants were asked to affirm the vehicle’s assessment of road objects as either hazards or mistakes in real-time as behavioral and subjective measures were collected. The baseline performance of the AI was purposefully low (<50%) to examine how the human’s shared mental model might lead to inappropriate compliance. Results indicated that while the participant true positive rate was high, overall performance was reduced by the large false positive rate, indicating that participants were indeed being influenced by the Al’s faulty assessments, despite full transparency as to the ground-truth. Both performance and compliance were directly affected by frustration, mental, and even physical demands. Dispositional factors such as faith in other people’s cooperativeness and in technology companies were also significant. Thus, our findings strongly supported the theory that shared mental models play a measurable role in performance and compliance, in a complex interplay with trust.

scholarly journals Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates

2014 ◽  
Author(s):  
Andreas Tuerk ◽  
Gregor Wiktorin ◽  
Serhat Güler
Keyword(s):  
Probability Distributions ◽  
False Positive Rate ◽  
Synthetic Data ◽  
True Positive Rate ◽  
Rna Seq ◽  
Microarray Quality Control ◽  
Data Set ◽  
Rna Transcripts ◽  
Positive Rate ◽  
Fragment Distribution

Quantification of RNA transcripts with RNA-Seq is inaccurate due to positional fragment bias, which is not represented appropriately by current statistical models of RNA-Seq data. This article introduces the Mix2(rd. "mixquare") model, which uses a mixture of probability distributions to model the transcript specific positional fragment bias. The parameters of the Mix2model can be efficiently trained with the Expectation Maximization (EM) algorithm resulting in simultaneous estimates of the transcript abundances and transcript specific positional biases. Experiments are conducted on synthetic data and the Universal Human Reference (UHR) and Brain (HBR) sample from the Microarray quality control (MAQC) data set. Comparing the correlation between qPCR and FPKM values to state-of-the-art methods Cufflinks and PennSeq we obtain an increase in R2value from 0.44 to 0.6 and from 0.34 to 0.54. In the detection of differential expression between UHR and HBR the true positive rate increases from 0.44 to 0.71 at a false positive rate of 0.1. Finally, the Mix2model is used to investigate biases present in the MAQC data. This reveals 5 dominant biases which deviate from the common assumption of a uniform fragment distribution. The Mix2software is available at http://www.lexogen.com/fileadmin/uploads/bioinfo/mix2model.tgz.

scholarly journals Exploring Author Profiling for Fake News Detection

2021 ◽  
Author(s):  
Shloak Rathod
Keyword(s):  
False Positive Rate ◽  
True Positive Rate ◽  
Supervised Machine Learning ◽  
Online Media ◽  
Fake News ◽  
Writing Style ◽  
Short Term ◽  
Positive Rate ◽  
Rapid Dissemination ◽  
Author Profiling

<div><div><div><p>The proliferation of online media allows for the rapid dissemination of unmoderated news, unfortunately including fake news. The extensive spread of fake news poses a potent threat to both individuals and society. This paper focuses on designing author profiles to detect authors who are primarily engaged in publishing fake news articles. We build on the hypothesis that authors who write fake news repeatedly write only fake news articles, at least in short-term periods. Fake news authors have a distinct writing style compared to real news authors, who naturally want to maintain trustworthiness. We explore the potential to detect fake news authors by designing authors’ profiles based on writing style, sentiment, and co-authorship patterns. We evaluate our approach using a publicly available dataset with over 5000 authors and 20000 articles. For our evaluation, we build and compare different classes of supervised machine learning models. We find that the K-NN model performed the best, and it could detect authors who are prone to writing fake news with an 83% true positive rate with only a 5% false positive rate.</p></div></div></div>

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