Wireless Sensor Networks Using Android Virtual Devices and Near Field Communication Peer-To-Peer Emulation
Abstract— Several new Android Smartphone’s support Near Field Communication (NFC). The Android SDK provides an NFC API that can be used to develop NFC applications that conduct peer-to-peer (P2P) data exchange. The Android emulator does not support P2P communication between instances of the Android Virtual Device (AVD). In addition to this constraint, P2P experimentation on actual Smartphone’s is difficult due to limited NFC support. To fill the gap created by this minimal support, we propose the Java Mail NFC API (JNFC). JNFC uses the Java Mail API to emulate the functionality of the Android NFC P2P API. To evaluate the performance of JNFC, we created the DroidWSN Wireless Sensor Network (WSN) model and implemented it as an Android application. We design and conduct an experiment for our DroidWSN model to measure the execution time of our Android application WSN on AVDs. We compare our simulation results against those from a similar experiment that measured the execution time of a WSN composed of Sun SPOT wireless devices. While the execution time of our DroidWSN model is slower, we assert that our design is more simple and flexible than that of our comparison study. We conclude that this benefit and the factors of JNFC cost (it is open source), the quality and quantity of Android Smartphone sensors, and imminent Android Smartphone support for NFC P2P, combine to make JNFC and the Android AVD a platform for NFC and WSN research. Our study also emphasizes the need for Google to create Android NFC P2P and sensor emulation APIs
Secure Transmission Medical Data for Pervasive Healthcare System using Android
Abstract— Several new Android Smartphone’s support Near Field Communication (NFC). The Android SDK provides an NFC API that can be used to develop NFC applications that conduct peer-to-peer (P2P) data exchange. The Android emulator does not support P2P communication between instances of the Android Virtual Device (AVD). In addition to this constraint, P2P experimentation on actual Smartphone’s is difficult due to limited NFC support. To fill the gap created by this minimal support, we propose the Java Mail NFC API (JNFC). JNFC uses the Java Mail API to emulate the functionality of the Android NFC P2P API. To evaluate the performance of JNFC, we created the DroidWSN Wireless Sensor Network (WSN) model and implemented it as an Android application. We design and conduct an experiment for our DroidWSN model to measure the execution time of our Android application WSN on AVDs. We compare our simulation results against those from a similar experiment that measured the execution time of a WSN composed of Sun SPOT wireless devices. While the execution time of our DroidWSN model is slower, we assert that our design is more simple and flexible than that of our comparison study. We conclude that this benefit and the factors of JNFC cost (it is open source), the quality and quantity of Android Smartphone sensors, and imminent Android Smartphone support for NFC P2P, combine to make JNFC and the Android AVD a platform for NFC and WSN research. Our study also emphasizes the need for Google to create Android NFC P2P and sensor emulation APIs
Secure Transmission Medical Data for Pervasive Healthcare System using Android
Ref: IEEE 2013 International conference on Communication and Signal Processing
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ABSTRACT:Emerging technologies are transforming the workflows
in pervasive healthcare enterprises. Pervasive
Healthcare is a one of the developing technology within the pervasive
computing paradigm. The presence of pervasive
computing, consisting of wireless network gives innovative medium for
data transmission of medical applications. We currently use a various wireless
technology in healthcare domain. In the existing technology of e-Health has
less security of Electronic Medical Records (EMR) and cannot access the medical
records in wireless medium. An EMR is a digital version of the traditional
paper-based medical record for an each patient’s record. The EMR represents a
medical record within a faculty can access the data, such as a doctor or a patient
or administration. The accessing of information from the remote database should
be high security; it should be a secured access of data by authorized persons.
We propose a Pervasive Mobile Healthcare solve these problems and provide user
to access the multimedia medical record from anywhere and anytime with security using Elliptical Curve Cryptography(ECC)
algorithm, which includes authentication and access control. The authentication
is allows the types of users who is authorized to use the application. Security
is provided through the process of Encryption and decryption of data. This
secured system will provide security in delivering the EMR of patients.
Implementation here is done by using Android software and for database MySql is
used in Server system. A Wi-Fi enabled mobile is used to receive or transmit the
secured medical data as well as image retrieval. The novelty of my application
deals with mobility where the users can able to access the secure information.
The mobile application develop for real world environment.
REF: IEEE 2012 International Conference on Trust, Security and Privacy in Computing and Communications
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ABSTRACT:The market for smart phones has been booming in the past few years. There
are now over 400,000 applications on the Android market. Over 10 billion
Android applications have been downloaded from the Android market. Due to the
Android popularity, there are now a large number of malicious vendors targeting
the platform. Many honest end users are being successfully hacked on a regular
basis. In this work, a cloud based reputation security model has been proposed
as a solution which greatly mitigates the malicious attacks targeting the
Android market. Our security solution takes advantage of the fact that each
application in the android platform is assigned a unique user id (UID). Our
solution stores the reputation of Android applications in an anti-malware providers’
cloud (AM Cloud). The experimental results witness that the proposed model
could well identify the reputation index of a given application and hence its
potential of being risky or not.
CAM: Cloud-Assisted Privacy Preserving
Mobile Health Monitoring
Ref:2013 IEEE Transactions on Information Forensics and Security,
Project Price: Contact US
Abstract:Cloud-assisted mobile health (mHealth)
monitoring, which applies the prevailing mobile communications and cloud computing
technologies to provide feedback decision support, has been considered as a
revolutionary approach to improving the quality of healthcare service while
lowering the healthcare cost. Unfortunately, it also poses a serious risk on
both clients’ privacy and intellectual property of monitoring service
providers, which could deter the wide adoption of mHealth technology. This paper
is to address this important problem and design a cloud assisted privacy
preserving mobile health monitoring system to protect the privacy of the involved
parties and their data. Moreover, the outsourcing decryption technique and a
newly proposed key private proxy re-encryption are adapted to shift the
computational complexity of the involved parties to the cloud without
compromising clients’ privacy and service providers’ intellectual property.
Finally, our security and performance analysis demonstrates the effectiveness
of our proposed design.
CloudMoV: Cloud-based Mobile Social TV
REF:2013 IEEE TRANSACTIONS ON MULTIMEDIA
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IMAS: an Intelligent Mobile Advertising System
Ref:International Conference on Advanced Information Networking and Applications Workshops
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Cloud Computing For Mobile Users: Can Offloading Computation Save Energy
Ref: 2013 IEEE TRANSACTIONS ON CLOUD COMPUTING
Project Price: Contact US
ABSTRACT : The cloud heralds a new era of
computing where application services are provided through the Internet. Cloud
computing can enhance the computing capability of mobile systems, but is it the
ultimate solution for extending such systems' battery lifetimes? Cloud
computing1 is a new paradigm in which computing resources such
as processing, memory, and storage are not physically present at the user’s
location. Instead, a service provider owns and manages these resources, and
users access them via the Internet. For example, Amazon Web Services lets users
store personal data via its Simple Storage Service (S3) and perform
computations on stored data using the Elastic Compute Cloud (EC2). This type of computing provides many advantages for
businesses—including low initial capital investment, shorter start-up time for
new services, lower maintenance and operation costs, higher utilization through
virtualization, and easier disaster recovery—that make cloud computing an
attractive option. Reports suggest that there are several benefits in shifting
computing from the desktop to the cloud.1,2 What about cloud computing for
mobile users? The primary constraints for mobile computing are limited energy
and wireless bandwidth. Cloud computing can provide energy savings as a service
to mobile users, though it also poses some unique challenges.
AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds
Project Price: Contact US
Abstract—While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed A MES-Cloud, which has two main parts: adaptive mobile video streaming (AMOV) and efficient social video sharing (ESOV). AMOV and ESOV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMOV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESOVmonitors the social network interactions among mobile users, and their private agents try to pref etch video content in advance. We implement a prototype of the A MES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., pref etching based on the social network analysis.
DEFENSES AGAINST LARGE SCALE
ONLINE PASSWORD GUESSING ATTACKS BY USING PERSUASIVE CLICK POINTS
Project Price: Contact US
Ref: IEEE 2012 International Journal of Communications and Engineering
Abstract
Usable
security has unique usability challenges because the need for security often
means that standard
human-computer-interaction approaches cannot be directly applied. An important
usability goal for authentication systems is to support users in selecting
better passwords. Users often create memorable passwords that are easy for attackers
to guess, but strong system-assigned passwords are difficult for users to
remember. So researchers of modern days have gone for alternative methods
wherein graphical pictures are used as passwords. Graphical passwords
essentially use images or representation of images as passwords. Human brain is
good in remembering picture than textual character. There are various graphical
password schemes or graphical password software in the market. However, very
little research has been done to analyze graphical passwords that are still
immature. There for, this project work merges persuasive
cued click points and password guessing resistant protocol. The major goal of
this work is to reduce the guessing attacks as well as encouraging users to
select more random, and difficult passwords to guess. Well known security
threats like brute force attacks and dictionary attacks can be successfully abolished
using this method.
Efficient
audit service outsourcing for data integrity in clouds
Ref: IEEE
2012 Transactions on Cloud Computing, Volume: 85 , Issue: 5
Project Price: Contact US
Abstract
Cloud-based
outsourced storage relieves the client’s burden for storage management and
maintenance by providing a comparably low-cost, scalable, location-independent
platform. However, the fact that clients no longer have physical possession of
data indicates that they are facing a potentially formidable risk for missing
or corrupted data. To avoid the security risks, audit services are critical to
ensure the integrity and availability of outsourced data and to achieve digital
forensics and credibility on cloud computing. Provable data possession (PDP),
which is a cryptographic technique for verifying the integrity of data without
retrieving it at an un trusted server, can be used to realize audit services. In
this paper, profiting from the interactive zero-knowledge proof system, we
address the construction of an interactive PDP protocol to prevent the
fraudulence of prover (soundness property) and the leakage of verified data
(zero-knowledge property). We prove that our construction holds these
properties based on the
computation Diffie–Hellman assumption and the rewind able black-box knowledge
extractor. We also propose an efficient mechanism with respect to probabilistic
queries and periodic verification to reduce the audit costs per verification
and implement abnormal detection timely. In addition, we present an efficient
method for selecting an optimal parameter value to minimize computational
overheads of cloud audit services. Our experimental results demonstrate the
effectiveness of our approach.
Ensuring
Distributed Accountability for Data Sharing in the Cloud
Ref:2012 IEEE TRANSACTIONS ON DEPENDABLE AND SECURE
COMPUTING
Project Price: Contact US
Abstract
Cloud
computing enables highly scalable services to be easily consumed over the
Internet on an as-needed basis. A major feature of the cloud services is that
users’ data are usually processed remotely in unknown machines that users do
not own or operate. While enjoying the onvenience
brought by this new emerging technology, users’ fears of losing control of
their own data (particularly, financial and health data) can become a
significant barrier to the wide adoption of cloud services. To address this
problem, in this paper, we propose a novel highly decentralized information
accountability framework to keep track of the actual usage of the users’ data
in the cloud. In particular, we propose an object-centered approach that
enables enclosing our logging mechanism together with users’ data and policies.
We leverage the JAR programmable capabilities to both create a dynamic and
traveling object, and to ensure that any access to users’ data will trigger
authentication and automated logging local to the JARs. To strengthen user’s
control, we also provide distributed auditing mechanisms. We provide extensive
experimental studies that demonstrate the efficiency and effectiveness of the
proposed approaches.
Network
Assisted Mobile Computing with Optimal Uplink Query Processing
Project Price: Contact US
Ref: IEEE 2012 Transactions on Mobile Computing, Volume: PP, Issue: 99
Abstract
Many mobile
applications retrieve content from remote servers via user generated queries.
Processing these queries is often needed before the desired content can be
identified. Processing the request on the mobile devices can quickly sap the
limited battery resources. Conversely, processing user-queries at remote
servers can have slow response times due communication latency incurred during
transmission of the potentially large query. We evaluate a network-assisted
mobile computing scenario where mid network nodes with “leasing” capabilities
are deployed by a service provider. Leasing computation power can reduce
battery usage on the mobile devices and improve response times. However,
borrowing processing power from mid-network nodes comes at a leasing cost which
must be accounted for when making the decision of where processing should
occur. We study the tradeoff between battery usage, processing and transmission
latency, and mid-network leasing. We use the dynamic programming framework to
solve for the optimal processing policies that suggest the amount of processing
to be done at each mid-network node in order to minimize the processing and
communication latency and processing costs. Through numerical studies, we
examine the properties of the optimal processing policy and the core tradeoffs
in such systems.
Payments
for Outsourced Computations
Project Price: Contact US
Ref: 2012 IEEE TRANSACTIONS ON PARALLEL AND
DISTRIBUTED SYSTEMS,
Abstract
With the
recent advent of cloud computing, the concept of outsourcing computations,
initiated by volunteer computing efforts, is being revamped. While the two
paradigms differ in several dimensions, they also share challenges, stemming
from the lack of trust between outsourcers and workers. In this work, we
propose a unifying trust framework, where correct participation is financially rewarded:
neither participant is trusted, yet outsourced computations are efficiently
verified and validly remunerated. We propose three solutions for this problem,
relying on an offline bank to generate and redeem payments; the bank is
oblivious to interactions between
outsourcers
and workers. We propose several attacks that can be launched against our
framework and study the effectiveness of our solutions. We implemented our most
secure solution and our experiments show that it is efficient: the bank can
perform hundreds of payment transactions per second and the overheads imposed
on outsourcers and workers are negligible.
Query
Planning for Continuous Aggregation Queries over a Network of Data Aggregators
Project Price: Contact US
Ref:
2012 IEEE Transactions on Knowledge and Data Engineering
Abstract
Continuous
queries are used to monitor changes to time varying data and to provide results
useful for online decision making. Typically a user desires to obtain the value
of some aggregation function over distributed data items, for example, to know
value of portfolio for a client; or the AVG of temperatures sensed by a set of
sensors. In these queries a client specifies a coherency requirement as part of
the query. We present a low-cost, scalable technique to answer continuous aggregation
queries using a network of aggregators of dynamic data items. In such a network
of data aggregators, each data aggregator serves a set of data items at
specific coherencies. Just as various fragments of a dynamic web-page are
served by one or more nodes of a content distribution network, our technique
involves decomposing a client query into sub-queries and executing sub-queries
on judiciously chosen data aggregators with their individual sub-query
incoherency bounds. We provide a technique for getting the optimal set of
sub-queries with their incoherency bounds which satisfies client query’s
coherency requirement with least number of refresh messages sent from
aggregators to the client. For estimating the number of refresh messages, we
build a query cost model which can be used to estimate the number of messages
required to satisfy the client specified incoherency bound. Performance results
using real-world traces show that our cost based query planning leads to queries
being executed using less than one third the number of messages required by
existing schemes.
Ranking
Model Adaptation for Domain-Specific Search
Project Price: Contact US
Ref: IEEE 2012
Transaction on Knowledge and Data Engineering, Volume:24,Issue:4
Abstract
With the
explosive emergence of vertical search domains, applying the broad-based
ranking model directly to different domains is no longer desirable due to
domain differences, while building a unique ranking model for each domain is
both laborious for labeling data and time-consuming for training models. In
this paper, we address these difficulties by proposing a regularization based algorithm
called ranking adaptation SVM (RA-SVM), through which we can adapt an existing
ranking model to a new domain, so that the amount of labeled data and the
training cost is reduced while the performance is still guaranteed. Our
algorithm only requires the prediction from the existing ranking models, rather
than their internal representations or the data from auxiliary domains. In
addition, we assume that documents similar in the domain-specific feature space
should have consistent rankings, and add some constraints to control the margin
and slack variables of RA-SVM adaptively. Finally, ranking
adaptability measurement is proposed to quantitatively estimate if an existing
ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale datasets crawled
from a commercial search engine demonstrate the applicabilities of the proposed
ranking adaptation algorithms and the ranking adaptability measurement.
Scalable
and Secure Sharing of Personal Health Records in Cloud Computing using Attribute-based Encryption
Project Price: Contact US
Ref: IEEE 2012 Transactions on Parallel and Distributed Systems, Volume: PP
, Issue:99
Abstract
Personal
health record (PHR) is an emerging patient-centric model of health information
exchange, which is often outsourced to be stored at a third party, such as
cloud providers. However, there have been wide privacy concerns as personal
health information could be exposed to those third party servers and to
unauthorized parties. To assure the patients’ control over access to their own
PHRs, it is a promising method to encrypt the PHRs before outsourcing. Yet,
issues such as risks of privacy exposure, scalability in key management,
flexible access and efficient user revocation, have remained the most important
challenges toward achieving fine-grained, cryptographically enforced data
access control. In this paper, we propose a novel patient-centric framework and
a suite of mechanisms for data access control to PHRs stored in semi-trusted
servers. To achieve fine-grained and
scalable data access control for PHRs, we leverage attribute based encryption
(ABE) techniques to encrypt each patient’s PHR file. Different from previous
works in secure data outsourcing, we focus on the multiple data owner scenario,
and divide the users in the PHR system into multiple security domains that
greatly reduces the key management complexity for owners and users. A high
degree of patient privacy is guaranteed simultaneously by exploiting
multi-authority ABE. Our scheme also enables dynamic modification of access
policies or file attributes, supports efficient on-demand user/attribute
revocation and break-glass access under emergency scenarios. Extensive analytical
and experimental results are presented which show the security, scalability and
efficiency of our proposed scheme.
SPOC: A Secure and Privacy-preserving Opportunistic Computing
Framework for Mobile-Healthcare Emergency
Project Price: Contact US
Ref: 2012 IEEE Transactions
on Parallel and Distributed Systems, Volume: PP, Issue: 99
Abstract
With
the pervasiveness of smart phones and the advance of wireless body sensor
networks (BSNs), mobile Healthcare (m-Healthcare), which extends the operation
of Healthcare provider into a pervasive environment for better health
monitoring, has attracted considerable interest recently. However, the flourish
of m-Healthcare still faces many challenges including information security and
privacy preservation. In this paper, we propose a secure and privacy-preserving
opportunistic computing framework, called SPOC, for m-Healthcare emergency.
With SPOC, smart phone resources including computing power and energy can be
opportunistically gathered to process the computing-intensive personal health
information (PHI) during m-Healthcare emergency with minimal privacy disclosure.
In specific, to leverage the PHI privacy disclosure and the high reliability of
PHI process and transmission in m-Healthcare emergency, we introduce an
efficient user-centric privacy access control in SPOC framework, which is based
on an attribute-based access control and a new privacy-preserving scalar
product computation (PPSPC) technique, and allows a medical user to decide who can
participate in the opportunistic computing to assist in processing his
overwhelming PHI data. Detailed security analysis shows that the proposed SPOC
framework can efficiently achieve user-centric privacy access control in
m-Healthcare emergency. In addition, performance evaluations via extensive
simulations demonstrate the SPOC’s effectiveness in term of providing high
reliable PHI process and transmission while minimizing the privacy disclosure
during m-Healthcare emergency.
The Three-Tier
Security Scheme in Wireless Sensor Networks with Mobile Sinks
Project Price: Contact US
Ref: IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED
SYSTEMS, VOL. 23, NO. 5,
Abstract: Mobile
sinks (MSs) are vital in many wireless sensor network (WSN) applications for
efficient data accumulation, localized sensor reprogramming, and for
distinguishing and revoking compromised sensors. However, in sensor networks
that make use of the existing key pre distribution schemes for pair wise key
establishment and authentication between sensor nodes and mobile sinks, the employment
of mobile sinks for data collection elevates a new security challenge: in the
basic probabilistic and q-composite key pre distribution schemes, an
attacker can easily obtain a large number of keys by capturing a small fraction
of nodes, and hence, can gain control of the network by deploying a replicated
mobile sink preloaded with some compromised keys. This article describes a three-tier
general framework that permits the use of any pair wise key pre distribution
scheme as its basic component. The new framework requires two separate key
pools, one for the mobile sink to access the network, and one for pair wise key
establishment between the sensors. To further reduce the damages caused by
stationary access node replication attacks, we have strengthened the authentication
mechanism between the sensor and the stationary access node in the proposed
framework. Through detailed analysis, we show that our security framework has a
higher network resilience to a mobile sink replication attack as compared to
the polynomial pool-based scheme.
Music Recommendation Using Content and Context Information Mining
Abstract— Mobile devices such as smart phones are becoming popular, and real time access to multimedia data in different environments is getting easier. With properly equipped communication services, users can easily obtain the widely distributed videos, music, and documents they want. Because of its usability and capacity requirements, music is more popular than other types of multimedia data. Documents and videos are difficult to view on mobile phones’ small screens, and videos’ large data size results in high overhead for retrieval. But advanced compression techniques for music reduce the required storage space significantly and make the circulation of music data easier. This means that users can capture their favorite music directly from the Web without going to music stores. Accordingly, helping users find music they like in a large archive has become an attractive but challenging issue over the past few years. Traditional music recommenders have been based primarily on collaborative filtering (CF). But their effectiveness has been limited by insufficient information, including sparse rating data and a lack of contextual information. Sparse rating data occurs frequently in real applications and can result in a distorted recommendation list. In addition, a user’s preferences can vary in different contexts, such as location, time, movement state, and temperature. For example, someone jogging might prefer hip-hop to classical music. A survey showed that activity (a type of context information) significantly affects a listener’s mood.1 This finding delivers an important message that context information is an important element for a music recommender to consider in selecting music to suit the listener’s mood.
Music Recommendation Using Content and Context Information Mining
Abstract— Mobile devices such as smart phones are becoming popular, and real time access to multimedia data in different environments is getting easier. With properly equipped communication services, users can easily obtain the widely distributed videos, music, and documents they want. Because of its usability and capacity requirements, music is more popular than other types of multimedia data. Documents and videos are difficult to view on mobile phones’ small screens, and videos’ large data size results in high overhead for retrieval. But advanced compression techniques for music reduce the required storage space significantly and make the circulation of music data easier. This means that users can capture their favorite music directly from the Web without going to music stores. Accordingly, helping users find music they like in a large archive has become an attractive but challenging issue over the past few years. Traditional music recommenders have been based primarily on collaborative filtering (CF). But their effectiveness has been limited by insufficient information, including sparse rating data and a lack of contextual information. Sparse rating data occurs frequently in real applications and can result in a distorted recommendation list. In addition, a user’s preferences can vary in different contexts, such as location, time, movement state, and temperature. For example, someone jogging might prefer hip-hop to classical music. A survey showed that activity (a type of context information) significantly affects a listener’s mood.1 This finding delivers an important message that context information is an important element for a music recommender to consider in selecting music to suit the listener’s mood.
A Flexible Approach to Multi session Trust Negotiations
Abstract—Trust Negotiation has shown to be a successful, policy-driven approach for automated trust establishment, through the release of digital credentials. Current real applications require new flexible approaches to trust negotiations, especially in light of the widespread use of mobile devices. In this paper, we present a multisession dependable approach to trust negotiations. The proposed framework supports voluntary and unpredicted interruptions, enabling the negotiating parties to complete the negotiation despite temporary unavailability of resources. Our protocols address issues related to validity, temporary loss of data, and extended unavailability of one of the two negotiators. A peer is able to suspend an ongoing negotiation and resume it with another (authenticated) peer. Negotiation portions and intermediate states can be safely and privately passed among peers, to guarantee the stability needed to continue suspended negotiations. We present a detailed analysis showing that our protocols have several key properties, including validity, correctness, and minimality. Also, we show how our negotiation protocol can withstand the most significant attacks. As by our complexity analysis, the introduction of the suspension and recovery procedures and mobile negotiations does not significantly increase the complexity of ordinary negotiations. Our protocols require a constant number of messages whose size linearly depend on the portion of trust negotiation that has been carried before the suspensions.
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