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ANSPRO TECHNOLOGIES

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IEEE 2017-18 PROJECT LIST

Android

18ANSP-AD-001 My Privacy My Decision: Control of Photo Sharing on Online Social

Networks

Photo sharing is an attractive feature which popularizes online social

networks (OSNs). Unfortunately, it may leak users’ privacy if they are

allowed to post, comment, and tag a photo freely. In this paper, we

attempt to address this issue and study the scenario when a user shares

a photo containing individuals other than himself/herself (termed co-

photo for short). To prevent possible privacy leakage of a photo, we

design a mechanism to enable each individual in a photo be aware of

the posting activity and participate in the decision making on the photo

posting. For this purpose, we need an efficient facial recognition (FR)

system that can recognize everyone in the photo. However, more

demanding privacy setting may limit the number of the photos publicly

available to train the FR system. To deal with this dilemma, our

mechanism attempts to utilize users’ private photos to design a

personalized FR system specifically trained to differentiate possible

photo co-owners without leaking their privacy. We also develop a

distributed consensus-based method to reduce the computational

complexity and protect the private training set. We show that our

system is superior to other possible approaches in terms of recognition

ratio and efficiency. Our mechanism is implemented as a proof of

concept Android application on Facebook’s platform. 18ANSP-AD-002 Understanding Smartphone Sensor and App Data for Enhancing the

Security of Secret Questions

Many web applications provide secondary authentication methods,

i.e., secret questions (or password recovery questions), to reset the

account password when a user’s login fails. However, the answers to

many such secret questions can be easily guessed by an acquaintance

or exposed to a stranger that has access to public online tools (e.g.,

online social networks); moreover, a user may forget her/his answers

long after creating the secret questions. Today’s prevalence of

smartphones has granted us new opportunities to observe and

understand how the personal data collected by smartphone sensors and

apps can help create personalized secret questions without violating

the users’ privacy concerns. In this paper, we present a Secret-

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Question based Authentication system, called “Secret-QA”, that

creates a set of secret questions on basic of people’s smartphone usage.

We develop a prototype on Android smartphones, and evaluate the

security of the secret questions by asking the acquaintance/stranger

who participates in our user study to guess the answers with and

without the help of online tools; meanwhile, we observe the questions’

reliability by asking participants to answer their own questions. Our

experimental results reveal that the secret questions related to motion

sensors, calendar, app installment, and part of legacy app usage history

(e.g., phone calls) have the best memorability for users as well as the

highest robustness to attacks. 18ANSP-AD-003 Soft Hint Enabled Adaptive Visible Light Communication over

Screen-Camera Links

Screen-camera links for Visible Light Communication (VLC) are

diverse, as the link quality varies according to many factors, such as

ambient light and camera’s performance. This paper presents

SoftLight, a channel coding approach that considers the unique

channel characteristics of VLC links and automatically adapts the

transmission data rate to the link qualities of various scenarios.

SoftLight incorporates two new ideas: (1) an expanded color

modulation interface that provides soft hint about its confidence in

each demodulated bit and establishes a bit-level VLC erasure channel,

and (2) a rateless coding scheme that achieves bit-level rateless

transmissions with low computation complexity and tolerates the false

positive of bits provided by the soft hint enabled erasure channel.

SoftLight is orthogonal to the visual coding schemes and can be

applied atop any barcode layouts. We implement SoftLight on Android

smartphones and evaluate its performance under a variety of

environments. The experiment results show that SoftLight can

correctly transmit a 22-KByte photo between two smartphones within

0.6 second and improves the average goodput of the state-of-the-art

screen-camera VLC solution by 2.2. 18ANSP-AD-004 Design and Analysis of an Efficient Friend-to-Friend Content

Dissemination System

Opportunistic communication, off-loading, and decentrlaized

distribution have been proposed as a means of cost efficient

disseminating content when users are geographically clustered into

communities. Despite its promise, none of the proposed systems have



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not been widely adopted due to unbounded high content delivery

latency, security, and privacy concerns. This paper, presents a novel

hybrid content storage and distribution system addressing the trust and

privacy concerns of users, lowering the cost of content distribution and

storage, and shows how they can be combined uniquely to develop

mobile social networking services. The system exploit the fact that

users will trust their friends, and by replicating content on friends’

devices who are likely to consume that content it will be possible to

disseminate it to other friends when connected to low cost networks.

The paper provides a formal definition of this content replication

problem, and show that it is NP hard. Then, it presents a community

based greedy heuristic algorithm with novel dynamic centrality

metrics that replicates the content on a minimum number of friends’

devices, to maximize availability. Then using both real world and

synthetic datasets, the effectiveness of the proposed scheme is

demonstrated. The practicality of the proposed system, is

demonstrated through an implementation on Android smartphones. 18ANSP-AD-005 CrowdNavi: Demystifying Last Mile Navigation With Crowdsourced

Driving Information

With detailed digital map of the transport network and even real-time

traffic, today’s navigation services provide good quality routes in the

major route level. Once entering the last mile near the destination, they

unfortunately can be ineffective and, instead, local drivers often have

a better understanding of the routes there. With the deep penetration of

3G/4G mobile networks, drivers today are well connected anytime and

anywhere; they can readily access information from the Internet and

share information to the driver’s community. This motivates our

design of CrowdNavi, a complementary service to existing navigation

systems, seeking to combat the last mile puzzle. CrowdNavi collects

the crowdsourced driving information from users to identify their local

driving patterns, and recommend the best local routes for users to reach

their destinations. In this paper, we present the architectural design of

CrowdNavi and identifies the unique challenges therein, particularly

on identifying the last segment in a route from the crowdsourced

driving information and navigate drivers through the last segment. We

offer a complete set of algorithms to identify the last segment from the

drivers’ trajectories, scoring the landmark, and locating best routes

with user preferences. We then present effective navigation algorithm



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to locate the best route along the landmarks for the last segment.We

further realize the potential risks of attacks in crowdsourced systems

and develop a multisensor crossvalidation method against them. We

have implemented the CrowdNavi app on Android mobile OS, and

have examined its performance under various circumstances. The

experimental results demonstrate its superiority in navigating drivers

in the last segment toward the destination. 18ANSP-AD-006 Wi-Fi Seeker: A link and Load Aware AP Selection Algorithm

The demand of mobile traffic increases tremendously as various

Internet services are provided to mobile phones and computing

devices, making mobile carriers increase the capacity of their cellular

network. Since current cell towers and auxiliary devices cannot carry

an excessive amount of traffic, several mobile carriers try to install low

cost wireless equipment such as a Wi-Fi access point (AP) in order to

offload overloaded traffic on cell towers. However, APs are installed

randomly unlike cell towers because they can be installed by anyone

and anywhere. The randomized allocation of APs incurs interference

that causes serious degradation of wireless communication quality.

Therefore, it is necessary to design mobile devices to use the

environment or channel that contains the least interference for proper

Wi-Fi communication. In order to make a mobile device experience

the best wireless download quality, this paper proposes a novel AP

selection mechanism that selects an AP that contains the least

interference level compared to others with different number of

communicating devices and amount of traffic load. The proposed

selection mechanism does not require any additional feedback from

near by devices or APs. Performance evaluation was done by using

real mobile devices with open source platform called Android. 18ANSP-AD-007 SMASheD: Sniffing and Manipulating Android Sensor Data for

Offensive Purposes

The current Android sensor security model either allows only

restrictive read access to sensitive sensors (e.g., an app can only read

its own touch data) or requires special installtime permissions (e.g., to

read microphone, camera, or GPS). Moreover, Android does not allow

write access to any of the sensors. Sensing-based security and non-

security applications, therefore, crucially rely upon the sanity of the

Android sensor security model. In this paper, we show that such a

model can be effectively circumvented. Specifically, we build



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SMASheD, a legitimate framework under the current Android

ecosystem that can be used to stealthily sniff as well as manipulate

many of the Android’s restricted sensors (even touch input). SMASheD

exploits the Android debug bridge functionality and enables a

malicious app with only the INTERNET permission to read, and write

to, multiple different sensor data files at will. SMASheD is the first

framework, to the best of our knowledge that can sniff and manipulate

protected sensors on unrooted Android devices, without user

awareness, without constant device-PC connection and without the

need to infect the PC. The primary contributions of this paper are

twofold. First, we design and develop the SMASheD framework, and

evaluate its effectiveness on multiple Android devices, including

phones, watches, and glasses. Second, as an offensive implication of

the SMASheD framework, we introduce a wide array of potentially

devastating attacks. Our attacks against the touch sensor range from

accurately logging the touchscreen input (TouchLogger) to injecting

touch events for accessing restricted sensors and resources, installing

and granting special permissions to other malicious apps, accessing

user accounts, and authenticating on behalf of the user—essentially

almost doing whatever the device user can do (secretively). Our attacks

against various physical sensors (motion, position, and environmental)

can subvert the functionality provided by numerous existing sensing-

based security and non-security applications, including those used for

(continuous) authentication, authorization, safety, and elderly care. 18ANSP-AD-008 Design of a New Mobile-Optimized Remote Laboratory Application

Architecture for M-Learning

As mobile learning (M-Learning) has demonstrated increasing impacts

on online education, more and more mobile applications are designed

and developed for the M-Learning. In this paper, a new mobile-

optimized application architecture using Ionic framework is proposed

to integrate the remote laboratory into mobile environment for the M-

Learning. With this mobile-optimized application architecture, remote

experiment applications can use a common codebase to deploy native-

like applications on many different mobile platforms such as iOS,

Android, Windows Mobile, and Blackberry. To demonstrate the

effectiveness of the proposed new architecture for M-Learning, an

innovative remote networked proportional–integral–derivative control

experiment has been successfully implemented based on this new



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application architecture. The performance is validated by the Baidu

mobile cloud testing bed. 18ANSP-AD-009 Group-Aware Delay-Constrained Video Transmission Over

Multihomed Device-to-Device Networks

The technological advancements in wireless communication systems

enable mobile users to leverage different radio interfaces (e.g., cellular

and WiFi) for concurrent data transmission. However, the existing

transmission schemes do not seriously consider the problem of real-

time video multicast to a cluster of co-located multihomed mobile

devices. Conventionally, each client fetches the video streaming to the

best of its capability, and this results in competing resources that

degrade user-perceived video quality. Several literatures investigated

the problem of using cellular to obtain video contents from the remote

server and sharing them through WiFi. However, the stringent delay

constraint of real-time video is not addressed in these solutions. In this

paper, a cooperative transmission scheme is proposed to tackle the

problem. First, a mathematical framework dubbed (Group-Aware

Delay-COnstraint) is developed to formulate the delay-constrained

good put maximization problem of real-time video transmission to a

group of multihomed mobiles. Second, a dataflow distribution

mechanism is presented to conserve the cellular bandwidth and

maximize user experience. Then, a prototype is implemented on the

Android platform involving real-time video encoded with H.264

codec. Experimental results show that the proposed scheme achieves

appreciable improvements over the reference schemes in video peak

signal-to-noise ratio, end-to-end delay, and goodput. 18ANSP-AD-010 RoadSense: Smartphone Application to Estimate Road Conditions

Using Accelerometer and Gyroscope

Monitoring the road condition has acquired a critical significance

during recent years. There are different reasons behind broadening

research on this field: to start with, it will guarantee safety and comfort

to different road users; second, smooth streets will cause less damage

to the car. Our motivation is to create a real-time Android Application

RoadSense that automatically predicts the quality of the road based on

a triaxial accelerometer and a gyroscope, show the road location trace

on a geographic map using GPS, and save all recorded workout entries.

C4.5 Decision tree classifier is applied on training data to classify road

segments and to build our model. Our experimental results show



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consistent accuracy of 98.6%. Using this approach, we expect to

visualize a road quality map of a selected region. Hence, we can

provide constructive feedback to drivers and local authorities. Besides,

road manager can benefit from this system to evaluate the state of their

road network and make a checkup on road construction projects,

whether they meet or not the required quality. 18ANSP-AD-011 Body and Fall Detection System with Heart Rate Monitoring

Falls can result in physical and psychological trauma, especially for

the elderly. In order to improve the quality of life of these patients this

work presents the development of a fall detection and body positioning

with a heart rate monitoring system. This system consists of the

sensing equipment, gateway and a real-time patient monitoring

structure. The sensing device obtains information from accelerometers

and gyroscope and sends them to the gateway via ZigBee technology,

which makes the processing of the data and sends them to the cloud.

The current patient body position and temperature sensing are

provided through a web platform and Android application. The body,

walking and falls position were respectively satisfactory in 100%, 90%

and 60% of cases during tests at the laboratory. We conclude that our

current proposal achieved the goal of patient fall detection with a

corporal temperature monitoring using a low cost budget

implementation. 18ANSP-AD-012 An Interactive Telecare System Enhanced with IoT Technology

Information technology, coupled with the rising popularity of mobile

devices, provides an opportunity to enhance self-management of

disease without the need for expensive medical treatments. 1

Numerous studies indicate that behavior change is the most effective

self-care method for patients living with a chronic disease.2,3 For

example, research has suggested that long-term blood glucose control

is an effective way to reduce and defer complications of diabetes.4,5

Thus, many researchers have adopted Internet communication

technologies, such as Bluetooth and wireless local area networks

(WLAN), in developing healthcare systems that are more convenient

and have fewer recording errors. However, such systems are limited in

terms of use location, resulting in less-than-ideal responsiveness and

mobility. Telecare lets mobile devices confirm patient safety through

automatic and remote monitoring. Currently, most telecare systems

that adopt wireless communications technology access server



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databases only for text or multimedia message communication, and

smartphones and computers serve as the receiving devices (see the

“Related Work in Health Monitoring” sidebar). These systems mainly

focus on the transmission of multimedia data from a hospital’s

database; they don’t allow direct communication among family

caregivers, paramedics, and patients.6,7 However, using

communications technology to build an interactive model that links

patients’ medical devices to smartphones of family caregivers can

improve the quality of patient selfcare by motivating patients to adopt

healthy behaviors and by increasing family support. When family

caregivers acknowledge positive behavior changes in patients, it can

enhance the quality of patient self-care. 18ANSP-AD-013 E-Spector: Online Energy Inspection for Android Applications

Energy consumption is one of the most important aspects of mobile

apps. During energy testing, it is important for developers to

understand not only the energy consumption rate of an app, but also

why energy is consumed. However, existing energy testing tools are

more concerned about the accuracy of energy estimation, while

typically not providing explanations on why and how exactly energy

has been consumed. This paper presents E-Spector, an online energy

inspection method for Android apps, which can not only visualize the

energy consumption of an app in an instant online manner, but also can

tell what happened behind each energy hotspot on the energy curve. E-

Spector relies on static analysis and app instrumentation to collect the

activities from an app execution in real-time. Then it presents the

activities on an instant energy curve, such that the user can easily tell

what happened behind each energy spike. Experimental result shows

that the energy estimation error of ESpector is less than 10% and its

overhead on energy consumption is about 4%. We also show case

studies to demonstrate the applicability and effectiveness of E-Spector

in energy monitoring, analysis and bug inspection. 18ANSP-AD-014 A Secure, Usable, and Transparent Middleware for Permission

Managers on Android

Android’s permission system offers an all-or-nothing choice when

installing an app. To make it more flexible and fine-grained, users may

choose a popular app tool, called permission manager, to selectively

grant or revoke an app’s permissions at runtime. A fundamental

requirement for such permission manager is that the granted or



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revoked permissions should be enforced faithfully. However, we

discover that none of existing permission managers meet this

requirement due to permission leaks, in which an unprivileged app can

exercise certain permissions which are revoked or not-granted through

communicating with a privileged app. To address this problem, we

propose a secure, usable, and transparent OS-level middleware for any

permission manager to defend against the permission leaks. The

middleware is provably secure in a sense that it can effectively block

all possible permission leaks. The middleware is designed to have a

minimal impact on the usability of running apps. In addition, the

middleware is transparent to users and app developers and it requires

minor modifications on permission managers and Android OS.

Finally, our evaluation shows that the middleware incurs relatively low

performance overhead and power consumption. 18ANSP-AD-015 Active Authentication on Mobile Devices via Stylometry, Application

Usage, Web Browsing, and GPS Location

Active authentication is the problem of continuously verifying the

identity of a person based on behavioral aspects of their interaction

with a computing device. In this paper, we collect and analyze

behavioral biometrics data from 200 subjects, each using their personal

Android mobile device for a period of at least 30 days. This data set

is novel in the context of active authentication due to its size, duration,

number of modalities, and absence of restrictions on tracked activity.

The geographical colocation of the subjects in the study is

representative of a large closed-world environment such as an

organization where the unauthorized user of a device is likely to be an

insider threat: coming from within the organization. We consider four

biometric modalities: 1) text entered via soft keyboard, 2) applications

used, 3) websites visited, and 4) physical location of the device as

determined from GPS (when outdoors) or Wi-Fi (when indoors).We

implement and test a classifier for each modality and organize the

classifiers as a parallel binary decision fusion architecture. We are able

to characterize the performance of the system with respect to intruder

detection time and to quantify the contribution of each modality to the

overall performance. 18ANSP-AD-016 An Android-Based Mechanism for Energy Efficient Localization

Depending on Indoor/Outdoor Context



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Today, there is widespread use of mobile applications that take

advantage of a user’s location. Popular usages of location information

include geo tagging on social media websites, driver assistance and

navigation, and querying nearby locations of interest. However, the

average user may not realize the high energy costs of using location

services (namely the GPS) or may not make smart decisions regarding

when to enable or disable location services—for example, when

indoors. As a result, a mechanism that can make these decisions on the

user’s behalf can significantly improve a smartphone’s battery life. In

this paper, we present an energy consumption analysis of the

localization methods available on modern Android smartphones and

propose the addition of an indoor localization mechanism that can be

triggered depending on whether a user is detected to be indoors or

outdoors. Based on our energy analysis and implementation of our

proposed system, we provide experimental results—monitoring

battery life over time—and show that an indoor localization method

triggered by indoor or outdoor context can improve smartphone battery

life and, potentially, location accuracy. 18ANSP-AD-017

18ANSP-AD-018 Overcoming IoT Language Barriers Using Smartphone SDRs

In the Internet of Things (IoT) era, smartphones are expected to

frequently interact with IoT devices and even facilitate various IoT

applications. Due to limited roles, energy constraints, etc., however,

IoT devices may use mission-tailored or proprietary wireless protocols

that smartphones do not speak natively. In this paper, we propose a

novel approach to the wireless “language barrier” problem between the

smartphones and IoT devices of the future. We first demonstrate that

smartphones have become powerful enough to process software

defined radio (SDR) for some known wireless protocols. Moreover,

we show that the SDRs can be packaged as “apps” and be downloaded

from app stores for OS-independent deployment. Second, we show

different SDR protocols on the smartphone can concurrently run

through a shared RF to serve multi-tasked applications on it as might

happen in diversified IoT environments. For proof-of-concept, we

implement a prototype architecture that has all the SDR logic and

supporting middleware on an Android smartphone which uses a USRP

as the simple RF-end. Finally, we demonstrate that IEEE 802.11p and

IEEE 802.15.4 SDRs on a smartphone, respectively, communicate

with a ZigBee sensor mote, a ZigBee smart light bulb, and a



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commercial Wireless Access in Vehicular Environment (WAVE)

device, concurrently. 18ANSP-AD-019

18ANSP-AD-020 Learn to Recognize: Exploring Priors of Sparse Face Recognition on

Smartphones

Face recognition is one of the important components of many smart

devices apps, e.g., face unlocking, people tagging and games on smart

phones, tablets, or smart glasses. Sparse Representation Classification

(SRC) is a state-of-the-art face recognition algorithm, which has been

shown to outperform many classical face recognition algorithms in

Open CV, e.g., Eigen face algorithm. The success of SRC is due to its

use of ‘1 optimization, which makes SRC robust to noise and

occlusions. Since ‘1 optimization is computationally intensive, SRC

uses random projection matrices to reduce the dimension of the ‘1

problem. However, random projection matrices do not give consistent

classification accuracy as they ignored the prior knowledge of the

training set. In this paper, we propose to exploit the prior knowledge

of the training set to improve the recognition accuracy. It first learns

the optimized projection matrix from the training set to produce

consistent recognition performance then applies ‘1-based

classification based on the group sparsity structure of SRC to further

improve the recognition accuracy. Our evaluations, based on publicly

available databases and real experiment, show that face recognition

using optimized projection matrix is 8-17 percent more accurate than

its random counterpart and Eigen face algorithm, and the recognition

accuracy can be further improved by up to 5 percent by exploiting

group sparsity structure. Furthermore, the optimized projection matrix

does not have to be re-calculated even if new faces are added to the

training set. We implement the SRC with optimized projection matrix

on Android smartphones and find that the computation of residuals in

SRC is a severe bottleneck, taking up 85-90 percent of the computation

time. To address this problem, we propose a method to compute the

residuals approximately, which is 50 times faster with little sacrificing

recognition accuracy. Lastly, we demonstrate the feasibility of our new

algorithm by the implementation and evaluation of a new face

unlocking app and show its robustness to variation of poses, facial

expressions, lighting changes, and occlusions. 18ANSP-AD-021 WARM: Workload-Aware Reliability Management in Linux/Android



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With CMOS scaling beyond 14 nm, reliability is a major concern for

IC manufacturers. Reliability-aware design has a non-negligible

overhead and cannot account for user experience in mobile devices.

An alternative is dynamic reliability management (DRM), which

counteracts degradation by adapting the operating conditions at

runtime. In this paper, for the first time we formulate DRM as an

optimization problem that accounts for reliability, temperature and

performance. We develop an optimal policy for multicores using

convex optimization, and show that it is not feasible to implement on

real systems. or this reason, we propose workload-aware reliability

management (WARM), a fast DRM technique adapting to diverse

workload requirements to trade reliability and user experience.

WARM is implemented and tested on a real Android device. WARM

approximates the solution of the convex solver within 5% on average,

while executing more than 400× faster. WARM integrates a thermal

controller that allocates tasks to meet thermal constraints. This is

required since degradation strongly depends on temperature. We show

that WARM meets temperature constraints within 5% in 87.5% more

cases than the state-of-the-art. We show that WARM task allocation

achieves up to one year lifetime improvement for a multicore

platform. It can achieve up to 100% of performance improvement on

cluster architectures, such as big. LITTLE, while still guaranteeing the

reliability target. Finally, we show that it achieves performance in the

4% of the maximum for a broad range of a applications, while meeting

the reliability constraints. 18ANSP-AD-022 Design and Implementation of Context Aware Applications With

Wireless Sensor Network Support in Urban Train Transportation

Environments

Transportation system is experiencing steady growth, with the aim of

providing more efficient, reliable, and comfortable services, in the

framework of intelligent transportation systems. Moreover, context

aware environments are one of the main drivers in the achievement of

smart cities and smart regions. In this paper, wireless sensor networks

(WSNs) embedded in urban transportation systems will be analyzed

in terms of impact on wireless channel behavior and system

performance. An in-house developed 3-D ray launching tool, with he

inclusion of in-house human body model as well as with he study of

interference levels is employed. Wireless channel estimations indicate

an initial infrastructure node density in the order of 1node/150m2 to



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1node/500m2 as a function of the employed transceivers. A practical

solution has been implemented, combining an Android-based

application and a system level architecture over the WSN for urban

train transportation environmental monitoring, providing interaction

between users and the environment, with the aid of a combined

WSN/WLAN platform as well as an implemented software

architecture, scalable in terms of user density and future needs. 18ANSP-AD-023 When and Why Your Code Starts to Smell Bad (and Whether the

Smells Go Away)

Technical debt is a metaphor introduced by Cunningham to indicate

“not quite right code which we postpone making it right”. One

noticeable symptom of technical debt is represented by code smells,

defined as symptoms of poor design and implementation choices.

Previous studies showed the negative impact of code smells on the

comprehensibility and maintainability of code. While the

repercussions of smells on code quality have been empirically

assessed, there is still only anecdotal evidence on when and why bad

smells are introduced, what is their survivability, and how they are

removed by developers. To empirically corroborate such anecdotal

evidence, we conducted a large empirical study over the change history

of 200 open source projects. This study required the development of

a strategy to identify smell-introducing commits, the mining of over

half a million of commits, and the manual analysis and classification

of over 10K of them. Our findings mostly contradict common wisdom,

showing that most of the smell instances are introduced when an

artifact is created and not as a result of its evolution. At the same time,

80 percent of smells survive in the system. Also, among the 20 percent

of removed instances, only 9 percent are removed as a direct

consequence of refactoring operations. 18ANSP-AD-024 ShakeUnlock: Securely Transfer Authentication States Between

Mobile Devices

As users start carrying multiple mobile devices, we propose a novel,

token based mobile device unlocking approach. Mobile devices are

conjointly shaken to transfer the authentication state from an unlocked

token device to another device to unlock it. A common use case

features a wrist watch as token device, which remains unlocked as long

as it is strapped to the user’s wrist, and a locked mobile phone, which

is unlocked if both devices are shaken conjointly. Shaking can be done



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single-handedly, requires little user attention (users don’t have to look

at the device for unlocking it), and does not cause additional cognitive

load on users. In case attackers gain control over the locked phone,

forging shaking is difficult, which impedes malicious unlocks. We

evaluate our approach using acceleration records from our 29 people

sized ShakeUnlock database and discuss influence of its constituent

parts on the system performance. We further present a performance

study using an Android implementation and live data, which shows the

true negative rate of observational attacks to be in the range of 0.8—if

an attacker manages to gain control over the locked device and shake

it in parallel to the device owner shaking the token device. 18ANSP-AD-025

18ANSP-AD-026

18ANSP-AD-027 Lightweight Detection of Android-Specific Code Smells: The aDoctor

Project

Code smells are symptoms of poor design solutions applied by

programmers during the development of software systems. While the

research community devoted a lot of effort to studying and devising

approaches for detecting the traditional code smells defined by Fowler,

little knowledge and support is available for an emerging category of

Mobile app code smells. Recently, Reimann et al. proposed a new

catalogue of Android specific code smells that may be a threat for the

maintainability and the efficiency of Android applications. However,

current tools working in the context of Mobile apps provide limited

support and, more importantly, are not available for developers

interested in monitoring the quality of their apps. To overcome these

limitations, we propose a fully automated tool, coined ADOCTOR,

able to identify 15 Android-specific code smells from the catalogue

by Reimann et al. An empirical study conducted on the source code of

18 Android applications reveals that the proposed tool reaches, on

average, 98% of precision and 98% of recall. We made ADOCTOR

publicly available. 18ANSP-AD-028 Stronger Authentication for Password Credential Internet Services

Most Web and other on-line service providers (”Internet Services”)

only support legacy ID (or email) and password (ID/PW) credential

authentication. However, there are numerous vulnerabilities

concerning ID/PW credentials. Scholars and the industry have



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proposed several improved security solutions, such as MFA, however

most of the Internet Services have refused to adopt these solutions.

Mobile phones are much more sensitive to these vulnerabilities (so this

paper focuses on mobile phones). Many users take advantage of

password managers, to keep track of all their Internet Service profiles.

However, the Internet service profiles found in password managers,

are normally kept on the PC or mobile phone’s disk, in an encrypted

form. Our first contribution is a design guideline, whereby the Internet

Service profiles never need to touch the client’s disk. Most users would

benefit, if they had the ability to use MFA, to login to a legacy Internet

Service, which only supports ID/PW credential authentication. Our

second contribution is a design guideline, whereby users can choose,

for each legacy ID/PW Internet Service, which specific MFA they

wish to use. We have also presenting conceptual design guidelines,

showing that both of our contributions are minor changes to existing

password managers, which can be implemented easily with low

overhead. 18ANSP-AD-029 WAIPO: A Fusion-Based Collaborative Indoor Localization System

on Smartphones

Indoor localization based on smartphone can enhance user’s

experiences in indoor environments. Although some innovative

solutions have been proposed in the past two decades, how to

accurately and efficiently localize users in indoor environments is still

a challenging problem. Traditional indoor positioning systems based

on Wi-Fi fingerprints or dead reckoning suffer from the variation of

Wi-Fi signals and the drift f dead reckoning problems, respectively.

Crowdsourcing and ambient sensing stimulate new ways to improve

existing localization systems’ accuracy. Using human social factors to

calibrate the accuracy of localization is practical and awarding. In this

paper, we propose WAIPO, a collaborative indoor localization system

with the fusion of Wi-Fi and magnetic fingerprints, image matching,

and people co-occurrence. Specifically, we could obtain the most

likely top-n locations based on Wi-Fi fingerprints. We utilize the

statistics of users’ historical locations known by image-matching, for

which we propose a photo-room matching algorithm, to reduce

estimating areas. In order to further improve the accuracy of

localization, we propose a co-occurrence and non-co-occurrence

detection algorithm to detect users’ spatial temporal co-occurrence and

determine users’ locations with magnetic calibration. We have fully



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implemented WAIPO on the Android platform and perform test bed

experiments. The experimental results demonstrate that WAIPO

achieves an accuracy of 87.3% on average, which outperforms the

state-of-the-art indoor localization systems. 18ANSP-AD-030 Developing and evaluating a mobile driver fatigue detection network

based on electroencephalograph signals

The rapid development of driver fatigue detection technology indicates

important significance of traffic safety. The authors’ main goals of this

Letter are principally three: (i) A middleware architecture, defined as

process unit (PU), which can communicate with personal

electroencephalography (EEG) node (PEN) and cloud server (CS). The

PU receives EEG signals from PEN, recognizes the fatigue state of the

driver, and transfer this information to CS. The CS sends notification

messages to the surrounding vehicles. (ii) An android application for

fatigue detection is built. The application can be used for the driver to

detect the state of his/her fatigue based on EEG signals, and warn

neighborhood vehicles. (iii) The detection algorithm for driver fatigue

is applied based on fuzzy entropy. The idea of 10-fold cross validation

and support vector machine are used for classified calculation.

Experimental results show that the average accurate rate of detecting

driver fatigue is about 95%, which implying that the algorithm is

validity in detecting state of driver fatigue. 18ANSP-AD-031 CampusSense - A Smart Vehicle Parking Monitoring and

Management System using ANPR Cameras and Android Phones

Vehicle parking monitoring and management has become a big

challenge for educational institutions with increasing enrollments,

high percentage of vehicle ownership and decreasing parking supply

which in result triggering blockage of vehicle, congestion, wastage of

time and money. In university campuses particularly in Kingdom of

Saudi Arabia, vehicle parking monitoring and management problem is

getting worse and more frustrating due to the fact that majority of

students, faculty and staff members own cars and drive through them

to the university campuses. These common problems include finding

out people (evidence) who are responsible for the damages (hitting,

scraping, scratching and dents) to other cars and the blockage of car

due to wrong car parking which takes much time to locate the owner

of the car. Moreover, locating or forgetting their car park location

another difficulty that is often faced by the students, faculty and staff



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members. The existing cameras located at the parking lots are only for

video surveillance and cannot help in such situations as there is a lack

of proper vehicle parking monitoring and management system. To

cope with abovementioned problems and to ensure a better parking

experience by accommodating increasing number of vehicles in a

proper convenient manner, we propose a smart vehicle parking

monitoring and management system called CampusSense. In

CampusSense, Automatic Number Plate Recognition (ANPR)

cameras and android based mobile application is developed to

efficiently monitor, manage and protect the parking facilities at

university campuses. Parking problems around the university campus

faced by the students, faculty and staff members are analyzed by

conducting a survey. 18ANSP-AD-032 RAINA: Reliability and Adaptability in Android for Fog Computing

The ubiquity and universality of smartphones make them ideal fog

devices to bridge edge devices and the cloud. However, to support a

wide range of applications, as well as adhere to the resource constraints

presented, the software stack on smart phones needs to be reliable and

adaptable. We propose RAINA, an architecture to enable reliability

and adaptability in Android. While our work is on Android, our ideas

can easily be adapted to other mobile OSs. This article describes our

software architecture, systems challenges, application challenges, and

methods to address these challenges. We also discuss future work to

allow smartphones to truly be at the center of the fog. 18ANSP-AD-033 Virtual Eye Doc: An Android Based App in Bengali Language for Eye

Health & Vision Examination

Bangladesh is one of the most densely populated countries in the

world, where a significant number of populations are suffering eye

health and visionary problems. Over the last decade, a rapid growth of

ICT uses has shown in Bangladesh. Thus ICT based health services

would be an effective solution for providing the better health services

to the citizens of Bangladesh. In this research work, an android based

mobile application (Virtual Eye Doc) is developed in Bengali language

to support the citizens of Bangladesh for heir eye health monitoring

and vision examination. The paper highlights our experiences of

designing and developing the Virtual Eye Doc application. This paper

also presents a qualitative and quantitative exploration of the



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effectiveness and the perceived benefits of this app, and suggestions

for improvement of this app. 18ANSP-AD-034 RoadSense: Smartphone Application to Estimate Road Conditions

Using Accelerometer and Gyroscope

Monitoring the road condition has acquired a critical significance

during recent years. There are different reasons behind broadening

research on this field: to start with, it will guarantee safety and comfort

to different road users; second, smooth streets will cause less damage

to the car. Our motivation is to create a real-time Android Application

RoadSense that automatically predicts the quality of the road based on

a triaxial accelerometer and a gyroscope, show the road location trace

on a geographic map using GPS, and save all recorded workout

entries. C4.5 Decision tree classifier is applied on training data to

classify road segments and to build our model. Our experimental

results show consistent accuracy of 98.6%. Using this approach, we

expect to visualize a road quality map of a selected region. Hence, we

can provide constructive feedback to drivers and local authorities.

Besides, road manager can benefit from this system to evaluate the

state of their road network and make a checkup on road construction

projects, whether they meet or not the required quality. 18ANSP-AD-035 Android based warning system for the early detection of allergic

reactions

This work proposes an Android-based platform to warn the medical

staff of the onset of allergy reactions during a provocation test in a

hospital. The portable system carries out the analysis of the heart rate

variability for the early detection of allergic reactions in patients

undergoing allergy provocation tests at hospitals. The proposal is

composed of an ECG (electrocardiogram) acquisition system and an

Android device Smartphone, Tablet) that monitors and evaluates the

results in real time, increasing the safety of allergic tests. At present,

food and drug allergic tests are a major problem for patients because

of their long duration and intrusion. However, the authors have

designed an algorithm for detecting allergy reactions that have focused

on reducing the time of the tests and the number of doses. This

algorithm runs on an Android platform, and it is able to provide alarms

for the medical staff if there is an allergy reaction. The proposed

monitoring system is very suitable for the health monitoring during the

provocation tests.



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18ANSP-AD-036 Crowd Gaming: Motivating Learning with Outdoor Activities

This paper presents Crowd Gaming, a platform for creating and

running questionnaire-based games that facilitate learning combined

with outdoor activities. In its current (first) version, each one game

consists of a set of multiple choice type questions. The latter may be

grouped with the content of each one group of questions becoming

accessible by the player in pre-specified time periods, and only when

the latter positions himself within the set range of a pre-specified

geographical location. Game participation assumes the use a mobile

device (smartphone or tablet) incorporating a geographical location

positioning service (GPS). To run the game on the mobile client, the

player uses either a web browser, or the appropriate (Android or iOS)

application. 18ANSP-AD-037 iDispenser - Big Data Enabled Intelligent Dispenser

With healthcare-associated infections (HAIs) in the U.S. accounting

for an estimated 1.7 million infections and99,000 deaths annually,

reducing and preventing these infections is a top goal for healthcare

facilities throughout the country. Not only healthcare facilities, in other

captive environments, for instance, ships and cruises, provide

environment that may increase risk of infection. In this research paper,

we propose an innovative approach to prevent spread of airborne

diseases through the application of Big Data Technologies and IoT

Sensing. Our goal is to cutting down the millions of dollars spent on

infectious diseases, intelligent dispenser promises to keep hospitals

smelling fresh soothing and disinfecting. The paper presents

prototyping solution design as well as its application and certain

experimental results. 18ANSP-AD-038 Fuzzy Mobile Expert System for Academic Advising

Academic advising of students is a task that requires a lot of time,

expertise, and intellectual investments from the academic advisor. In

order to help students to find a suitable decision in short time and

effort, this project implements an Intelligent Algorithm in order to

design an expert application on smart phone. The proposed system was

implemented and tested for validation with real data that collected

from valid students. The experimental results showed that a system

have an average root mean square error of 6.64 % and thus can be used



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successfully to identify the possibility that registering for the course is

a correct decision. 18ANSP-AD-039 On-The-Fly Computing Meets IoT Markets – Towards a Reference

Architecture

The Internet of Things (IoT) connects the things of our everyday life

and supports us in our common activities. Several markets for IoT

services have been created. These markets enable IoT users to search

and compose services in order to support an IoT activity. However,

existing IoT markets like IFTTT (If This Then That) are not convenient

for users with respect to service discovery and composition. The

objective of On-the-fly (OTF) computing is to configure and provide

software markets that fulfill individual users’ wishes by the automatic

on-the-fly composition of single services. The architecture framework

of On-the-fly computing markets helps architects to systematically

develop these systems in different domains. In this paper, we use our

OTF architectural framework to examine the requirements of a

reference architecture for IoT markets. Furthermore, we perform a

comparison between the architecture of IFTTT as an existing IoT

market with this reference architecture. The results show how existing

IoT markets can be improved. In return, the practical knowledge of

IFTTT is taken to the reference architecture. This knowledge helps to

overcome the limitations of today’s IoT markets or creating new

markets in the future. 18ANSP-AD-040 SysPal: System-guided Pattern Locks for Android

To improve the security of user-chosen Android screen lock patterns,

we propose a novel system-guided pattern lock scheme called

“SysPal” that mandates the use of a small number of randomly selected

points while selecting a pattern. Users are given the freedom to use

those mandated points at any position. We conducted a large-scale

online study with 1,717 participants to evaluate the security and

usability of three SysPal policies, varying the number of mandatory

points that must be used (upon selecting a pattern) from one to three.

Our results suggest that the two SysPal policies that mandate the use

of one and two points can help users select significantly more secure

patterns compared to the current Android policy: 22.58% and 23.19%

fewer patterns were cracked. Those two SysPal policies, however, did

not show any statistically significant inferiority in pattern recall

success rate (the percentage of participants who correctly recalled their



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pattern after 24 hours). In our lab study, we asked participants to install

our screen unlock application on their own Android device, and

observed their real-life phone unlock behaviors for a day. Again, our

lab study did not show any statistically significant difference in

memorability for those two SysPal policies compared to the current

Android policy. 18ANSP-AD-041 Who you gonna call? Analyzing Web Requests in Android

Applications

Relying on ubiquitous Internet connectivity, applications on mobile

devices frequently perform web requests during their execution. They

fetch data for users to interact with, invoke remote functionalities, or

send user-generated content or metadata. These requests collectively

reveal common practices of mobile application development, like what

external services are used and how, and they point to possible negative

effects like security and privacy violations, or impacts on battery life.

In this paper, we assess different ways to analyze what web requests

Android applications make. We start by presenting dynamic data

collected from running 20 randomly selected Android applications and

observing their network activity. Next, we present a static analysis

tool, Stringoid, that analyzes string concatenations in Android

applications to estimate constructed URL strings. Using Stringoid, we

extract URLs from 30, 000 Android applications, and compare the

performance with a simpler constant extraction analysis. Finally, we

present a discussion of the advantages and limitations of dynamic and

static analyses when extracting URLs, as we compare the data

extracted by Stringoid from the same 20 applications with the

dynamically collected data. 18ANSP-AD-042 Policy Enforcement with Proactive Libraries

Software libraries implement APIs that deliver reusable

functionalities. To correctly use these functionalities, software

applications must satisfy certain correctness policies, for instance

policies about the order some API methods can be invoked and about

the values that can be used for the parameters. If these policies are

violated, applications may produce misbehaviors and failures at

runtime. Although this problem is general, applications that incorrectly

use API methods are more frequent in certain contexts. For instance,

Android provides a rich and rapidly evolving set of APIs that might be

used incorrectly by app developers who often implement and publish



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faulty apps in the marketplaces. To mitigate this problem, we introduce

the novel notion of proactive library, which augments classic libraries

with the capability of proactively detecting and healing misuses at

runtime. reactive libraries blend libraries with multiple proactive

modules that collect data, check the correctness policies of the

libraries, and heal executions as soon as the violation of a correctness

policy is detected. The proactive modules can be activated or

deactivated at runtime by the users and can be implemented without

requiring any change to the original library and any knowledge about

the applications that may use the library. We evaluated proactive

libraries in the context of the Android ecosystem. Results show that

proactive libraries can automatically overcome several problems

related to bad resource usage at the cost of a small overhead. 18ANSP-AD-043 GuideMe: An Innovative Mobile Application for Guiding Tourists

Many applications have been available in the market for helping

tourists for reaching their destinations efficiently. But these

applications suffer from many shortcomings including limited

functionality, accuracy and features. In order to overcome these

limitations, GuideMe has been developed to provide the tourists with

accurate and reliable information along with the ability to customize

the application to suit their requirements. In addition to providing

general information, GuideMe is capable of segmenting the industry

into multiple groups based on specific requirements. Also, this

application has been developed and hosted on multiple platforms, so

that it can be accessed using any device available with the prospective

client. Evaluation of the application shows that GuideMe provides

more accurate information with better efficiency than the existing

solutions. Index Terms—Tourism, Idea art, Web/Mobile/Telco

Applications, Accurize Data, Android, asp.NET MVC4. 18ANSP-AD-044 A Perceptive Based Application Along With High Frequency

Direction Finder during Quagmire in Our Provincial

We propose a system in which it allows the user to contact or make an

alert during any emergency situation to the appropriate service

provider directly using this application. Though there are several apps

in the market, what makes our application special is that it is

completely in our Tamil language. Here the user has to download and

install the application along with all the required details. In case of any

emergency situation, the user can just make an alert or call the service



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provider directly from the application .while making an alert or call,

the global position using the latitude and longitudinal coordinates of

the user will also be sent to the service provider. All the information

about the service provider will be stored pre-definably in the

application and it can also be updated or new contacts can be added if

required. In this application, we did not use any tool for translating the

text into our Tamil language. We are using the BAMINI as a font and

is imported for the user interface of the application. The main

drawback is to overcome the language complexity. It is in our Tamil

language which makes it to be used even by people who doesn’t know

any foreign language hence making it more user-friendly.



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