Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.

Battery Voltage Discharge Rate Prediction and Video Content Adaptation in Mobile Devices on 3G Access Networks

According to Cisco, mobile multimedia services now account for more than half the total amount of Internet traffic. This trend is burdening mobile devices in terms of power consumption, and as a result, more effort is needed to devise a range of pow- er-saving techniques. While most power-saving techniques are based on sleep scheduling of network interfaces, little has been done to devise multimedia content adaptation techniques. In this paper, we propose a multiple linear regression model that predicts the battery voltage discharge rate for several video send bit rates in a VoIP application. The battery voltage dis- charge rate needs to be accurately estimated in order to esti- mate battery life in critical VoIP contexts, such as emergency communication. In our proposed model, the range of video send bitrates is carefully chosen in order to maintain an acceptable VoIP quality of experience. From extensive profiling, the empir- ical resuhs show that the model effectively saves power and pro- longs real-time VoIP sessions when deployed in power-driven adaptation schemes.

Utility-Improved Key-Value Data Collection with Local Differential Privacy for Mobile Devices

The structure of key-value data is a typical data structure generated by mobile devices.The collection and analysis of the data from mobile devices are critical for service providers to improve service quality.Nevertheless,collecting raw data,which may contain various per⁃sonal information,would lead to serious personal privacy leaks.Local differential privacy(LDP)has been proposed to protect privacy on the device side so that the server cannot obtain the raw data.However,existing mechanisms assume that all keys are equally sensitive,which can⁃not produce high-precision statistical results.A utility-improved data collection framework with LDP for key-value formed mobile data is pro⁃posed to solve this issue.More specifically,we divide the key-value data into sensitive and non-sensitive parts and only provide an LDPequivalent privacy guarantee for sensitive keys and all values.We instantiate our framework by using a utility-improved key value-unary en⁃coding(UKV-UE)mechanism based on unary encoding,with which our framework can work effectively for a large key domain.We then vali⁃date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets.Finally,some pos⁃sible future research directions are envisioned.

The Digital Age of Campus Maps on Mobile Devices

Developing mobile applications have always been a rising topic in the technology world. With the recent development in technology, mobile applications play an important role in various applications throughout the world. Mobile applications are constantly evolving. There are several ongoing research and developments in both industry and academia. In this paper, we present the design and implementation of a mobile application that creates an electronic map or e-map application for the campus of Tuskegee University. The goals for this mobile application are to make the campus map easier and user-friendly for parents, visitors, and students using mobile devices. With this mobile application, the users will be able to search and find campus buildings, as well as give feedback on the application to eliminate the need for paper documentation.

Securing Display Path for Security-Sensitive Applications on Mobile Devices

While smart devices based on ARM processor bring us a lot of convenience,they also become an attractive target of cyber-attacks.The threat is exaggerated as commodity OSes usually have a large code base and suffer from various software vulnerabilities.Nowadays,adversaries prefer to steal sensitive data by leaking the content of display output by a security-sensitive application.A promising solution is to exploit the hardware visualization extensions provided by modern ARM processors to construct a secure display path between the applications and the display device.In this work,we present a scheme named SecDisplay for trusted display service,it protects sensitive data displayed from being stolen or tampered surreptitiously by a compromised OS.The TCB of SecDisplay mainly consists of a tiny hypervisor and a super light-weight rendering painter,and has only^1400 lines of code.We implemented a prototype of SecDisplay and evaluated its performance overhead.The results show that SecDisplay only incurs an average drop of 3.4%.

Enhancement of students' learning with mobile devices: Taking librarian-faculty collaboration in SJTU as an example

Purpose： This paper presents an innovative program of Shanghai Jiao Tong University Library which aims to help engineering students to make use of mobile devices to improve their learning efficiency. Design/methodology/approach： Information literacy training and course learning resources were integrated into students＇ learning process. Surveys on students＇ learning with the touch pads were conducted to help evaluate the program＇s effectiveness. Findings： Our practice showed that collaboration of library staff with faculty members is an effective way to integrate information literacy education and course-specific library resources into students＇ learning with mobile devices, which has greatly improved the efficiency of students＇ learning. Research limitations： First, our literacy training still focused on the use of mobile devices in information access, but not on how to evaluate and manage their information resources with mobile devices. Second, subject librarians need to shift their role from information service providers into information resource instructors while developing the partnership with faculty members and teaching assistants. Practical implications： This paper provides learning efficiency of university students with touch pads conveniently. a new insight into the way of how to enhance such new technical devices as smartphones and Originality/value： Our practice can be used as a valuable guide for libraries that plan to leverage mobile technologies to enhance students＇ learning efficiency.

Investigating approaches to improving rendering performance of 3D city models on mobile devices

Three-dimensional(3D)city models have uses including on-site validation of utility infrastructure,support for augmented reality,personalized tourist information,real estate sales,and 3D pedestrian navigation.Increasingly,such applications are deployed on mobile devices,whose use is becoming more prevalent.Tablet devices are used for more professional use requiring larger screens,mobile phones for more casual users.However,many 3D city models contain hundreds of buildings,which in turn results in performance issues when attempting to visualize such models on these devices.Two issues can be identified as contributory factors-the lower specification of the mobile device itself when compared with desktop machines and the lower bandwidth network between the device and the server(3G mobile or Wi-Fi).Both of these can be addressed by reducing the volume of data in the model.To achieve this,we generalize a 2D data-set(using aggregation and simplification)and then extrude the generalized 2D maps to 3D.This minimizes the number of buildings to be transmitted over the network and processed by the on-board graphics engine.To additionally address the bandwidth issue,we make use of topological data structuring to build and transmit a minimal description for each building.Combining these approaches,we compare the results obtained for generalized and un-generalized data-sets,on a tablet and mobile device.A performance increase of between 7 and 9 times is observed.

Collaborative Interaction for Videos on Mobile Devices Based on Sketch Gestures

With the rapid progress of the network and mobile techniques, mobile devices such as mobile phones and portable devices, have become one of the most important parts in common life. Efficient techniques for watching, navigating and sharing videos on mobile devices collaboratively are appealing in mobile environment. In this paper, we propose a novel approach supporting efficiently collaborative operations on videos with sketch gestures. Furthermore, effective collaborative annotation and navigation operations are given to interact with videos on mobile devices for facilitating users＇ communication based on mobile devices＇ characteristics. Gesture operation and collaborative interaction architecture are given and improved during the interactive process. Finally, a user study is conducted showing that the effectivity and collaborative accessibility of video exploration is improved.

Location Based Services Acceptance and Adoption Evaluation: An Investigation on Mobile Phone Users’ Response to Awareness and Utilization of LBS Technology in Nairobi Kenya

Location Based Navigation System (LBNS) is a specific Location Based Service (LBS) purely for navigational purpose. These systems resolve position of a user by using GNSS/GPS positioning technologies, to which supplementary information on goods and services are tagged. The navigation services have become popular and can be installed on mobile phones to provide route information, location of points of interest and user’s current location. LBS has continued to face challenges which include “communication” process towards user reference. Location Based Service System conveys suitable information through a mobile device for effective decision making and reaction within a given time span. This research was geared at understanding the state of LBS technology acceptance and adoption by users in Nairobi Kenya. To do this a quantitative study was carried out through a questionnaire, to investigate mobile phone users’ response on awareness and use of LBS technology. Testing the growth of this technology in this region compared to predictions in previous studies using Technology Acceptance Model (TAM), it is evident that many users may be aware of GPS functionality in mobile phones but are certainly yet to fully embrace the technology as they rarely use it. This points to some underlying challenges towards this technology within this part of the World, thereby recommending for deliberate monitoring and evaluation of LBS technology for sustenance growth based on user satisfaction and acceptance for improved usability.

A Behavioral Authentication Method for Mobile Based on Browsing Behaviors

The passwords for unlocking the mobile devices are relatively simple,easier to be stolen,which causes serious potential security problems.An important research direction of identity authentication is to establish user behavior models to authenticate users.In this paper,a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is designed.This architecture is suitable for users using the mobile terminal APP in the daily life.The architecture includes data acquisition,data processing,feature extraction,and sub model training.We can use this architecture for continuous authentication when the user uses APP at the mobile terminal.

香车美机 Audi Mobile Device

在东京车展上,奥迪不仅展示了新车型A1,同时还推出了一款配合A1使用的专用导航手机——Audi Mobile Device,该机将在汽车销售时赠送给消费者。这款奥迪自家品牌的手机采用触控操作方式,支持3G,Wi-Fi和GPS,奥迪A1上专门设计了放置手机的插槽.并可通过汽车内置的车载充电器为手机充电。当你忘记带车钥匙时,通过手机可以打开A1的车门.也能用来开启汽车空调。

Security Threat and Data Consumption as Mojor Nuisance of Social Media on Wi-Fi Network

This research is about the nuisances of social media applications on a Wi-Fi network at a university campus in Ghana. The aim was to access the security risk on the network, the speed of the network, and the data consumption of those platforms on the network. Network Mapper (Nmap Zenmap) Graphical User Interface 7.80 application was used to scan the various social media platforms to identify the protocols, ports, services, etc. to enable in accessing the vulnerability of the network. Data consumption of users’ mobile devices was collected and analyzed. Device Accounting (DA) based on the various social media applications was used. The results of the analysis revealed that the network is prone to attacks due to the nature of the protocols, ports, and services on social media applications. The numerous users with average monthly data consumption per user of 4 gigabytes, 300 megabytes on social media alone are a clear indication of high traffic as well as the cost of maintaining the network. A URL filtering of the social media websites was proposed on Rockus Outdoor AP to help curb the nuisance.

Multi-stage Defender-Attacker-Defender Model for Distribution System Resilience Enhancement in Ice Storms with Line Hardening,Mobile Device and Repair Crew Dispatching

This paper proposes a co-optimal strategy using line hardening,mobile devices(mobile ice-melting device,mobile emergency generator,mobile energy storage system),and repair crew dispatching to improve distribution system resilience during ice storms.A multi-stage defender-attacker-defender model is established to take into account interactions and coupling relationships between different measures.In our proposed model,ice storms will attack the distribution and transportation system in a worst-case scenario,affecting system performance from various perspectives.Corresponding to the different operating states in the distribution system affected by ice storms,aiming at minimizing the weighted load shedding value,this paper applies various measures to different stages to improve the response and defense capabilities to ice storms and realize restoration of the distribution system ultimately.The nested column-and-constraint generation algorithm is used to solve the model efficiently.The effectiveness of the proposed model and solution method for enhancing the distribution system resilience is verified on the modified IEEE 33-bus distribution system and modified realworld zone of Caracas 141-bus distribution system.

AN IMPROVED DOS-RESISTANT ID-BASED PASSWORD AUTHENTICATION SCHEME WITHOUT USING SMART CARD

In 2010,Hwang,et al.proposed a 'DoS-resistant ID-based password authentication scheme using smart cards' as an improvement of Kim-Lee-Yoo's 'ID-based password authentication scheme'.In this paper,we cryptanalyze Hwang,et al.'s scheme and point out that the revealed session key could threat the security of the scheme.We demonstrate that extracting information from smart cards is equal to knowing the session key.Thus known session key attacks are also effective under the as-sumption that the adversary could obtain the information stored in the smart cards.We proposed an improved scheme with security analysis to remedy the weaknesses of Hwang,et al.'s scheme.The new scheme does not only keep all the merits of the original,but also provides several additional phases to improve the flexibility.Finally,the improved scheme is more secure,efficient,practical,and convenient,because elliptic curve cryptosystem is introduced,the expensive smart cards and synchronized clock system are replaced by mobile devices and nonces.

Location Based Navigation Service Technology: Development of an Optimization Algorithm for Multivariate Contextual Cartographic Content in Location Based Navigation Service

User response or reaction to navigation applications is influenced by relevance in geographic information, in terms of cartographic context and content delivered within a definite time, providing a direct impact to outcome or consequence based on decision making and hence user reaction. Location Based Navigation Services (LBNS) have continuously advanced in cartographic visualization, making maps interpretation easy and ubiquitous to any user, as compared to pre-historic times when maps were a preserve of a few. Despite rapid growth in LBNS, there exist challenges that may be characterized as technical and non-technical challenges, among them being process of conveying geospatial information to user. LBNS system deliver appropriate information to a user through smartphone (mobile device) for effective decision making and response within a given time span. This research focuses on optimization of cartographic content for contextual information in LBNS to users, based on prevailing circumstances of various components that constitute it. The research looks into Geographic Information Retrieval (GIR), as a technical challenge centered on a non-technical issue of social being of user satisfaction, leading to decision making in LBNS, hence response and outcome. Though advanced technologically, current LBNS on information sourcing depends on user manual web pages navigation and maneuver, this can be painstaking and time consuming that it may cause unnecessary delay in information delivery, resulting to delayed information response time (DIRT). This in turn may lead to unappropriate decision making with erroneous reaction or response being taken, resulting in loss of opportunity, resources, time and even life. Optimization in LBNS is achieved by a mathematical relationship developed between user status, mobile device variables against cartographic content. The relationship is in turn applied in LBNS android application to fulfill optimization solution for user consumption.

Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total consumption.An increase of 3.3%in consumption is predicted from 2024 to 2032.Tomatoes are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose levels.Thus,tomatoes are globally important due to their widespread usage and nutritional value.To face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize them.Diseases are one of the major causes that adversely affect crop yield and degrade the quality of the tomato fruit.This leads to financial losses and affects the livelihood of farmers.Therefore,automatic disease detection at any stage of the tomato plant is a critical issue.Deep learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of parameters.Also,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf region.Moreover,the existing techniques lack in recognizing multiple diseases on the same leaf.To address these concerns,we introduce a customized deep learning-based convolution vision transformer model.Themodel achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 categories.It requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,respectively.Its training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned models.Thus,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato crops.The periodicmonitoring promotes timely action to prevent the spread of diseases and reduce crop loss.

Indoor imaging visible light positioning with sampled sparse light source and mobile device

Indoor visible light positioning becomes attractive due to the increasing demands of location-based services. This Letter proposes an indoor imaging visible light positioning scheme with a sampled sparse light source, image sensor, and gyro. An indoor positioning cellular with a single reference light source and an off-the-shelf mobile device is demonstrated. Experimental results show that the 3-dimensional positioning error is only several centimeters even with a rotated, rolled, and pitched mobile device. The proposed scheme is convenient and cost effective because the transmitter takes advantage of the existing lighting infrastructure and the receiver is a commercial mobile phone without any extra accessories.

supported by the National Natural Science Foundation of China(Nos.62172051,61772085,and 61877005)and Jiangsu Agriculture Science and Technology Innovation Fund(No.CX(18)3054).

Numerous neural network(NN)applications are now being deployed to mobile devices.These applications usually have large amounts of calculation and data while requiring low inference latency,which poses challenges to the computing ability of mobile devices.Moreover,devices’life and performance depend on temperature.Hence,in many scenarios,such as industrial production and automotive systems,where the environmental temperatures are usually high,it is important to control devices’temperatures to maintain steady operations.In this paper,we propose a thermal-aware channel-wise heterogeneous NN inference algorithm.It contains two parts,the thermal-aware dynamic frequency(TADF)algorithm and the heterogeneous-processor single-layer workload distribution(HSWD)algorithm.Depending on a mobile device’s architecture characteristics and environmental temperature,TADF can adjust the appropriate running speed of the central processing unit and graphics processing unit,and then the workload of each layer in the NN model is distributed by HSWD in line with each processor’s running speed and the characteristics of the layers as well as heterogeneous processors.The experimental results,where representative NNs and mobile devices were used,show that the proposed method can considerably improve the speed of the on-device inference by 21%–43%over the traditional inference method.

Fast and accurate visual odometry from a monocular camera

This paper aims at a semi-dense visual odometry system that is accurate,robust,and able to run realtime on mobile devices,such as smartphones,AR glasses and small drones.The key contributions of our system include:1)the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation;2)adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation;and 3)an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization.A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system:LSD-SLAM.For the relocalization task,our system is 2.0X∼4.6X faster than DBoW2 and achieves a similar accuracy.