A Literature Review: Potential Effects That Health Apps on Mobile Devices May Have on Patient Privacy and Confidentiality

Purpose: This research aims to evaluate the potential threats to patient privacy and confidentiality posed by mHealth applications on mobile devices. Methodology: A comprehensive literature review was conducted, selecting eighty-eight articles published over the past fifteen years. The study assessed data gathering and storage practices, regulatory adherence, legal structures, consent procedures, user education, and strategies to mitigate risks. Results: The findings reveal significant advancements in technologies designed to safeguard privacy and facilitate the widespread use of mHealth apps. However, persistent ethical issues related to privacy remain largely unchanged despite these technological strides.

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.

Parallelized Jaccard-Based Learning Method and MapReduce Implementation for Mobile Devices Recognition from Massive Network Data

The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this paper,we propose a novel method for mobile device model recognition by using statistical information derived from large amounts of mobile network traffic data.Specifically,we create a Jaccardbased coefficient measure method to identify a proper keyword representing each mobile device model from massive unstructured textual HTTP access logs.To handle the large amount of traffic data generated from large mobile networks,this method is designed as a set of parallel algorithms,and is implemented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features.Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and producer-independency requirements of large mobile network operators.Results show that a 91.5% accuracy rate is achieved for recognising mobile client models from 2 billion records,which is dramatically higher than existing solutions.

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.

Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

Various mobile devices and applications are now used in daily life.These devices require high-speed data processing,low energy consumption,low communication latency,and secure data transmission,especially in 5G and 6G mobile networks.High-security cryptography guarantees that essential data can be transmitted securely;however,it increases energy consumption and reduces data processing speed.Therefore,this study proposes a low-energy data encryption(LEDE)algorithm based on the Advanced Encryption Standard(AES)for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things(IoT)devices.In the proposed LEDE algorithm,the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES.Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption.This reduction in encryption time results in the LEDE algorithm having a 67.4%lower energy consumption and 43.9%shorter encryption time than conventional AES;thus,the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices.

Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

Mobile technology is developing significantly.Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners.Typically,computer vision models focus on image detection and classification issues.MobileNetV2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to users.This leads to increased latency.Processing biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational speed.Hence,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is required.Quantizing pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory requirement.This proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and memory.Our contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable models.The model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class Normal.From the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is compressed.The testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,and 89.76%respectively while MobileNetV2-SVM,InceptionV3,and MobileNetV2 accuracy are observed to be 92.59%,83.38%,and 90.16%,respectively.The proposed novel technique can be used to classify all biometric medical image datasets on mobile devices.

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.

A point-based rendering approach for real-time interaction on mobile devices

Mobile device is an important interactive platform. Due to the limitation of computation, memory, display area and energy, how to realize the efficient and real-time interaction of 3D models based on mobile devices is an important research topic. Considering features of mobile devices, this paper adopts remote rendering mode and point models, and then, proposes a transmission and rendering approach that could interact in real time. First, improved simplification algorithm based on MLS and display resolution of mobile devices is proposed. Then, a hierarchy selection of point models and a QoS transmission control strategy are given based on interest area of operator, interest degree of object in the virtual environment and rendering error. They can save the energy consumption. Finally, the rendering and interaction of point models are completed on mobile devices. The experiments show that our method is efficient.

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.

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.

Investigating the Correlation between Patient Education on Web-Based Portal Functionality and the Reduction in 30-Day Hospital Readmission Rates

Background: The growing use of web-based patient portals offers patients valuable tools for accessing health information, communicating with healthcare providers, and engaging in self-management. However, the influence of educating patients on these portals’ functionality on clinical outcomes, such as all-cause readmission rates, remains underexplored. Objective: This research proposal tested the hypothesis that educating a subset of patients with Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF), on how to effectively access and utilize the functionality of web-based patient portals can reduce all-cause readmission rates. Methods: We performed a prospective, quasi-experimental study at Bon Secours St. Mary’s Hospital in Richmond, Virginia, USA;dividing participants into an intervention group, receiving education about accessing and navigating “My Chart”, the Bon Secours Web based portal, and a control group, receiving standard care. We then compared 30-day readmission rates, patient engagement, and self-management behaviors between the groups. Data was analyzed using statistical tests to assess the intervention’s impact. Results: We projected that educated patients will exhibit lower readmission rates, improved engagement, and better self-management. The results of the study showed that there was a significant decrease in 30-day readmissions in the intervention group in comparison with the control group (22.7% and 40.9%, respectively). This reduction of 18. 2% of readmissions evaluated here for a trial of meaningful clinical effect is statistically insignificant (p = 0. 184). The practical significance of the intervention is considered small-to-moderate (Cramer V = 0. 20) suggesting that the observed difference has a potential clinical importance even though the difference was not statistically significant. Conclusion: These results imply that the proposed educational intervention might have a positive impact on readmissions;nonetheless, the patient’s characteristics that make him or her capable of readmission cannot be changed and are assessed by the RoR (Risk of Readmission) score. The potential impact of the intervention may be offset, in part, by these baseline risk factors. The study’s power may be limited by sample size, potentially affecting the detection of significant differences. Future studies with larger, multi-center samples and longer follow-up periods are recommended to confirm these findings.

The Adaptation of Mobile Learning System Based on Business Rules

In the mobile learning system,it is important to adapt to mobile devices.Most of mobile learning systems are not quickly suitable for mobile devices.In order to provide adaptive mobile services,the approach for adaptation is proposed in this paper.Firstly,context of mobile devices and its influence on mobile learning system are analized and business rules based on these analysis are presented.Then,using the approach,the mobile learning system is constructed.The example implies this approach can adapt the mobile service to the mobile devices flexibly.

The Underlying Motivations for Mobile Device Use by Seniors

To date, there has been limited research carried out to better understand seniors＇ needs and purchase motivations related to mobile devices. To that end, this research enabled an exploratory assessment of the intrinsic and extrinsic needs/motives to consider in future research and development of ubiquitous mobile devices and related applications, specifically for seniors. The 65＋ population is expected to double by 2025 （WHO, 2013） from 390 million to 800 million. The results demonstrate specific needs/motives which should be considered during the development of new mobile attributes and apps for this segment. For both attributes of devices themselves and the applications found on them, three tiers of priority for development were determined.

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。