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.

Quality of Service and Security on Cisco Network Devices, Coupled with the Development of a Mobile Application Prototype Software for Server Room Temperature Monitoring

In an era where digital technology is paramount, higher education institutions like the University of Zambia (UNZA) are employing advanced computer networks to enhance their operational capacity and offer cutting-edge services to their academic fraternity. Spanning across the Great East Road campus, UNZA has established one of the most extensive computer networks in Zambia, serving a burgeoning community of over 20,000 active users through a Metropolitan Area Network (MAN). However, as the digital landscape continues to evolve, it is besieged with burgeoning challenges that threaten the very fabric of network integrity—cyber security threats and the imperatives of maintaining high Quality of Service (QoS). In an effort to mitigate these threats and ensure network efficiency, the development of a mobile application to monitor temperatures in the server room was imperative. According to L. Wei, X. Zeng, and T. Shen, the use of wireless sensory networks to monitor the temperature of train switchgear contact points represents a cost-effective solution. The system is based on wireless communication technology and is detailed in their paper, “A wireless solution for train switchgear contact temperature monitoring and alarming system based on wireless communication technology”, published in the International Journal of Communications, Network and System Sciences, vol. 8, no. 4, pp. 79-87, 2015 [1]. Therefore, in this study, a mobile application technology was explored for monitoring of temperatures in the server room in order to aid Cisco device performance. Additionally, this paper also explores the hardening of Cisco device security and QoS which are the cornerstones of this study.

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.

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.

Phishing Techniques in Mobile Devices

The rapid evolution in mobile devices and communication technology has increased the number of mobile device users dramatically. The mobile device has replaced many other devices and is used to perform many tasks ranging from establishing a phone call to performing critical and sensitive tasks like money payments. Since the mobile device is accompanying a person most of his time, it is highly probably that it includes personal and sensitive data for that person. The increased use of mobile devices in daily life made mobile systems an excellent target for attacks. One of the most important attacks is phishing attack in which an attacker tries to get the credential of the victim and impersonate him. In this paper, analysis of different types of phishing attacks on mobile devices is provided. Mitigation techniques—anti-phishing techniques—are also analyzed. Assessment of each technique and a summary of its advantages and disadvantages is provided. At the end, important steps to guard against phishing attacks are provided. The aim of the work is to put phishing attacks on mobile systems in light, and to make people aware of these attacks and how to avoid them.

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.

Inferring Locations of Mobile Devices from Wi-Fi Data

Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use this information to provide better service and improve user experience. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes. An Android application is able to collect useful Wi-Fi information without registering a location listener with a network-based provider. We passively collected the data of the IDs of Wi-Fi access points and the received signal strengths. We developed and implemented an algorithm to analyse the data;and designed heuristics to infer the location of the device over time—all without ever connecting to the network thus maximally preserving the privacy of the user.

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%.

Study on Architecture-Oriented Information Security Management Model for Using Mobile Devices Control

The popularization of mobile devices has caused considerable impact on the security of the military of the Republic of China.The military barrack-areas have long been faced the control of mobile devices four issues:the lack of accurate use of resources,the lack of protection of the mobile device from the overall point of view,the unclear division of responsibility among specialized agencies,and unclear members’responsibilities for their own duties.This study applies the structure behavior coalescence(SBC)methodology to integrate the organizational structure of the participating management and control units with effective management behaviors in a visualized and useful manner.The units can effectively communicate with each other and solve the four issues faced by the military barrack-areas for the control of mobile devices.This research fulfills improving the lack of control of the military mobile devices by using of management resources effectively and the establishment of mobile devices management with the overall concept,and strengthening the rights and responsibilities and information security awareness,through the logical verification and enterprise interview results.

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.

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.

Case of colonic intussusception secondary to mobile cecum syndrome repaired by laparoscopic cecopexy using a barbed wound suture device

A 27-year-old man with recurrent right lower quadrant pain was admitted to our hospital. Ultrasonography and computed tomography examination of the abdomen revealed a target sign in the ascending colon, which was compatible with the diagnosis of cecal intussusception. The intussusception was spontaneously resolved at that time, but it relapsed 6 mo later. The patient underwent a successful colonoscopic disinvagination; there was no evidence of neoplastic or inflammatory lesions in the colon and terminal ileum. The patient underwent laparoscopic surgery for recurring cecal intussusception. During laparoscopy, we observed an unfixed cecum on the posterior peritoneum(i.e. a mobile cecum). Thus, we performed laparoscopic appendectomy and cecopexy with a lateral peritoneal flap using a barbed wound suture device. The patient's post-operative course was uneventful, and he continued to do well without recurrence at 10 mo after surgery. Laparoscopic cecopexy using a barbed wound suture device is a simple and reliable procedure that can be the treatment of choice for recurrent cecal intussusception associated with a mobile cecum.

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.

Mobile Data Mining-Based Services on the Base of Mobile Device Management (MDM) System

Client software on mobile devices that can cause the remote control perform data mining tasks and show production results is significantly added the value for the nomadic users and organizations that need to perform data analysis stored in the repository, far away from the site, where users work, allowing them to generate knowledge regardless of their physical location. This paper presents new data analysis methods and new ways to detect people work location via mobile computing technology. The growing number of applications, content, and data can be accessed from a wide range of devices. It becomes necessary to introduce a centralized mobile device management. MDM is a KDE software package working with enterprise systems using mobile devices. The paper discussed the design system in detail.

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.

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。

基于Windows Mobile的测量软件的开发

为了开发简单易用的移动测量设备数据采集、处理软件,对目前常用的数据采集、处理软件的优缺点作了分析,并针对存在缺点提出了GeoSolution软件的解决方案。以Windows Mobile 6.0操作系统应用程序的开发环境为平台,搭建GeoSolution软件的开发架构,并对GeoSolution软件的开发过程作了阐明,针对软件开发过程中遇到的技术难题提出了解决方法。