Marketing a Mobile Application in a Highly Competitive Global Marketplace

The purpose of this research was to study the marketing of mobile applications. The main objective was to find out what are the most efficient ways of marketing to increase the sales for a mobile application within a highly competitive marketplace. The marketplaces, app stores, are studied from the perspective of size, ease of entry, competition and customers and their purchasing process. The study also includes research on what are some of the main marketing methods used in mobile app marketing in general. The study consists of two parts, theoretical and empirical research. Theoretical research was done by studying past scientific research on the chosen subjects. As the subject is very new, the research was also extended to other publications from the field of mobile technology. The empirical part was done through interviews and empirical experiments with a case-company, which were used to answer the main objective of this study. These experiments showed that the chosen methods of mobile app marketing, app store optimization, localization and selected social media marketing activities, created the most sales when used together. Positive results were seen also when the activities were conducted by themselves, but together they were able to push the case company to their all time best results. However the key to succeeding and hitting high positions in the app store rankings would most likely require creating a solid marketing strategy, trying out other marketing activities alongside the ones used here, without forgetting to stay on top of mobile technology trends.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

The secret life of software applications

One of the most undervalued problems by smartphone users is the security of data on their mobile devices. Today smartphones and tablets are used to send messages and photos and especially to stay connected with social networks, forums and other platforms. These devices contain a lot of private information like passwords, phone numbers, private photos, emails, etc. and an attacker may choose to steal or destroy this information. The main topic of this thesis is the security of the applications present on the most popular stores (App Store for iOS and Play Store for Android) and of their mechanisms for the management of security. The analysis is focused on how the architecture of the two systems protects users from threats and highlights the real presence of malware and spyware in their respective application stores. The work described in subsequent chapters explains the study of the behavior of 50 Android applications and 50 iOS applications performed using network analysis software. Furthermore, this thesis presents some statistics about malware and spyware present on the respective stores and the permissions they require. At the end the reader will be able to understand how to recognize malicious applications and which of the two systems is more suitable for him. This is how this thesis is structured. The first chapter introduces the security mechanisms of the Android and iOS platform architectures and the security mechanisms of their respective application stores. The Second chapter explains the work done, what, why and how we have chosen the tools needed to complete our analysis. The third chapter discusses about the execution of tests, the protocol followed and the approach to assess the “level of danger” of each application that has been checked. The fourth chapter explains the results of the tests and introduces some statistics on the presence of malicious applications on Play Store and App Store. The fifth chapter is devoted to the study of the users, what they think about and how they might avoid malicious applications. The sixth chapter seeks to establish, following our methodology, what application store is safer. In the end, the seventh chapter concludes the thesis.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.

Emerging perspectives on the design, use, and evaluation of mobile and handheld devices

Human-computer interaction is a growing field of study in which researchers and professionals aim to understand and evaluate the impact of new technologies on human behavior. With the integration of smart phones, tablets, and other portable devices into everyday life, there is a greater need to understand the influence of such technology on the human experience. Emerging Perspectives on the Design, Use, and Evaluation of Mobile and Handheld Devices is an authoritative reference source consisting of the latest scholarly research and theories from international experts and professionals on the topic of human-computer interaction with mobile devices. Featuring a comprehensive collection of chapters on critical topics in this dynamic field, this publication is an essential reference source for researchers, educators, students, and practitioners interested in the use of mobile and handheld devices and their impact on individuals and society as a whole. This publication features timely, research-based chapters pertaining to topics in the design and evaluation of smart devices including, but not limited to, app stores, category-based interfaces, gamified mobility applications, mobile interaction, mobile learning, pervasive multimodal applications, smartphone interaction, and social media use.

Prototype and proof of concept for a display system in retail stores

Selling devices on retail stores comes with the big challenge of grabbing the customer’s attention. Nowadays people have a lot of offers at their disposal and new marketing techniques must emerge to differentiate the products. When it comes to smartphones and tablets, those devices can make the difference by themselves, if we use their computing power and capabilities to create something unique and interactive. With that in mind, three prototypes were developed during an internship: a face recognition based Customer Detection, a face tracking solution with an Avatar and interactive cross-app Guides. All three revealed to have potential to be differentiating solutions in a retail store, not only raising the chance of a customer taking notice of the device but also of interacting with them to learn more about their features. The results were meant to be only proof of concepts and therefore were not tested in the real world.

Serum Ferritin Level Remains a Reliable Marker of Bone Marrow Iron Stores Evaluated by Histomorphometry in Hemodialysis Patients

Background and objectives: As well as being a marker of body iron stores, serum ferritin (sFerritin) has also been shown to be a marker of inflammation in hemodialysis (HD) patients. The aim of this study was to analyze whether sFerritin is a reliable marker of the iron stores present in bone marrow of HD patients. Design: Histomorphometric analysis of stored transiliac bone biopsies was used to assess iron stores by determining the number of iron-stained cells per square millimeter of bone marrow. Results: In 96 patients, the laboratory parameters were hemoglobin = 11.3 +/- 1.6 g/dl, hematocrit = 34.3 +/- 5%, sFerritin 609 +/- 305 ng/ml, transferrin saturation = 32.7 +/- 22.5%, and C-reactive protein (CRP) = 0.9 +/- 1.4 mg/dl. sFerritin correlated significantly with CRP, bone marrow iron, and time on HD treatment W = 0.006, 0.001, and 0.048, respectively). The independent determinants of sFerritin were CRP (beta-coef = 0.26; 95% CI = 24.6 to 132.3) and bone marrow iron (beta-coef = 0.32; 95% CI = 0.54 to 2.09). Bone marrow iron was higher in patients with sFerritin >500 ng/ml than in those with sFerritin :5500 ng/ml. In the group of patients with sFerritin :5500 ng/ml, the independent determinant of sFerritin was bone marrow iron (beta-coef = 0.48, 95% CI = 0.48 to 1.78), but in the group of patients with sFerritin >500 ng/ml, no independent determinant of sFerritin was found. Conclusions: sFerritin adequately reflects iron stores in bone marrow of HD patients.

Impact of allogeneic 2-RBC apheresis on iron stores of Brazilian blood donors

One limiting factor for automated two-red blood cells collections (2-RBC) is its potential iron depletion. We analyzed hematological parameters and iron balance before, two and four months after 2-RBC of 96 non-supplemented male donors. Four months after 2-RBC, ferritin level was significantly lower (P < 0.01) than baseline levels and the number of donors who presented ferritin <30 ng/ml increased from 18 to 47. We concluded that four months was not sufficient for iron recuperation in the population studied. In an attempt to avoid iron depletion after 2-RBC, we recommend augmentation in the interval between blood donations and pre-donation ferritin measurement. (C) 2009 Published by Elsevier Ltd.

Effects of alcohol consumption on indices of iron stores and of iron stores on alcohol intake markers

Background: Alcohol increases body iron stores. Alcohol and iron may increase oxidative stress and the risk of alcohol-related liver disease. The relationship between low or safe levels of alcohol use and indices of body iron stores, and the factors that affect the alcohol-iron relationship, have not been fully characterized. Other aspects of the biological response to alcohol use have been reported to depend on iron status. Methods: We have measured serum iron, transferrin, and ferritin as indices of iron stores in 3375 adult twin subjects recruited through the Australian Twin Registry. Information on alcohol use and dependence and smoking was obtained from questionnaires and interviews. Results: Serum iron and ferritin increased progressively across classes of alcohol intake. The effects of beer consumption were greater than those of wine or spirits. Ferritin concentration was significantly higher in subjects who had ever been alcohol dependent. There was no evidence of interactions between HFE genotype or body mass index and alcohol. Alcohol intake-adjusted carbohydrate-deficient transferrin was increased in women in the lowest quartile of ferritin results, whereas adjusted gamma -glutamyltransferase, aspartate aminotransferase, and alanine aminotransferase values were increased in subjects with high ferritin. Conclusions: Alcohol intake at low level increases ferritin and, by inference, body iron stores. This may be either beneficial or harmful, depending on circumstances. The response of biological markers of alcohol intake can be affected by body iron stores; this has implications for test sensitivity and specificity and for variation in biological responses to alcohol use.

Variation in coumarin 7-hydroxylase activity associated with genetic polymorphism of cytochrome P450 2A6 and the body status of iron stores in adult Thai males and females

The relationships between catalytic activity of cytochrome P450 2A6 (CYP2A6), polymorphism of CYP2A6 gene, gender and levels of body iron stores were analysed in a sample group of 202 apparently healthy Thais, aged 1947 years. Eleven individuals were found to have high activity of CYP2A6, judged by the relatively large amounts (11.2-14.6 mg) of 7-hydroyxcoumarin (7-OHC) excreted 3 h following administration of 15 mg of coumarin. Ten individuals, however, did not excrete any 7-OHC. Of these 10, four were found to have no CYP2A6 gene (whole gene deletion; CYP2A6*4 allele). The frequency of the CYP2A6 alleles; *1A, *1B and *4 in the whole sample group was 52, 40 and 8% while the frequency of the CYP2A6 gene types; *1A/* 1A, *1A/* 1B, *1B/* 1B, *1A/* 4, *1BI* 4, *4/* 4 was 29, 41, 16, 7, 5 and 2%. Subjects having CYP2A6* 1A/* 1B gene-type group were found to have higher rates of coumarin 7-hydroxylation compared with those of the CYP2A6* 1B/* 1B and CYP2A6* 1A/* 4 gene types. The inter-individual variability in CYP2A6 catalytic activity was therefore attributed in part to the CYP2A6 genetic polymorphism. Variation in CYP2A6 activity in this sample group was not associated with gender but, interestingly, it did show an inverse association with plasma ferritin; an indicator of body iron stores. Higher rates of coumarin 7-hydroxylation were found in individuals with low body iron stores (plasma ferritin < 20 μg/l) compared with subjects having normal body iron store status. Subjects (n = 16) with iron overload (plasma ferritin > 300 mug/l) also tended to have elevated rates of coumarin 7-hydroxylation. These results suggest an increased CYP2A6 expression in subjects who have excessive body iron stores. Further investigations into the underlying factors that may lead to increased expression of CYP2A6 in association with abnormal body iron stores are currently in progress in our laboratory. Pharmacogenetics 12:241-249 (C) 2002 Lippincott Williams Wilkins.