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Journal articles on the topic 'Mobile User profiles'
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PANAGIOTAKIS, SPYROS, MARIA KOUTSOPOULOU, and ATHANASSIA ALONISTIOTI. "CONTEXT-AWARENESS AND USER PROFILING IN MOBILE ENVIRONMENTS." International Journal of Semantic Computing 03, no. 03 (September 2009): 331–63. http://dx.doi.org/10.1142/s1793351x09000811.
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The evolution of mobile communication systems to 3G and beyond introduces requirements for flexible, customized, and ubiquitous multimedia service provision to mobile users. One must be able to know at any given time the network status, the user location, the profiles of the various entities (users, terminals, network equipment, services) involved and the policies that are employed within the system. Namely, the system must be able to cope with a large amount of context information. The present paper focuses on location and context awareness in service provisioning and proposes a flexible and innovative model for user profiling. The innovation is based on the enrichment of common user profiling architectures to include location and other contextual attributes, so that enhanced adaptability and personalization can be achieved. For each location and context instance an associated User Profile instance is created and hence, service provisioning is adapted to the User Profile instance that better apply to the current context. The generic model, the structure and the content of this location- and context-sensitive User Profile, along with some related implementation issues, are discussed.
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Wang, Dongjie, Pengyang Wang, Kunpeng Liu, Yuanchun Zhou, Charles E. Hughes, and Yanjie Fu. "Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4410–17. http://dx.doi.org/10.1609/aaai.v35i5.16567.
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In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model can perfectly reproduce users' mobility trajectories. In the reverse direction, once the predictive model can imitate users' mobility patterns, the learned user profiles are also optimal. Such intuition motivates us to propose an imitation-based mobile user profiling framework by exploiting reinforcement learning, in which the agent is trained to precisely imitate users' mobility patterns for optimal user profiles. Specifically, the proposed framework includes two modules: (1) representation module, that produces state combining user profiles and spatio-temporal context in real-time; (2) imitation module, where Deep Q-network (DQN) imitates the user behavior (action) based on the state that is produced by the representation module. However, there are two challenges in running the framework effectively. First, epsilon-greedy strategy in DQN makes use of the exploration-exploitation trade-off by randomly pick actions with the epsilon probability. Such randomness feeds back to the representation module, causing the learned user profiles unstable. To solve the problem, we propose an adversarial training strategy to guarantee the robustness of the representation module. Second, the representation module updates users' profiles in an incremental manner, requiring integrating the temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM), we introduce a gated mechanism to incorporate new and old user characteristics into the user profile. In the experiment, we evaluate our proposed framework on real-world datasets. The extensive experimental results validate the superiority of our method comparing to baseline algorithms.
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Patrikakis, Ch Z., I. G. Nikolakopoulos, and A. S. Voulodimos. "Mobile user profiles for Personal Networks: The MAGNET Beyond case." International Journal of Communication Systems 23, no. 9-10 (April 6, 2010): 1289–309. http://dx.doi.org/10.1002/dac.1130.
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Ukrit, M. Ferni, B. Venkatesh, and Swetabh Suman. "Location Based Services with Location Centric Profiles." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 3001. http://dx.doi.org/10.11591/ijece.v6i6.11111.
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<p>In the advancing world of technology, Mobile applications are a rapidly growing segment of the global mobile market. Mobile applications are evolving at a meteor pace to give users a rich and fast user experience. Android operating system supports most applications in today’s technical world. It is an open source operating system which highly satisfies the user’s needs. In this paper, the applications that are location based i.e. the applications that make use of the Global Positioning System (GPS) is discussed. It is a space based satellite navigation system which provides details of time and location in all weather conditions anywhere on or near earth. We introduce PROFILER, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of collocated users. In our System we find the corresponding location using Global Positioning and Service Provider also. This is to change the profile mode automatically and alarm notification enable automatically when you are going to reach corresponding location or Regular Location.</p>
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Ukrit, M. Ferni, B. Venkatesh, and Swetabh Suman. "Location Based Services with Location Centric Profiles." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 3001. http://dx.doi.org/10.11591/ijece.v6i6.pp3001-3005.
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<p>In the advancing world of technology, Mobile applications are a rapidly growing segment of the global mobile market. Mobile applications are evolving at a meteor pace to give users a rich and fast user experience. Android operating system supports most applications in today’s technical world. It is an open source operating system which highly satisfies the user’s needs. In this paper, the applications that are location based i.e. the applications that make use of the Global Positioning System (GPS) is discussed. It is a space based satellite navigation system which provides details of time and location in all weather conditions anywhere on or near earth. We introduce PROFILER, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of collocated users. In our System we find the corresponding location using Global Positioning and Service Provider also. This is to change the profile mode automatically and alarm notification enable automatically when you are going to reach corresponding location or Regular Location.</p>
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Felice, Magdalena. "USER PROFILES: Uses and appropriations of mobile phones by the youth in the city of Buenos Aires." Luciérnaga-Comunicación 5, no. 9 (June 2013): 29–38. http://dx.doi.org/10.33571/revistaluciernaga.v5n9a3.
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The article presents some results of the research about the uses and appropriations of mobile phones by young people between 20 and 29 years old belonging to middle-high sectors in the city of Buenos Aires. This research was carried out between August 2011 and September 2012. In this paper we propose to acknowledge the codes of significance that actors attach to this device and we make a description of the communication field using mobile phones as topography. This way we try to build classifications and categories that will allow us to synthesize and organize information gathered throughout the investigation. Thus, we have defined four user profiles: the resistant, the pragmatists, the enthusiasts and the heavy users; the first two make up the group of the “unattached” and the last two, the group of the “fans”.
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Chen, Junpu, and Hong Xie. "An Online Learning Approach to Sequential User-Centric Selection Problems." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6231–38. http://dx.doi.org/10.1609/aaai.v36i6.20572.
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This paper proposes a new variant of multi-play MAB model, to capture important factors of the sequential user-centric selection problem arising from mobile edge computing, ridesharing applications, etc. In the proposed model, each arm is associated with discrete units of resources, each play is associate with movement costs and multiple plays can pull the same arm simultaneously. To learn the optimal action profile (an action profile prescribes the arm that each play pulls), there are two challenges: (1) the number of action profiles is large, i.e., M^K, where K and M denote the number of plays and arms respectively; (2) feedbacks on action profiles are not available, but instead feedbacks on some model parameters can be observed. To address the first challenge, we formulate a completed weighted bipartite graph to capture key factors of the offline decision problem with given model parameters. We identify the correspondence between action profiles and a special class of matchings of the graph. We also identify a dominance structure of this class of matchings. This correspondence and dominance structure enable us to design an algorithm named OffOptActPrf to locate the optimal action efficiently. To address the second challenge, we design an OnLinActPrf algorithm. We design estimators for model parameters and use these estimators to design a Quasi-UCB index for each action profile. The OnLinActPrf uses OffOptActPrf as a subroutine to select the action profile with the largest Quasi-UCB index. We conduct extensive experiments to validate the efficiency of OnLinActPrf.
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SONG, WEI, DIAN TJONDRONEGORO, and MICHAEL DOCHERTY. "EXPLORATION AND OPTIMIZATION OF USER EXPERIENCE IN VIEWING VIDEOS ON A MOBILE PHONE." International Journal of Software Engineering and Knowledge Engineering 20, no. 08 (December 2010): 1045–75. http://dx.doi.org/10.1142/s0218194010005067.
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Compared with viewing videos on PCs or TVs, mobile users have different experiences in viewing videos on a mobile phone due to different device features such as screen size and distinct usage contexts. To understand how mobile user's viewing experience is impacted, we conducted a field user study with 42 participants in two typical usage contexts using a custom-designed iPhone application. With user's acceptance of mobile video quality as the index, the study addresses four influence aspects of user experiences, including context, content type, encoding parameters and user profiles. Accompanying the quantitative method (acceptance assessment), we used a qualitative interview method to obtain a deeper understanding of a user's assessment criteria and to support the quantitative results from a user's perspective. Based on the results from data analysis, we advocate two user-driven strategies to adaptively provide an acceptable quality and to predict a good user experience, respectively. There are two main contributions from this paper. Firstly, the field user study allows a consideration of more influencing factors into the research on user experience of mobile video. And these influences are further demonstrated by user's opinions. Secondly, the proposed strategies — user-driven acceptance threshold adaptation and user experience prediction — will be valuable in mobile video delivery for optimizing user experience.
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Xu, Jia, Jin Xin Xiang, Xiang Chen, Fang Bin Liu, and Jing Jie Yu. "ODMBP: Behavior Forwarding for Multiple Property Destinations in Mobile Social Networks." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7908328.
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The smartphones are widely available in recent years. Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. The behavior based forwarding has become a new transmission paradigm for supporting many novel applications. However, the commodities, services, and individuals usually have multiple properties of their interests and behaviors. In this paper, we profile these multiple properties and propose an Opportunistic Dissemination Protocol based on Multiple Behavior Profile, ODMBP, in mobile social networks. We first map the interest space to the behavior space and extract the multiple behavior profiles from the behavior space. Then, we propose the correlation computing model based on the principle of BM25 to calculate the correlation metric of multiple behavior profiles. The correlation metric is used to forward the message to the users who are more similar to the target in our protocol. ODMBP consists of three stages: user initialization, gradient ascent, and group spread. Through extensive simulations, we demonstrate that the proposed multiple behavior profile and correlation computing model are correct and efficient. Compared to other classical routing protocols, ODMBP can significantly improve the performance in the aspect of delivery ratio, delay, and overhead ratio.
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Dee, Timothy, Ian Richardson, and Akhilesh Tyagi. "Continuous Nonintrusive Mobile Device Soft Keyboard Biometric Authentication." Cryptography 6, no. 2 (March 23, 2022): 14. http://dx.doi.org/10.3390/cryptography6020014.
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Mobile banking, shopping, and in-app purchases utilize persistent authentication states for access to sensitive data. One-shot authentication permits access for a fixed time period. For instance, a username/password-based authentication allows a user access to all the shopping and payments data in the Amazon shopping app. Traditional user passwords and lock screens are easily compromised. Snooping attacks—observing an unsuspecting user entering passwords—and smudge attacks—examining touchscreen finger oil residue—enable compromised user authentication. Mobile device interactions provide robust human and device identity data. Such biometrics enhance authentication. In this paper, behavioral attributes during user input constitute the password. Adversary password reproduction difficulty increases since pure observation is insufficient. Current mobile continuous authentication schemes use, among others, touchscreen–swipe interactions or keyboard input timing. Many of these methods require cumbersome training or intrusive authentication. Software keyboard interactions provide a consistent biometric data stream. We develop biometric profiles using touch pressure, location, and timing. New interactions authenticate against a profile using a divergence measure. In our limited user–device data sets, the classification achieves virtually perfect accuracy.
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Morais, Leonardo P. de, Roger Immich, Nádia Félix Silva, Thierson Couto Rosa, and Vinicius da Cunha Martins Borges. "Characterization of the Mobile User Profile Based on Sentiments and Network Usage Attributes." Journal of Internet Services and Applications 13, no. 1 (December 27, 2022): 82–97. http://dx.doi.org/10.5753/jisa.2022.2520.
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Providing resources to meet user needs in futuristic mobile networks is still challenging since the network resources like spectrum and base stations do not increase in the same proportion as the accelerated growth of network traffic. Because of this, human/user behavior attributes can assist resource management in dealing with these challenges, which pick up aspects of how the user impacts the usage of mobile networks, such as network usage, the content of interest, urban mobility routines, social networks, and sentiment. A user profile is a combination of user/human behavior attributes. Such profiles are expected to be a knowledge for softwarization enablers to improve the management of future wireless networks fully. Nevertheless, the correlation between human sentiment and wireless and mobile network usage has not been deeply investigated in the literature about the mobile user profile. This work aims to define the user profile using a transfer learning approach for the sentiment classification of WhatsApp messages. A real-life experiment was conducted to collect users' attributes, namely the WhatsApp messages and network usage. A new data analysis methodology is proposed that consists of a frequent item-set pattern mining (FP-Growth) based on Association Rules, the Chi-squared statistical test, and descriptive statistics. This methodology assesses the correlation between sentiment and network usage in a profound way. Results show that the users participating in the experiment form three groups. The first group, with 55.6% of the users, contains users who present a strong relation between negative sentiment and low network usage and also a strong relation between positive sentiment and high network usage. The second group contains 25.9% of the users and is composed of userswho present a strong relation between positive sentiment and high network usage. The third group contains 18.5% of the users for whom the correlation between sentiment and network usage is still statistical significant, but the strength of this relation is much more weak then in the other two groups. Thus, 81.5% of the users (the first two groups) present a strong relation between user sentiment captured from WhatsApp messages and the network traffic generated by them.
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Lakshmanarao, A., and M. Shashi. "Android Malware Detection with Deep Learning using RNN from Opcode Sequences." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 01 (January 18, 2022): 145–57. http://dx.doi.org/10.3991/ijim.v16i01.26433.
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Android is the most widely used operating system in smartphones. Mobile users can download and access apps easily from the play store. Due to lack of security awareness and risk associated with mobile apps, malware apps would be downloaded by normal users in general. The consequences after installing a malware app are unpredictable. Malware apps can gather user personal data, browsing history, user profiles, user sensitive data like passwords. Hence, android malware detection is essential for providing security to mobile users. Android malware detection using machine learning is done either by extracting static features (opcodes, permissions, intents, system commands) or by extracting dynamic features (log behavior, system calls, dataflow). In this paper, opcode sequences are extracted from malware and benign apps, and Recurrent Neural Networks are proposed on extracted sequences. Benign apps are collected from the play store, apkpure.com and malware apps are collected from the virus share website. The proposed Recurrent Neural Network model could achieve 96% accuracy for android malware detection.
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Abdullah, Nor Aniza, Rasheed Abubakar Rasheed, Mohd Hairul Nizam Md Nasir, and Md Mujibur Rahman. "Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey." Applied Sciences 11, no. 20 (October 15, 2021): 9608. http://dx.doi.org/10.3390/app11209608.
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Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.
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Fitzpatrick, Colin, and Jeremy Birnholtz. "“I Shut the Door”: Interactions, tensions, and negotiations from a location-based social app." New Media & Society 20, no. 7 (August 15, 2017): 2469–88. http://dx.doi.org/10.1177/1461444817725064.
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Location-based social apps leverage mobile phones to provide face-to-face (FtF) social opportunities for physically proximate individuals, such as finding nearby people to socialize, date, or hook up. Prior work on dating and hookup apps has focused mostly on profiles and user goals, but this leaves open important questions of how, after constructing a profile, people use these apps to connect and realize their goals, and what these experiences are like. We report on 22 interviews with users of Grindr, a location-based social app for men who have sex with men. We examine interaction processes from viewing profiles to meeting up. Using the perspective of relational dialectics, we explore tensions around connecting with others, sharing information, and being predictable or novel. We find that profile presentations are flexible and subject to change, disinhibition challenges interaction and revealing goals, and social consequences increase through moving from profile browsing to meeting FtF.
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Li, Fudong, Nathan Clarke, Maria Papadaki, and Paul Dowland. "Misuse Detection for Mobile Devices Using Behaviour Profiling." International Journal of Cyber Warfare and Terrorism 1, no. 1 (January 2011): 41–53. http://dx.doi.org/10.4018/ijcwt.2011010105.
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Mobile devices have become essential to modern society; however, as their popularity has grown, so has the requirement to ensure devices remain secure. This paper proposes a behaviour-based profiling technique using a mobile user’s application usage to detect abnormal activities. Through operating transparently to the user, the approach offers significant advantages over traditional point-of-entry authentication and can provide continuous protection. The experiment employed the MIT Reality dataset and a total of 45,529 log entries. Four experiments were devised based on an application-level dataset containing the general application; two application-specific datasets combined with telephony and text message data; and a combined dataset that included both application-level and application-specific. Based on the experiments, a user’s profile was built using either static or dynamic profiles and the best experimental results for the application-level applications, telephone, text message, and multi-instance applications were an EER (Equal Error Rate) of 13.5%, 5.4%, 2.2%, and 10%, respectively.
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Saighi, Asma, Zakaria Laboudi, Philippe Roose, Sébastien Laborie, and Nassira Ghoualmi-Zine. "On Using Multiple Disabilities Profiles to Adapt Multimedia Documents." International Journal of Information Technology and Web Engineering 15, no. 3 (July 2020): 34–60. http://dx.doi.org/10.4018/ijitwe.2020070103.
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Currently, advanced technological hardware can offer mobile devices which fits in the hand with a capacity to consult documents at anytime and anywhere. Multiple user context constraints as well as mobile device capabilities may involve the adaptation of multimedia content. In this article, the authors propose a new graph-based method for adapting multimedia documents in complex situations. Each contextual situation could correspond to a physical handicap and therefore triggers an adaptation action using ontological reasoning. Consequently, when several contextual situations are identified, this leads to multiple disabilities and may give rise to inconsistency between triggered actions. Their method allows modeling relations between adaptation-actions to select the compatible triggerable ones. In order to evaluate the feasibility and the performance of their proposal, an experimental study has been made on some real scenarios. When tested and compared with some existing approaches, their proposal showed improvements according to various criteria.
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P George, Jossy, and Vinay M. "Mobile in learning: Enhancement of information and communication technologies." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 98. http://dx.doi.org/10.14419/ijet.v7i2.6.10075.
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The technological advancement in the world has changed the people’s life. The people view point towards the usage of technologies in different fields like business, tourism, communication, education etc. has changed. Mobile learning can give flexible learning environment for the user. It can also increase the participant number in the online teaching learning process. This paper discusses about the effectiveness of the current technologies used in higher education system. It profiles the advantages of using mobile in accessing the university central system for teaching and learning. It also discusses about mobile digital book with augmentation, which can be used to improve the teaching and learning process of the different departments in the university
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Wagata, Kensuke, and Andrew Beng Jin Teoh. "Few-Shot Continuous Authentication for Mobile-Based Biometrics." Applied Sciences 12, no. 20 (October 14, 2022): 10365. http://dx.doi.org/10.3390/app122010365.
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The rapid growth of smartphone financial services raises the need for secure mobile authentication. Continuous authentication is a user-friendly way to strengthen the security of smartphones by implicitly monitoring a user’s identity through sessions. Mobile continuous authentication can be viewed as an anomaly detection problem in which models discriminate between one genuine user and the rest of the impostors (anomalies). In practice, complete impostor profiles are hardly available due to the time and monetary cost, while leveraging genuine data alone yields poorly generalized models due to the lack of knowledge about impostors. To address this challenge, we recast continuous mobile authentication as a few-shot anomaly detection problem, aiming to enhance the inference robustness of unseen impostors by using partial knowledge of available impostor profiles. Specifically, we propose a novel deep learning-based model, namely a local feature pooling-based temporal convolution network (LFP-TCN), which directly models raw sequential mobile data, aggregating global and local feature information. In addition, we introduce a random pattern mixing augmentation to generate class-unconstrained impostor data for the training. The augmented pool enables characterizing various impostor patterns from limited impostor data. Finally, we demonstrate practical continuous authentication using score-level fusion, which prevents long-term dependency or increased model complexity due to extended re-authentication time. Experiments on two public benchmark datasets show the effectiveness of our method and its state-of-the-art performance.
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Gatziolis, Kleanthis G., Nikolaos D. Tselikas, and Ioannis D. Moscholios. "Adaptive User Profiling in E-Commerce and Administration of Public Services." Future Internet 14, no. 5 (May 9, 2022): 144. http://dx.doi.org/10.3390/fi14050144.
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The World Wide Web is evolving rapidly, and the Internet is now accessible to millions of users, providing them with the means to access a wealth of information, entertainment and e-commerce opportunities. Web browsing is largely impersonal and anonymous, and because of the large population that uses it, it is difficult to separate and categorize users according to their preferences. One solution to this problem is to create a web-platform that acts as a middleware between end users and the web, in order to analyze the data that is available to them. The method by which user information is collected and sorted according to preference is called ‘user profiling‘. These profiles could be enriched using neural networks. In this article, we present our implementation of an online profiling mechanism in a virtual e-shop and how neural networks could be used to predict the characteristics of new users. The major contribution of this article is to outline the way our online profiles could be beneficial both to customers and stores. When shopping at a traditional physical store, real time targeted “personalized” advertisements can be delivered directly to the mobile devices of consumers while they are walking around the stores next to specific products, which match their buying habits.
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Perreault, Mathieu, and Derek Ruths. "The Effect of Mobile Platforms on Twitter Content Generation." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 289–96. http://dx.doi.org/10.1609/icwsm.v5i1.14112.
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The increased popularity of feature-rich mobile devices in recent years has enabled widespread consumption and production of social media content via mobile devices. Because mobile devices and mobile applications change context within which an individual generates and consumes microblog content, we might expect microblogging behavior to differ depending on whether the user is using a mobile device. To our knowledge, little has been established about what, if any, effects such mobile interfaces have on microblogging. In this paper, we investigate this question within the context of Twitter, among the most popular microblogging platforms. This work makes three specific contributions. First, we quantify the ways in which user profiles are effected by the mobile context: (1) the extent to which users tend to be either fully non-mobile or mobile and (2) the relative activity of the mobile Twitter community. Second, we assess the differences in content between mobile and non-mobile tweets (posts to the Twitter platform). Our results show that mobile platforms produce very different patterns of Twitter usage. As part of our analysis, we propose and apply a classification system for tweets. We consider this to be the third contribution of this work. While other classification systems have been proposed, ours is the first to permit the independent encoding of a tweet’s form, content, and intended audience. In this paper we apply this system to show how tweets differ between mobile and non-mobile contexts. However, because of its flexibility and breadth, the schema may be useful to researchers studying Twitter content in other contexts as well.
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Oliveira, David, Luís Pedro, and Carlos Santos. "The Use of Mobile Applications in Higher Education Classrooms: An Exploratory Measuring Approach in the University of Aveiro." Education Sciences 11, no. 9 (August 31, 2021): 484. http://dx.doi.org/10.3390/educsci11090484.
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The proliferation of mobile devices and mobile applications has changed the way people communicate, work, and study, namely in higher education contexts. However, users have very different application usage habits from each other, and the data collecting instruments that typically support the studies usually rely on the perception that users report on their use. Hence, the reported user perception may not match the actual usage. Based on an exploratory approach, this article aims to analyze the use of mobile applications by students at the University of Aveiro. The study has a mixed approach that contemplates non-participant observation, the application of a survey, and log analysis. The triangulation between log records, the obtained data from the non-participant observation, and the surveys allows for a more objective assessment of the user profiles and will help to identify potential discrepancies between self-perception and actual use of mobile devices. The main conclusions show that the usage is quite significant and there is a huge tendency for the use of social media during classes. The most used applications are social networks which had a more intensive use than reported in the surveys. The methodology is also an important output of this study.
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Shin, M., and KW Lee. "Similarity attraction-based mobile advertising: designing and testing." MATEC Web of Conferences 277 (2019): 01006. http://dx.doi.org/10.1051/matecconf/201927701006.
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This study explores the possibility of applying the sociopsychological phenomenon known as similarity attraction to the creation of more customized and persuasive mobile advertising without privacy concerns, and for this purpose, we develop two applications, SensPlus and PerAds. While the SensPlus app accumulates the smartphone usage profiles of participants and constructs a regression model to predict their personality, the PerAds app presents user-created content according to the user's personality. The participants' response to a smartphone ad is measured in terms of invitation acceptance of the cherry blossom festival and classical music concert. The χ2 statistics imply participants respond differentially to the various types of smartphone ads according to the similarity between the personalities of the user and ad types. Our results indicate that the socio-psychological phenomenon can be introduced into practical application by replacing the personality trait factors with the smartphone usage pattern.
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Kotsopoulos, Dimosthenis, Cleopatra Bardaki, Stavros Lounis, and Katerina Pramatari. "Employee Profiles and Preferences towards IoT-enabled Gamification for Energy Conservation." International Journal of Serious Games 5, no. 2 (June 22, 2018): 65–85. http://dx.doi.org/10.17083/ijsg.v5i2.225.
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Gamification can be used to effect positive behavioral change in various fields, including energy conservation. This paper reports on a survey we conducted to formulate a holistic view of users’ profiles and preferences in an Internet of Things (IoT)-enabled gamified mobile application that provides personalized energy-saving recommendation tips to employees, towards conserving energy and adopting a more green behavior at the workplace. The collected insight dictates that a gamified app promoting energy saving at the workplace may become a daily habit for its users if it at least includes three game design elements: progression, levels and points. Additionally, we complement existing design guidelines about the requirements of each HEXAD gamification user type (Philanthropist, Socializer, Free Spirit, Achiever, Disruptor, Player) in game elements, specifically for energy efficiency applications. Thus, we apply and validate the HEXAD gamification user typology in a new context – multicultural workplaces – as well as domain – energy-saving. Moreover, the collected insight inspired us to devise a modular, rule-based mechanism for formulating personalized energy-saving recommendation tips tailored to the users’ profiles and game design choices. This research may assist researchers, as well as practitioners, in designing personalized gamified behavioral interventions, especially towards energy conservation in workplaces.
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Konstantakis, Markos, Yannis Christodoulou, John Aliprantis, and George Caridakis. "ACUX Recommender: A Mobile Recommendation System for Multi-Profile Cultural Visitors Based on Visiting Preferences Classification." Big Data and Cognitive Computing 6, no. 4 (November 28, 2022): 144. http://dx.doi.org/10.3390/bdcc6040144.
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In recent years, Recommendation Systems (RSs) have gained popularity in different scientific fields through the creation of (mostly mobile) applications that deliver personalized services. A mobile recommendation system (MRS) that classifies in situ visitors according to different visiting profiles could act as a mediator between their visiting preferences and cultural content. Drawing on the above, in this paper, we propose ACUX Recommender (ACUX-R), an MRS, for recommending personalized cultural POIs to visitors based on their visiting preferences. ACUX-R experimentally employs the ACUX typology for assigning profiles to cultural visitors. ACUX-R was evaluated through a user study and a questionnaire. The evaluation conducted showed that the proposed ACUX-R satisfies cultural visitors and is capable of capturing their nonverbal visiting preferences and needs.
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Manogna, T. "Mobile Application for Food Donation." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 15, 2021): 1195–97. http://dx.doi.org/10.22214/ijraset.2021.35171.
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This application is designed for any random user who’s willing to donate and have no proper chance or time to donate the food to orphanages, old-age homes, foodless and even stray animals. This application includes the history, status of donation and profiles of the donor, receiver and volunteer. This android application uses the information given by the donors, like-food type, mobile no., etc. to locate and collect the food. Then our NGO’s or volunteers distribute the food for the needed. This application mainly has 3 types of users: donor, receiver, volunteer. Donors log-in to their account and fill the details of food to be donated like- type, quantity and time at which food is prepared. Receivers (orphanages, old-age homes and foodless) log-in to their account using their credentials and mention their requirements like quantity of food and the type of food. Volunteers are mainly NGOs which can use the food for their purpose of feeding needy. Whenever there is a request from the donor, app notifies the volunteers of that city. All these details are stored in the main database.
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Jabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.
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Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.
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Cassitas Hino, Marcia, and Maria Alexandra Cunha. "Female lens in urban mobility: technology-use behavior and individual differences." Information Technology & People 34, no. 4 (May 20, 2021): 1370–97. http://dx.doi.org/10.1108/itp-05-2020-0342.
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PurposeThe purpose of this study is to investigate how women's individual differences influence urban mobility service technology-use behavior. The reduction in urban mobility is a major problem in countries with emerging economies, thus affecting both the economy and quality of life.Design/methodology/approachThe theoretical approach follows the individual differences theory of gender and information technology (IDTGIT). This research combines structured interviews to understand how the use of urban mobility service technology in daily routines is perceived, questionnaires to map individual differences and user demonstrations to capture how participants used mobility applications on their cell phones.FindingsThis study shows the influence of individual characteristics on the use of mobile apps and presents five behavioral profiles of women. This article goes beyond gender segregation to also show intragender differences.Practical implicationsThis study explains women's behavior regarding urban mobility mobile applications through the generation of five profiles. These profiles can inform public policy managers on urban mobility and provide opportunities for improving the services of companies in the urban transport service chain.Originality/valueWith an intragender perspective, this study identifies the influence of individual characteristics on the use of technology and suggests that contextual identity, a novel dimension of characteristics that influence technology-use behavior, is relevant in the adoption of technology by its users.
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Khalid Khoshnaw, Karwan Hoshyar, Zardasht Abdulaziz Abdulkarim Shwany, Twana Mustafa, and Shayda Khudhur Ismail. "Mobile recommender system based on smart city graph." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1771. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1771-1776.
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<span>Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.</span>
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Roman, Cristian, Ruizhi Liao, Peter Ball, and Shumao Ou. "Mobility and Network Selection in Heterogeneous Wireless Networks: User Approach and Implementation." Network Protocols and Algorithms 8, no. 2 (July 21, 2016): 107. http://dx.doi.org/10.5296/npa.v8i2.9327.
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The Intelligent Transport Systems (ITS) wireless infrastructure needs to support various safety and non-safety services for both autonomous and non-autonomous vehicles.The existing wireless infrastructures can already be used for communicating with different mobile entities at various monetary costs.A packet scheduler, included in a shim layer between the network layer and the medium access (MAC) layer, which is able to schedule packets between uncoordinated Radio Access Technologies (RATs) without modification of the wireless standards, has been devised and its performance evaluated.In this paper, we focus on the influence of mobility type in heterogeneous wireless networks.Three cases are considered based on the mobility in the city: walking, cycling, and driving. Realistic simulations are performed by generating mobility traces of Oxford from Google Maps and overlaying the real locations of existing WiFi Access Points. Results demonstrate that the shim layer approach can accommodate different user profiles and can be a useful abstraction to support Intelligent Transport Systems where there is no coordination between different wireless operators.
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Kursun, Bertan, Chemay Shola, and Yi Shen. "Initial validations of an iterative hearing-aid self-fitting procedure with Monte-Carlo simulations." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A125. http://dx.doi.org/10.1121/10.0010858.
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Self-directed gain adjustments may present a satisfactory solution for fitting a hearing aid (HA) without the requirement of supervision from a clinician. The current study develops a self-fitting procedure and provides initial evaluations of the procedure using Monte-Carlo simulations. In the procedure, the user interacts with the HA using a mobile device with a touch screen, while a continuous speech as a stimulus and a babble background noise is played in the room. The user explores the locations on the touch screen while hearing its effects on the hearing-aid processed audio in real time, until a preferred setting is identified. This adjustment process is repeated over trials, with the mathematical map between the touch-screen location (2D-coordinates) and the amplification profile of the HA being updated from trial to trial. This algorithm is evaluated using simulated listeners with known preferred amplification profiles serving as the ground truth. Results showed that estimates of the user preferred amplification profile, close to the ground truth, can be obtained by taking the mean of the user identified gain settings at the end of each trial.
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Marepalli, Sai Manoj, Razia Sultana, and Andreas Christ. "Research on System Architecture to Provide Maximum Security, End User Device Independency and User Centric Control over Content in Cloud." International Journal of E-Entrepreneurship and Innovation 4, no. 3 (July 2013): 38–52. http://dx.doi.org/10.4018/ijeei.2013070103.
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Cloud computing is the emerging technology providing IT as a utility through internet. The benefits of cloud computing are but not limited to service based, scalable, elastic, shared pool of resources, metered by use. Due to mentioned benefits the concept of cloud computing fits very well with the concept of m-learning which differs from other forms of e-learning, covers a wide range of possibilities opened up by the convergence of new mobile technologies, wireless communication structure and distance learning development. The concept of cloud computing like any other concept has not only benefits but also introduces myriad of security issues, such as transparency between cloud user and provider, lack of standards, security concerns related to identity, Service Level Agreements (SLA) inadequacy etc. Providing secure, transparent, and reliable services in cloud computing environment is an important issue. This paper introduces a secured three layered architecture with an advance Intrusion Detection System (advIDS), which overcomes different vulnerabilities on cloud deployed applications. This proposed architecture can reduce the impact of different attacks by providing timely alerts, rejecting the unauthorized access over services, and recording the new threat profiles for future verification. The goal of this research is to provide more control over data and applications to the cloud user, which are now mainly controlled by Cloud Service Provider (CSP).
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Bai, Jing. "College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology." Applied Bionics and Biomechanics 2022 (July 5, 2022): 1–6. http://dx.doi.org/10.1155/2022/7132817.
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With the advancement of the “big data” technology, college students inadvertently purchase personal advice while taking advantage of the exciting Internet to access information quickly and easily. In order to objectively achieve the real office of college students’ material enlightenment penetration in the mobile-friendly network, we choose the popular mobile social network, and we apply the natural clustering algorithm rules to segment the college students. Further, we identify college students, based on which we construct information leakage and apply the risk assessment design. The comprehensive entrepreneurial evaluation of the microblog platform combined with the user’s mobile complaints is utilized to conduct a psychological analysis on the key components and key communication channels of college students’ complaint leakage. We obtain ticket data using the social prospect method and refer to four dogmatic characteristic elements of query motivation. And we also collect dimensions through surrogate analysis. Based on the reference feature factor, four different user groups are rapidly moved. Dining, social, teaching and large users, and the model features of various usage profiles are described in combination with eight categories of user feature importance. The |objective is to improve college students’ awareness of social network and mobile partner network mobile information to a certain extent. We attempt to protect college students from fraud and installation plans, to standardize the management of online social platform of advertisements, and to progressively promote the disposal of network movable property due to security changes.
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Njoku, Howard O., Chibuoke T. Eneh, Mtamabari Simeon Torbira, and Chibeoso Wodi. "Infrared Detection of Elevations in Mobile Phone Temperatures Induced by Casings." Proceedings 42, no. 1 (April 11, 2020): 23. http://dx.doi.org/10.3390/ecsa-6-06570.
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The design and utility of mobile handheld devices have developed tremendously, from being initially intended for audio calls only to the recent incorporation of augmented reality in smartphones. Recent smartphone functions are power-intensive, and cause excessive heating in phone parts, primarily batteries and processors. Left unmanaged, phone temperatures would exceed the threshold temperature of discomfort, negatively affecting user experience. The use of phone casings has simultaneously become common in recent years. They form an additional barrier to heat dissipation from mobile devices, which has not been considered in existing studies. In this work, the thermal profiles of two identical smartphones were assessed during common tasks, including music playing, voice calling, video streaming and 3D online gaming. One of the phones (the test case) was operated while enclosed in a plastic phone casing, while the other (the control case) was bare. Transient surface temperature distributions were obtained with infrared imaging and thermocouple sensors, while processor and battery temperatures were obtained from inbuilt sensors. Test results showed that casings generally redirect the dissipation of the heat generated within the phone. For tasks involving contact with users’ hands, this will protect the user from high phone surface temperatures. However, the processor and battery temperatures are increased as a result. This user protection was not achieved in the online gaming task, for which the heat generated exceeded the insulating capacity of the plastic casing. The range of protection offered to phone users could be extended by using phone casings which incorporate phase change materials.
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Jiang, Jehn-Ruey, and Hanas Subakti. "An Indoor Location-Based Augmented Reality Framework." Sensors 23, no. 3 (January 26, 2023): 1370. http://dx.doi.org/10.3390/s23031370.
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This paper proposes an indoor location-based augmented reality framework (ILARF) for the development of indoor augmented-reality (AR) systems. ILARF integrates an indoor localization unit (ILU), a secure context-aware message exchange unit (SCAMEU), and an AR visualization and interaction unit (ARVIU). The ILU runs on a mobile device such as a smartphone and utilizes visible markers (e.g., images and text), invisible markers (e.g., Wi-Fi, Bluetooth Low Energy, and NFC signals), and device sensors (e.g., accelerometers, gyroscopes, and magnetometers) to determine the device location and direction. The SCAMEU utilizes a message queuing telemetry transport (MQTT) server to exchange ambient sensor data (e.g., temperature, light, and humidity readings) and user data (e.g., user location and user speed) for context-awareness. The unit also employs a web server to manage user profiles and settings. The ARVIU uses AR creation tools to handle user interaction and display context-aware information in appropriate areas of the device’s screen. One prototype AR app for use in gyms, Gym Augmented Reality (GAR), was developed based on ILARF. Users can register their profiles and configure settings when using GAR to visit a gym. Then, GAR can help users locate appropriate gym equipment based on their workout programs or favorite exercise specified in their profiles. GAR provides instructions on how to properly use the gym equipment and also makes it possible for gym users to socialize with each other, which may motivate them to go to the gym regularly. GAR is compared with other related AR systems. The comparison shows that GAR is superior to others by virtue of its use of ILARF; specifically, it provides more information, such as user location and direction, and has more desirable properties, such as secure communication and a 3D graphical user interface.
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Alrfaay, M., A. K. Ali, S. Chaoui, H. Lenando, and S. Alanazi. "R-SOR: Ranked Social-based Routing Protocol in Opportunistic Mobile Social Networks." Engineering, Technology & Applied Science Research 12, no. 1 (February 12, 2022): 7998–8006. http://dx.doi.org/10.48084/etasr.4612.
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Exploiting social information to improve routing performance is an increasing trend in Opportunistic Mobile Social Networks (OMSNs). Selecting the next message’s relay node based on the user’s social behavior is a critical factor in attaining a high delivery rate. So, to ascertain the most efficient selection of the next relay, the correlation between daily social activities and the social characteristics in the user profiles can be exploited. In this paper, we consider the impact of the social characteristics on mobile user activities during certain periods of the day and then rank these characteristics based on their relative importance in order to be included in the routing protocol. These processes consolidate the proposed Ranked Social-based Routing (R-SOR) protocol to provide an effective way for data dissemination in OMSN. We use the real data set INFOCOM06 to evaluate the proposed protocol. The experimental results show that the proposed protocol has higher routing efficiency than flooding-based protocols such as Epsoc and Epidemic, prediction-based protocols such as PRoPHET, and social-based protocols such as MSM and Bubble Rap.
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Torres-Ruiz, Miguel, Rolando Quintero, Giovanni Guzman, and Kwok Tai Chui. "Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information." Sustainability 15, no. 1 (December 28, 2022): 499. http://dx.doi.org/10.3390/su15010499.
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The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application.
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Silva, Luis Augusto, Valderi Reis Quietinho Leithardt, Carlos O. Rolim, Gabriel Villarrubia González, Cláudio F. R. Geyer, and Jorge Sá Silva. "PRISER: Managing Notification in Multiples Devices with Data Privacy Support." Sensors 19, no. 14 (July 13, 2019): 3098. http://dx.doi.org/10.3390/s19143098.
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With the growing number of mobile devices receiving daily notifications, it is necessary to manage the variety of information produced. New smart devices are developed every day with the ability to generate, send, and display messages about their status, data, and information about other devices. Consequently, the number of notifications received by a user is increasing and their tolerance may decrease in a short time. With this, it is necessary to develop a management system and notification controls. In this context, this work proposes a notification and alert management system called PRISER. Its focus is on user profiles and environments, applying data privacy criteria.
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Wu, Hongchen, Huaxiang Zhang, Lizhen Cui, and Xinjun Wang. "CEPTM: A Cross-Edge Model for Diverse Personalization Service and Topic Migration in MEC." Wireless Communications and Mobile Computing 2018 (August 12, 2018): 1–12. http://dx.doi.org/10.1155/2018/8056195.
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For several reasons, the cloud computing paradigm, e.g., mobile edge computing (MEC), is suffering from the problem of privacy issues. MEC servers provide personalization services to mobile users for better QoE qualities, but the ongoing migrated data from the source edge server to the destination edge server cause users to have privacy concerns and unwillingness of self-disclosure, which further leads to a sparsity problem. As a result, personalization services ignore valuable user profiles across edges where users have accounts in and tend to predict users’ potential purchases with insufficient sources, thereby limiting further improvement of QoE through personalization of the contents. This paper proposes a novel model, called CEPTM, which (1) collects mobile user data across multiple MEC edge servers, (2) improves the users’ experience in personalization services by loading collected diverse data, and (3) lowers their privacy concern with the improved personalization. This model also reveals that famous topics in one edge server can migrate into several other edge servers with users’ favorite content tags and that the diverse types of items could increase the possibility of users accepting the personalization service. In the experiment section, we use exploratory factor analysis to mathematically evaluate the correlations among those factors that influence users’ information disclosure in the MEC network, and the results indicate that CEPTM (1) achieves a high rate of personalization acceptance due to the availability of more data as input and highly diverse personalization as output and (2) gains the users’ trust because it collects user data while respecting individual privacy concerns and providing better personalization. It outperforms a traditional personalization service that runs on a single-edge server. This paper provides new insights into MEC diverse personalization services and privacy problems, and researchers and personalization providers can apply this model to merge popular users’ like trends throughout the MEC edge servers and generate better data management strategies.
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Poliak, O. "MODERN INFORMATION MOBILE TOOLS OF TRAINING IN HIGHER EDUCATION INSTITUTIONS." Visnyk Taras Shevchenko National University of Kyiv. Pedagogy, no. 2 (10) (2019): 48–51. http://dx.doi.org/10.17721/2415-3699.2019.10.13.
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The article is devoted to modern information tools of training as a way to optimize the process of training future specialists. Emphasis is placed on the importance of technological innovation and the theory of learning in the application of a variety of technologies to teaching and teaching. It is determined that the patterns of behavior of modern students are changing with respect to their rapidly expanding digital environment. Modern digital students think and process information differently from their predecessors – thinking in parallel and linear models and reading visual images that could be used to read text. It is emphasized that new technologies, such as those described in the article, can give students of the information society and the interaction society the opportunity to receive increased and continuous participation in learning. Active students interact with their environment and manipulate objects in that environment, observe the impact of their interventions, and build their own interpretations of the phenomena and results of manipulations, share these interpretations with others. These descriptions offer connections to previously identified features of the Information Society and the Society for Interaction and New Technologies. Different approaches of providing modern active education in higher education institutions are analyzed. In summary, mobile learning devices can provide individuality through unique profiles, this category of technology can be a proper choice of learning environment aimed at enhancing individual learning orientation. Mobile devices need to be in tune with how events are happening around the world and the web, even if those devices have been used for personal purposes only. It is emphasized that a critical aspect of such a strategy is that the mobile device is not simply integrated into the educational and higher education institutions or fell into the hands of its user, as has been done, for example, with recent initiatives on computers and laptops. On the contrary, individual profiles must be intentionally designed into the device and involved in the educational process in educational institutions in general and in higher education institutions in particular.
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Thompson, Santi, and Michele Reilly. "Embedded Metadata Patterns Across Web Sharing Environments." International Journal of Digital Curation 13, no. 1 (December 27, 2018): 223–34. http://dx.doi.org/10.2218/ijdc.v13i1.607.
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This research project tried to determine how or if embedded metadata followed the digital object as it was shared on social media platforms by using EXIFTool, a variety of social media platforms and user profiles, the embedded metadata extracted from selected New York Public Library (NYPL) and Europeana images, PDFs from open access science journals, and captured mobile phone images. The goal of the project was to clarify which embedded metadata fields, if any, migrated with the object as it was shared across social media.
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Ali, Ismail A., Martin Fleury, and Mohammed Ghanbari. "Distortion-Based Slice Level Prioritization for Real-Time Video over QoS-Enabled Wireless Networks." Advances in Multimedia 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/319785.
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This paper presents a prioritization scheme based on an analysis of the impact on objective video quality when dropping individual slices from coded video streams. It is shown that giving higher-priority classified packets preference in accessing the wireless media results in considerable quality gain (up to 3 dB in tests) over the case when no prioritization is applied. The proposed scheme is demonstrated for an IEEE 802.11e quality-of-service- (QoS-) enabled wireless LAN. Though more complex prioritization systems are possible, the proposed scheme is crafted for mobile interactive or user-to-user video services and is simply implemented within the Main or the Baseline profiles of an H.264 codec.
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Thatcher, Jim. "You are where you go, the commodification of daily life through ‘location’." Environment and Planning A: Economy and Space 49, no. 12 (September 15, 2017): 2702–17. http://dx.doi.org/10.1177/0308518x17730580.
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Recent years have seen an explosion in the investment into and valuation of mobile spatial applications. With multiple applications currently valued at well over one billion U.S. dollars, mobile spatial applications and the data they generate have come to play an increasingly significant role in the function of late capitalism. Empirically based upon a series of interviews conducted with mobile application designers and developers, this article details the creation of a digital commodity termed ‘location.’ ‘Location’ is developed through three discursive poles: Its storing of space and time as digital data object manipulable by code, its spatial and temporal immediacy, and its ability to ‘add value’ or ‘tell a story’ to both end-users and marketers. As a commodity it represents the sum total of targeted marking information, including credit profiles, purchase history, and a host of other information available through data mining or sensor information, combined with temporal immediacy, physical location, and user intent. ‘Location’ is demonstrated to exist as a commodity from its very inception and, as such, to be a key means through which everyday life is further entangled with processes of capitalist exploitation.
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Revier, Kevin. "“Now you’re connected”: Carceral visuality and police power on MobilePatrol." Theoretical Criminology 24, no. 2 (June 11, 2018): 314–34. http://dx.doi.org/10.1177/1362480618779401.
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Police increasingly rely on new media software for public communication and policing operations. One such software is MobilePatrol: Public Safety App, a free to use mobile phone application marketed by data and analytics company Appriss Safety. It compiles information including mug shot photos, sex offender lists, and most wanted profiles for public access. To capture carceral visuality on the application, I conduct an ethnographic content analysis of police use in upstate New York. I penned a daily user log, took in-app “screenshots”, and analyzed user product reviews. I find that MobilePatrol reinforces an emerging new carceral visibility where an assemblage of police records are rapidly disseminated for widespread consumption, further driving data-led state entrenchment into the public sphere.
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Cho, Hyunsung, Akhil Mathur, and Fahim Kawsar. "FLAME." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 3 (September 6, 2022): 1–29. http://dx.doi.org/10.1145/3550289.
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Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. While we witness increasing applications of FL in the area of mobile sensing, such as human activity recognition (HAR), FL has not been studied in the context of a multi-device environment (MDE), wherein each user owns multiple data-producing devices. With the proliferation of mobile and wearable devices, MDEs are increasingly becoming popular in ubicomp settings, therefore necessitating the study of FL in them. FL in MDEs is characterized by being not independent and identically distributed (non-IID) across clients, complicated by the presence of both user and device heterogeneities. Further, ensuring efficient utilization of system resources on FL clients in a MDE remains an important challenge. In this paper, we propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in MDEs, and bring consistency in inference performance across devices. FLAME features (i) user-centered FL training utilizing the time alignment across devices from the same user; (ii) accuracy- and efficiency-aware device selection; and (iii) model personalization to devices. We also present an FL evaluation testbed with realistic energy drain and network bandwidth profiles, and a novel class-based data partitioning scheme to extend existing HAR datasets to a federated setup. Our experiment results on three multi-device HAR datasets show that FLAME outperforms various baselines by 4.3-25.8% higher F1 score, 1.02-2.86x greater energy efficiency, and up to 2.06x speedup in convergence to target accuracy through fair distribution of the FL workload.
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Sârbu, Annamaria, Simona Miclăuș, Angela Digulescu, and Paul Bechet. "Comparative Analysis of User Exposure to the Electromagnetic Radiation Emitted by the Fourth and Fifth Generations of Wi-Fi Communication Devices." International Journal of Environmental Research and Public Health 17, no. 23 (November 27, 2020): 8837. http://dx.doi.org/10.3390/ijerph17238837.
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A suitable metric to describe human exposure to microwaves emitted by wireless communication devices is still incomplete. By using both theoretical analysis and experimental validation (in controlled and real deployed networks), we analyze and compare the specificity of exposure due to data transmissions in different configurations of fourth and fifth generation wireless fidelity (Wi-Fi) standards in the proximity of a mobile device. Measurements made use of the capability of the amplitude probability density incorporated in a real-time spectrum analyzer, proving its agility of highlighting different user exposure profiles. The results are presented comparatively and indicate that, in Wi-Fi networks, the modulation and coding scheme (MCS) should be used together with the duty cycle for an improved exposure assessment. The present work introduces the emitted energy density per bit in describing the user’s exposure to Wi-Fi signals and proves its superiority in characterizing the true levels of exposure for the IEEE 802.11n and 802.11ac standards of communication.
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Mapp, G., F. Katsriku, M. Aiash, N. Chinnam, R. Lopes, E. Moreira, R. M. Porto Vanni, and M. Augusto. "Exploiting Location and Contextual Information to Develop a Comprehensive Framework for Proactive Handover in Heterogeneous Environments." Journal of Computer Networks and Communications 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/748163.
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The development and deployment of several wireless and cellular networks mean that users will demand to be always connected as they move around. Mobile nodes will therefore have several interfaces and connections will be seamlessly switched among available networks using vertical handover techniques. Proactive handover mechanisms can be combined with the deployment of a number of location-based systems that provide location information to a very high degree of accuracy in different contexts. Furthermore, this new environment will also allow contextual information such as user profiles as well as the availability of using location and contextual information to provide efficient handover mechanisms. Using location-based techniques, it is possible to demonstrate that the Time Before Vertical Handover as well as the Network Dwell Time can be accurately estimated. These techniques are dependent on accurately estimating the handover radius. This paper investigates how location and context awareness can be used to estimate the best handover radius. The paper also explores how such techniques may be integrated into the Y-Comm architecture which is being used to explore the development of future mobile networks. Finally, the paper highlights the use of ontological techniques as a mechanism for specifying and prototyping such systems.
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Muthumani, M., and K. Chinnasamy. "Enhancing Role and Impact of Librarians in Research Output through Internet Tools: A Case Study Based on Google Scholar Profiles of Engineering Colleges in and around Madurai." Asian Journal of Information Science and Technology 9, S1 (February 5, 2019): 72–74. http://dx.doi.org/10.51983/ajist-2019.9.s1.218.
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A librarian in an academic institution plays a potentially very crucial role in the research output of the institution. Digital literacy of the librarian will be of immense help for the researchers and the institution with the ever increasing availability of internet and mobile tools. The wealth of information contained in the publicly accessible Google Scholar profiles is one such useful tool. A case study is carried out by analysing the profiles of research faculty in 12 (twelve) engineering colleges located in Madurai district. The user profiles having verified email IDs with the domain names of these institutions form part of this study. It has been demonstrated that the librarians can play an enhanced role in research output and its impact by effectively using such readily available information in a myriad ways. With the citation indicators viz. citations, h-index and i10-index for different researchers in the institution, the librarian can help the researcher and the institution compare the productivity and impact of research work. The librarian will be able to find out the publications with higher research impact and make informed decisions on subscriptions etc. Such bench marking will also help the institution to attract research talent; to identify and reward impactful works; and to publicize achievements. By creating a tag cloud of research areas in an engineering college derived through Google Scholar profiles it is illustrated how librarian can plan the library resources to be made available to the users. (S)he can further probe the above labels and find out the highly acclaimed journals in the field, post latest developments in the research field, help the users connect with the other leading researchers in the field etc. Similarly, a study of co-authors of a researcher in one of the colleges in Madurai reveals that the collaborative research network extends beyond district / state borders and comprises institutions of countries such as Australia, China and Korea. Having known this, a librarian can understand the research network – physical and virtual – and facilitate further collaboration. The readily available Google Scholar user profiles of researchers of an academic institution give a good deal of information that covers many of the impact indicators used in frameworks such as Becker Medical Library Model for Research Impact. Such possibilities are elaborated using a case study of the profiles of researchers in twelve Madurai based Engineering Colleges.
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Mahou, Xosé, Bran Barral, Ángela Fernández, Ramón Bouzas-Lorenzo, and Andrés Cernadas. "eHealth and mHealth Development in Spain: Promise or Reality?" International Journal of Environmental Research and Public Health 18, no. 24 (December 10, 2021): 13055. http://dx.doi.org/10.3390/ijerph182413055.
Full textAbstract:
In the last decades, the use of Information and Communication Technologies (ICTs) has progressively spread to society and public administration. Health is one of the areas in which the use of ICTs has more intensively developed through what is now known as eHealth. That area has recently included mHealth. Spanish health system has stood out as one of the benchmarks of this technological revolution. The development of ICTs applied to health, especially since the outbreak of the pandemic caused by SARS Cov-2, has increased the range of health services delivered through smartphones and the development of subsequent specialized apps. Based on the data of a Survey on Use and Attitudes regarding eHealth in Spain, the aim of this research was to conduct a comparative analysis of the different eHealth and mHealth user profiles. The results show that the user profile of eHealth an mHealth services in Spain is not in a majority. Weaknesses are detected both in the knowledge and use of eHealth services among the general population and in the usability or development of their mobile version. Smartphones can be a democratizing vector, as for now, access to eHealth services is only available to wealthy people, widening inequality.
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Kounas, Dimitrios, Orfefs Voutyras, Georgios Palaiokrassas, Antonios Litke, and Theodora Varvarigou. "QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities." Applied System Innovation 3, no. 2 (April 7, 2020): 19. http://dx.doi.org/10.3390/asi3020019.
Full textAbstract:
Location-based services are becoming extremely popular due to the widespread use of smartphones and other mobile and portable devices. These services mainly rely on the sincerity of users, who can spoof the location they report to them. For applications with higher security requirements, the user should be unable to report a location different than the real one. Proof of Location protocols provide a solution to secure localization by validating the device’s location with the help of nearby nodes. We propose QuietPlace, a novel protocol that is based on ultrasound and provides strong identities, proving the location of the owner of a device, without exposing though their identity. QuietPlace provides unforgeable proof that is able to resist to various attacks while respecting the users’ privacy. It can work regardless of certificate authority and location-based service and is able to support trust schemas that evaluate the participants’ behavior. We implement and validate the protocol for Android devices, showing that ultrasound-based profiles offer a better performance in terms of maximum receiving distance than audible profiles, and discuss its strengths and weaknesses, making suggestions about future work.
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Awoyemi, B. S., B. T. Maharaj, and A. S. Alfa. "Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7385627.
Full textAbstract:
Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA’s productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.