Mobile User Interaction Research Articles

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.

An Integrated Remote Control-Based Human-Robot Interface for Education Application

Portable interfaced robot arms equipped with mobile user interactions are significantly being utilized in modern world. The application of teaching robotics is being used in challenging pandemic situation but it is still challenging due to mathematical formulation. This article utilizes the augmented reality (AR) concept for remote control-based human-robot interaction using the Bluetooth correspondence. The proposed framework incorporates different modules like a robot arm control, a regulator module and a distant portable smartphone application for envisioning the robot arm points for its real-time relevance. This novel approach fuses AR innovation into portable application which permit the continuous virtual coordination with actual physical platform. The simulation yields effective outcomes with 96.94% accuracy for testing stage while maintaining error and loss values of 0.194 and 0.183 respectively. The proposed interface gives consistent results for teaching application in real time changing environment by outperforming existing methods with an accuracy improvement of 13.4

Mobile behavioral biometrics for passive authentication

• We present a first benchmark of HuMIdb, a publicly available HCI database. • We evaluate touchscreen and background sensor data for mobile passive authentication. • We train a recurrent neural network with triplet loss for each individual modality. • A multimodal system is achieved through the fusion of modalities at score level. • Our results show the suitability of behavioral biometrics for mobile authentication. Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, 1 1 https://github.com/BiDAlab/HuMIdb one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval.

IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning

The prosperity of smart mobile devices has made mobile crowdsensing (MCS) a promising paradigm for completing complex sensing and computation tasks. In the past, great efforts have been made on the design of incentive mechanisms and task allocation strategies from MCS platform's perspective to motivate mobile users’ participation. However, in practice, MCS participants face many uncertainties coming from their sensing environment as well as other participants’ strategies, and how do they interact with each other and make sensing decisions is not well understood. In this paper, we take MCS participants’ perspectives to derive an online sensing policy to maximize their payoffs via MCS participation. Specifically, we model the interactions of mobile users and sensing environments as a multi-agent Markov decision process. Each participant cannot observe others’ decisions, but needs to decide her effort level in sensing tasks only based on local information, e.g., her own record of sensed signals’ quality. To cope with the stochastic sensing environment, we develop an intelligent crowdsensing algorithm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IntelligentCrowd</i> by leveraging the power of multi-agent reinforcement learning (MARL). Our algorithm leads to the optimal sensing policy for each user to maximize the expected payoff against stochastic sensing environments, and can be implemented at the individual participant's level in a distributed fashion. Numerical simulations demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IntelligentCrowd</i> significantly improves users’ payoffs in sequential MCS tasks under various sensing dynamics.

MUIT: A Domain-Specific Language and its Middleware for Adaptive Mobile Web-Based User Interfaces in WS-BPEL

In enterprise organizations, the Bring-Your-Own-Device (BYOD) requirement has become prevalent as employees use their own mobile devices to process the workflow-oriented tasks. Consequently, it calls for approaches that can quickly develop and integrate mobile user interactions into existing business processes, and adapt to various contexts. However, designing, developing, and deploying adaptive and mobile-oriented user interfaces for existing process engines are non-trivial, and require significant systematic efforts. To address this issue, we present a novel middleware-based approach, called MUIT, to developing and deploying the Mobility, User Interactions and Tasks into WS-BPEL engines. MUIT provides a Domain-Specific Language (DSL) that provides some intuitive facilities to support the declarative development of adaptive, mobile-oriented, and Web-based user interfaces in WS-BPEL. The DSL can significantly reduce developers' manual efforts of developing user interactions by preventing arbitrarily mixed code, and its runtime supports satisfactory user experiences. Additionally, MUIT can be seamlessly integrated into WS-BPEL without intrusions of existing process instances. We implement a proof-of-concept prototype by integrating MUIT into the commodity WS-BPEL-based Apusic Platform, and evaluate the performance and usability of MUIT platform.

Minimizing Latency to Support VR Social Interactions Over Wireless Cellular Systems via Bandwidth Allocation

Immersive social interactions of mobile users are soon to be enabled within a virtual space, by means of virtual reality (VR) technologies and wireless cellular systems. In a VR mobile social network, the states of all interacting users should be updated synchronously and with low latency via two-way communications with edge computing servers. The resulting end-to-end latency depends on the relationship between the virtual and physical locations of the wireless VR users and of the edge servers. In this letter, the problem of analyzing and optimizing the end-to-end latency is investigated for a simple network topology, yielding important insights into the interplay between physical and virtual geometries.

Finger Gesture-based Three-Dimension Mobile User Interaction Using a Rear-facing Camera

In this paper, we propose a 3D mobile user interaction for 3D space using a mono camera in mobile devices. The proposed system can support a UI using only the index finger of the hand holding the mobile device. Proposed fingertip detection algorithm was developed to overcome the limitation of the skin color segmentation of previous studies under varying lighting conditions and complex background using MMGC and AdaBoost algorithm. Also in proposed 3D mobile user interface, the estimated coordinates and area of the fingertip can be mapping 3D space of implemented application. The experimental result indicates the feasibility of the proposed algorithm for finger movement-based mobile user interfaces.

Point Navigation Map method to track low-income mobile user's interaction

Mobile phones obtained large popularization in Brazil thanks to pre-paid (pay as you go) contracts. This kind of contract represents 82% of mobile phone connections in Brazil, corresponding to 143 million in a total of 174 million mobile phone accesses in the country. In this scenario, a research about the usability of mobile phones with 17 to 24 year old youngsters, living in poor communities, enrolled in the NGO Spectaculu. Usability tests were carried through with 30 youngsters from a NGO in Rio de Janeiro. Was made a extraction of the navigation sequence with data information of input and output from the system. Point Navigation Map was created to track low-income mobile user's interaction.

Quality of experience key metrics framework for network mobility user

This paper presents a new framework of Quality of Experience (QoE) in mobile network. The user acceptance of a mobile application depends on the application’s perceived QoE. Currently, there is no comprehensive framework which integrates QoE and Quality of Service (QoS). The components of framework consist of the network mobility users, traffic classification matrixes and QoE key metric. The performance evaluation of this framework for assessing QoE in service class and user class model is presented. The simulation results demonstrate the user satisfaction evaluated by the achieved QoE at different service classes and user classes. The new QoE score model can give deeper understanding of the mobile user interaction within the mobile environment; quantify the user’s QoE and their relationship with QoS.

Security in Mobile IPv6: A survey

Secure mobile communication is essential for the pervasive accessibility of critical information infrastructure. Connecting control systems with the business enterprise, wireless telemetry and mobile user interaction with critical infrastructure systems are examples of services that motivate the need for secure mobile communication. Mobile IPv6 is being touted to provide communication support for such services. The security of Mobile IPv6 poses key challenges impeding its wide-scale adoption. Several security mechanisms have been proposed in the literature. This paper surveys security vulnerabilities of Mobile IPv6, provides a taxonomy for the main existing and proposed solutions, and then extends to outline some open issues.

Speech and Language Processing for Multimodal Human-Computer Interaction

In this paper, we describe our recent work at Microsoft Research, in the project codenamed Dr. Who, aimed at the development of enabling technologies for speech-centric multimodal human-computer interaction. In particular, we present in detail MiPad as the first Dr. Who's application that addresses specifically the mobile user interaction scenario. MiPad is a wireless mobile PDA prototype that enables users to accomplish many common tasks using a multimodal spoken language interface and wireless-data technologies. It fully integrates continuous speech recognition and spoken language understanding, and provides a novel solution to the current prevailing problem of pecking with tiny styluses or typing on minuscule keyboards in today's PDAs or smart phones. Despite its current incomplete implementation, we have observed that speech and pen have the potential to significantly improve user experience in our user study reported in this paper. We describe in this system-oriented paper the main components of MiPad, with a focus on the robust speech processing and spoken language understanding aspects. The detailed MiPad components discussed include: distributed speech recognition considerations for the speech processing algorithm designs a stereo-based speech feature enhancement algorithm used for noise-robust front-end speech processings Aurora2 evaluation results for this front-end processings speech feature compression (source coding) and error protection (channel coding) for distributed speech recognition in MiPads HMM-based acoustic modeling for continuous speech recognition decodings a unified language model integrating context-free grammar and N-gram model for the speech decodings schema-based knowledge representation for the MiPad's personal information management tasks a unified statistical framework that integrates speech recognition, spoken language understanding and dialogue managements the robust natural language parser used in MiPad to process the speech recognizer's outputs a machine-aided grammar learning and development used for spoken language understanding for the MiPad tasks Tap & Talk multimodal interaction and user interface designs back channel communication and MiPad's error repair strategys and finally, user study results that demonstrate the superior throughput achieved by the Tap & Talk multimodal interaction over the existing pen-only PDA interface. These user study results highlight the crucial role played by speech in enhancing the overall user experience in MiPad-like human-computer interaction devices.

Situated Computing: A Paradigm for the Mobile User-Interaction with Multimedia Sources

Today, users interact with computers in an explicit manner and the system's response is independent from their situations. Hence, it is difficult to integrate computers with working life as embedded tools, which can facilitate users to accomplish real world objectives easily. Situated computing is a new paradigm for mobile computer users based on their physical context and activities carried out as a part of their working business. It provides the mechanism to have a mobile computer as a utility to satisfy the user's real world requirements as well as an infrastructure for the situated interaction using applications. In this paper, we are presenting a metaphor called situation metaphor to model interaction between the user and mobile computers in order to achieve expectations of situated computing. A three-layered schema is followed in developing situation metaphor. We discuss extensively the theoretical foundation and framework for the situation metaphor, and followed by applications developed based on the framework.