Personality Trait Inference Via Mobile Phone Sensors: A Machine Learning Approach

This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we were able to predict users' personality up to a 0.78 F1 score on a two class problem. Given the fast growing number of data collected from mobile phones, our novel personality indicators open the door to exciting avenues for future research in social sciences. Our results reveal distinct behavioral patterns that proved to be differentially predictive of big five personality traits. They potentially enable cost effective, questionnaire free investigation of personality related questions at an unprecedented scale. We show how a combination of rich behavioral data obtained with smartphone sensing and the use of machine learning techniques can help to advance personality research and can inform both practitioners and researchers about the different behavioral patterns of personality. These findings have practical implications for organizations harnessing mobile sensor data for personality assessment, guiding the refinement of more precise and efficient prediction models in the future.Comment: 9 pages, 5 figure

Applied deep learning in intelligent transportation systems and embedding exploration

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization and data retrieval. The first part of this dissertation is about a Spatio-TEmporal Fuzzy neural Network (STEF-Net) which accurately predicts passenger demand by incorporating the complex interaction of all known important factors, such as temporal, spatial and external information. Specifically, a convolutional long short-term memory network is employed to simultaneously capture spatio-temporal feature interaction, and a fuzzy neural network to model external factors. A novel feature fusion method with convolution and an attention layer is proposed to keep the temporal relation and discriminative spatio-temporal feature interaction. Experiments on a large-scale real-world dataset show the proposed model outperforms the state-of-the-art approaches. The second part is a light-weight and energy-efficient system which detects transportation modes using only accelerometer sensors in smartphones. Understanding people’s transportation modes is beneficial to many civilian applications, such as urban transportation planning. The system collects accelerometer data in an efficient way and leverages a convolutional neural network to determine transportation modes. Different architectures and classification methods are tested with the proposed convolutional neural network to optimize the system design. Performance evaluation shows that the proposed approach achieves better accuracy than existing work in detecting people’s transportation modes. The third component of this dissertation is a deep reinforcement learning model, based on Q learning, to control the traffic light. Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. In the proposed model, the complex traffic scenario is quantified as states by collecting data and dividing the whole intersection into grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards, which is further optimized by several components, such as dueling network, target network, double Q-learning network, and prioritized experience replay. The simulation results in Simulation of Urban MObility (SUMO) show the efficiency of the proposed model in controlling traffic lights. The last part of this dissertation studies the hierarchical structure in an embedding system. Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which generates storage-inefficient representation and fails to effectively encode the internal semantic structure of data. A regularized autoencoder framework is proposed to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of data points, aiming at capturing semantic structures of data. Experimental results on synthetic and real-world datasets show that the proposed HKD embedding can effectively reveal the semantic structure of data via visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy

The role of speech technology in biometrics, forensics and man-machine interface

Day by day Optimism is growing that in the near future our society will witness the Man-Machine Interface (MMI) using voice technology. Computer manufacturers are building voice recognition sub-systems in their new product lines. Although, speech technology based MMI technique is widely used before, needs to gather and apply the deep knowledge of spoken language and performance during the electronic machine-based interaction. Biometric recognition refers to a system that is able to identify individuals based on their own behavior and biological characteristics. Fingerprint success in forensic science and law enforcement applications with growing concerns relating to border control, banking access fraud, machine access control and IT security, there has been great interest in the use of fingerprints and other biological symptoms for the automatic recognition. It is not surprising to see that the application of biometric systems is playing an important role in all areas of our society. Biometric applications include access to smartphone security, mobile payment, the international border, national citizen register and reserve facilities. The use of MMI by speech technology, which includes automated speech/speaker recognition and natural language processing, has the significant impact on all existing businesses based on personal computer applications. With the help of powerful and affordable microprocessors and artificial intelligence algorithms, the human being can talk to the machine to drive and control all computer-based applications. Today's applications show a small preview of a rich future for MMI based on voice technology, which will ultimately replace the keyboard and mouse with the microphone for easy access and make the machine more intelligent

Feedback presentation for mobile personalised digital physical activity coaching platforms

User interface design and feedback are important in personalised behavior change support systems. This paper discusses two service platforms that monitor user’s physical activity through wearable sensors and that present the user personalised feedback. Important principles for effectiveness of such systems are personalisation or tailoring, context- awareness, feedback and interaction. We focus here on the presentation of feedback to the user. We present results of a number of short and long term user studies in which we compare different forms of feedback presentation: text, graphics and with or without an anthropomorphic graphical talking character. Results show that although some users like the talking character they don’t have a positive effect on adherence to the activity program. The outcomes of the user evaluations support our beliefs that personal motivation is of primary importance for the effectiveness of these systems. Technical challenges ahead are to support more personal and context-aware feedback, more variations as well as the possibility for more interaction with the coaching system

Improving elderly access to audiovisual and social media, using a multimodal human-computer interface

With the growth of Internet and especially, the proliferation of social media services, an opportunity has emerged for greater social and technological integration of the elderly. However, the adoption of new technologies by this segment of the population is not always straightforward mainly due to the physical and cognitive difficulties that are typically associated with ageing. Thus, for elderly to take advantage of new technologies and services that can help improve their quality of life, barriers must be broken by designing solutions with those needs in mind from the start. The aim of this work is to verify whether Multimodal Human-Computer Interaction (MHCI) systems designed with Universal Accessibility principles, taking into account elderly specific requirements, facilitate the adoption and access to popular Social Media Services (SMSs) and Audiovisual Communication Services, thus potentially contributing to the elderly social and technological integration. A user study was initially conducted in order to learn about the limitations and requirements of elderly people with existing HCI, concerning particularly SMSs and Audiovisual Communication Services, such as Facebook or Windows Live Messenger (WLM). The results of the study, basically a set of new MHCI requirements, were used to inform further development and enhancement of a multimodal prototype previously proposed for mobility-impaired individuals, now targeting the elderly. The prototype allows connecting users with their social networks through a text, audio and video communication service and integrates with SMSs, using natural interaction modalities, like speech, touch and gesture. After the development stage a usability evaluation study was conducted. The study reveals that such multimodal solution could simplify accessibility to the supported services, through the provision of simpler to use interfaces, by adopting natural interaction modalities and by being more satisfying to use by the elderly population, than most of the current graphical user interfaces for those same services, such as Facebook.Com o crescimento da Internet e, especialmente, das redes sociais surge a oportunidade para uma maior integração social e tecnológica dos idosos. No entanto, a adoção de novas tecnologias por essa população nem sempre é simples, principalmente devido às dificuldades físicas e cognitivas que estão associadas com o envelhecimento. Assim, e para que os idosos possam tirar proveito das novas tecnologias e serviços que podem ajudar a melhorar sua qualidade de vida, essas barreiras devem ser ultrapassadas desenhando soluções de raiz com essas necessidades em mente. O objetivo deste trabalho é verificar se interfaces humano-computador multimodais desenhadas com base em princípios de Acessibilidade Universal, tendo em conta requisitos específicos da população idosa, proporcionam um acesso simplificado a serviços de média social e serviços de comunicação audiovisuais, potencialmente contribuindo para a integração social e tecnológica desta população. Um estudo com utilizadores foi inicialmente conduzido a fim de apurar as necessidades especiais desses utilizadores com soluções de software, mais especificamente serviços de média social e serviços de conferência, como o Facebook ou o Windows Live Messenger. Os resultados do estudo foram utilizados para planear o desenvolvimento de um protótipo multimodal proposto anteriormente para utilizadores com mobilidade reduzida. Este permite ligar utilizadores às suas redes sociais através de um serviço de conferência por texto, áudio e vídeo, e um serviço integrado de média social, usando modalidades de interação natural, como o toque, fala e gestos. Após a fase de desenvolvimento foi realizado um estudo de usabilidade. Esse estudo revelou que este tipo de soluções pode simplificar a acessibilidade aos serviços considerados, dado ter interfaces mais simples, por adotar modalidades de interação mais naturais e por ser mais gratificante do que a maioria das interfaces gráficas atuais para os mesmos serviços, como por exemplo o Facebook

Proceedings of the 3rd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 3rd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems