48 research outputs found

    Analysis and use of the emotional context with wearable devices for games and intelligent assistants

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    In this paper, we consider the use of wearable sensors for providing affect-based adaptation in Ambient Intelligence (AmI) systems. We begin with discussion of selected issues regarding the applications of affective computing techniques. We describe our experiments for affect change detection with a range of wearable devices, such as wristbands and the BITalino platform, and discuss an original software solution, which we developed for this purpose. Furthermore, as a test-bed application for our work, we selected computer games. We discuss the state-of-the-art in affect-based adaptation in games, described in terms of the so-called affective loop. We present our original proposal of a conceptual design framework for games, called the affective game design patterns. As a proof-of-concept realization of this approach, we discuss some original game prototypes, which we have developed, involving emotion-based control and adaptation. Finally, we comment on a software framework, that we have previously developed, for context-aware systems which uses human emotional contexts. This framework provides means for implementing adaptive systems using mobile devices with wearable sensors

    Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems

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    Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential. The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical Systems (CP-ADS). The main objective was broken down into three sub-objectives: 1) Identifying key-requirements that an explainable CP-ADS should satisfy, 2) Developing supervised ANN-based explainable CP-ADSs, 3) Developing unsupervised ANN-based explainable CP-ADSs. In achieving those objectives, this dissertation provides the following contributions: 1) a set of key-requirements that an explainable CP-ADS should satisfy, 2) a methodology for deriving summaries of the knowledge of a trained supervised CP-ADS, 3) a methodology for validating derived summaries, 4) an unsupervised neural network methodology for learning cyber-physical (CP) behavior, 5) a methodology for visually and linguistically explaining the learned CP behavior. All the methods were implemented on real-world and benchmark datasets. The set of key-requirements presented in the first contribution was used to evaluate the performance of the presented methods. The successes and limitations of the presented methods were identified. Furthermore, steps that can be taken to overcome the limitations were proposed. Therefore, this dissertation takes several necessary steps toward developing explainable ANN-based CP-ADS and serves as a framework that can be expanded to develop trustworthy ANN-based CP-ADSs

    Tourist experiences recommender system based on emotion recognition with wearable data

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    The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.This research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja. We thank the members of the GICAC group (Research Group in Administrative and Accounting Sciences) of the Universidad Santo Tomás Seccional Tunja for their participation in the experimental phase of this investigation

    Explainable artificial intelligence for developing smart cities solutions

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    Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results

    Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques

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    The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste. A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions

    Using blog-like documents to investigate software practice: Benefits, challenges, and research directions

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    Background An emerging body of research is using grey literature to investigate software practice. One frequently occurring type of grey literature is the blog post. Whilst there are prospective benefits to using grey literature and blog posts to investigate software practice, there are also concerns about the quality of such material. Objectives To identify and describe the benefits and challenges to using blog‐like content to investigate software practice, and to scope directions for further research. Methods We conduct a review of previous research, mainly within software engineering, to identify benefits, challenges, and directions and use that review to complement our experiences of using blog posts in research. Results and Conclusion We identify and organise benefits and challenges of using blog‐like documents in software engineering research. We develop a definition of the type of blog‐like document that should be of (more) value to software engineering researchers. We identify and scope several directions in which to progress research into and with blog‐like documents. We discuss similarities and differences in secondary and primary studies that use blog‐like documents and similarities and differences between the use of blog‐like documents and the use of already established research methods, eg, interview and survey

    Negoitation in Modernity : The BAZNAS (National Zakat Collection Agency) and the Philosophy of Zakat (Alms) Socialization in Indonesia

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    To pay Zakat (alms) is an obligation for a Muslim. However, this religious obligation cannot encourage Muslims in Indonesia to pay Zakat. In fact, in several cities, some Zakat organizations are established to collect the zakat. Some of them is the BAZNAS which is spread in most cities in Indonesia. In fact, this organization is a semi-government because there are some collaborations between the BAZNAS and local government in most regions. This collaboration indicates also that it tries to get benefit from the modern and established government structure. This article aims to know the BAZNAS negoitation with modernity, specifically it wants to deal with the BAZNAS zakat socialization. Using a case study, this article finds that the zakat organization like the BAZNAS Kepulauan Meranti Indonesia deals with a complicated negoitation with modernity through its zakat socialization. In fact, there is a religious understanding among Muslims there that to pay zakat is an obligation but it cannot deal with their religious awareness to pay zakat. This article identifies that disseminating the zakat payment obligation is a never ending project. The BAZNAS improves Muslim understanding about Zakat through socialization. Some socialization activities done are using modern instruments but some are not. Keywords : Zakat, BAZNAS (National Zakat Collection Agency), Socialization

    New fish product ideas generated by European consumers

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    Food lifestyles are changing; people have less time to spend on food purchase and preparation, therefore leading to increasing demand for new food products. However, around 76% of new food products launched in the market fail within the first year (Nielsen, 2014). One of the most effective ways to enhance new products’ success in the market is by incorporating consumers’ opinions and needs during the New Product Development (NPD) process (Moon et al., 2018). This study aimed to explore the usefulness of a qualitative technique, focus groups, to generate new aquaculture fish product ideas as well as to identify the most relevant product dimensions affecting consumers’ potential acceptance.Peer ReviewedPostprint (published version

    Recent Developments in Smart Healthcare

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    Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

    GSI Scientific Report 2009 [GSI Report 2010-1]

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