59 research outputs found
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Recently, sound recognition has been used to identify sounds, such as car and
river. However, sounds have nuances that may be better described by
adjective-noun pairs such as slow car, and verb-noun pairs such as flying
insects, which are under explored. Therefore, in this work we investigate the
relation between audio content and both adjective-noun pairs and verb-noun
pairs. Due to the lack of datasets with these kinds of annotations, we
collected and processed the AudioPairBank corpus consisting of a combined total
of 1,123 pairs and over 33,000 audio files. One contribution is the previously
unavailable documentation of the challenges and implications of collecting
audio recordings with these type of labels. A second contribution is to show
the degree of correlation between the audio content and the labels through
sound recognition experiments, which yielded results of 70% accuracy, hence
also providing a performance benchmark. The results and study in this paper
encourage further exploration of the nuances in audio and are meant to
complement similar research performed on images and text in multimedia
analysis.Comment: This paper is a revised version of "AudioSentibank: Large-scale
Semantic Ontology of Acoustic Concepts for Audio Content Analysis
Recognition, Analysis, and Assessments of Human Skills using Wearable Sensors
One of the biggest social issues in mature societies such as Europe and Japan
is the aging population and declining birth rate. These societies have a serious
problem with the retirement of the expert workers, doctors, and engineers etc.
Especially in the sectors that require long time to make experts in fields like medicine and industry; the retirement and injuries of the experts, is a serious problem. The technology to support the training and assessment of skilled workers (like doctors, manufacturing
workers) is strongly required for the society. Although there are some solutions for
this problem, most of them are video-based which violates the privacy of the subjects.
Furthermore, they are not easy to deploy due to the need for large training data.
This thesis provides a novel framework to recognize, analyze, and assess human
skills with minimum customization cost. The presented framework tackles this problem
in two different domains, industrial setup and medical operations of catheter-based
cardiovascular interventions (CBCVI).
In particular, the contributions of this thesis are four-fold. First, it proposes an
easy-to-deploy framework for human activity recognition based on zero-shot learning
approach, which is based on learning basic actions and objects. The model recognizes
unseen activities by combinations of basic actions learned in a preliminary way and involved objects. Therefore, it is completely configurable by the user and can be used to detect completely new activities.
Second, a novel gaze-estimation model for attention driven object detection task is
presented. The key features of the model are: (i) usage of the deformable convolutional
layers to better incorporate spatial dependencies of different shapes of objects and
backgrounds, (ii) formulation of the gaze-estimation problem in two different way, as a
classification as well as a regression problem. We combine both formulations using a
joint loss that incorporates both the cross-entropy as well as the mean-squared error in
order to train our model. This enhanced the accuracy of the model from 6.8 by using only
the cross-entropy loss to 6.4 for the joint loss.
The third contribution of this thesis targets the area of quantification of quality of
i
actions using wearable sensor. To address the variety of scenarios, we have targeted two
possibilities: a) both expert and novice data is available , b) only expert data is available,
a quite common case in safety critical scenarios.
Both of the developed methods from these scenarios are deep learning based. In the
first one, we use autoencoders with OneClass SVM, and in the second one we use the
Siamese Networks. These methods allow us to encode the expertâs expertise and to learn
the differences between novice and expert workers. This enables quantification of the
performance of the novice in comparison to the expert worker.
The fourth contribution, explicitly targets medical practitioners and provides a
methodology for novel gaze-based temporal spatial analysis of CBCVI data. The developed
methodology allows continuous registration and analysis of gaze data for analysis
of the visual X-ray image processing (XRIP) strategies of expert operators in live-cases scenarios and may assist in transferring expertsâ reading skills to novices
S-BPM in the Wild
This is the first book to present field studies on the application of subject-oriented business process management (S-BPM). Each case presents a specific story and focuses on an essential modeling or implementation issue, and most end with implications or suggestions for further studies. Significant variables and success factors are identified that were discovered during the respective study and lead to suggesting S-BPM novelties. For each case, the authors explain step-by-step how the story develops, and provide readers guidance by detailing the respective rationale. The studies covered are clustered according to three main S-BPM themes: Part I âBusiness Operation Supportâ documents approaches to the practical development of S-BPM solutions in various application domains and organizational settings, while Part II âConsultancy and Education Supportâ highlights cases that can help to train readers in S-BPM modeling and knowledge acquisition for S-BPM lifecycle iterations. It also refers to architecting S-BPM solutions for application cases based on hands-on experience. Part III âTechnical Execution Supportâ focuses on concepts for utilizing specific theories and technologies to execute S-BPM models. It also addresses how to create reference models for certain settings in the field. Lastly, the appendix covers all relevant aspects needed to grasp S-BPM modeling and apply it based on fundamental examples. Its format reconciles semantic precision with syntactic rigor.>Addressing the needs of developers, educators and practitioners, this book will help companies to learn from the experiences of first-time users and to develop systems that fit their business processes, explaining the latest key methodological and technological S-BPM developments in the fields of training, research and application
Symmetry-Adapted Machine Learning for Information Security
Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis
Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino
On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-Ââit 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall âCavallerizza Realeâ. The CLiC-Ââit conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
Student Expectations: The effect of student background and experience
CONTEXT
The perspectives and previous experiences that students bring to their programs of study can affect their approaches to study and the depth of learning that they achieve Prosser & Trigwell, 1999; Ramsden, 2003). Graduate outcomes assume the attainment of welldeveloped independent learning skills which can be transferred to the work-place.
PURPOSE
This 5-year longitudinal study investigates factors influencing studentsâ approaches to learning in the fields of Engineering, Software Engineering, and Computer Science, at two higher education institutes delivering programs of various levels in Australia and New Zealand. The study aims to track the development of student approaches to learning as they progress through their program. Through increased understanding of studentsâ approaches, faculty will be better able to design teaching and learning strategies to meet the needs of an increasingly diverse student body. This paper reports on the first stage of the project.
APPROACH
In August 2017, we ran a pilot of our survey using the Revised Study Process Questionnaire(Biggs, Kember, & Leung, 2001) and including some additional questions related to student demographics and motivation for undertaking their current program of study. Data were analysed to evaluate the usefulness of data collected and to understand the demographics of the student cohort. Over the period of the research, data will be collected using the questionnaire and through focus groups and interviews.
RESULTS
Participants provided a representative sample, and the data collected was reasonable, allowing the questionnaire design to be confirmed.
CONCLUSIONS
At this preliminary stage, the study has provided insight into the student demographics at both institutes and identified aspects of studentsâ modes of engagement with learning. Some areas for improvement of the questionnaire have been identified, which will be implemented for the main body of the study
Foundations of Trusted Autonomy
Trusted Autonomy; Automation Technology; Autonomous Systems; Self-Governance; Trusted Autonomous Systems; Design of Algorithms and Methodologie
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective
Book of short Abstracts of the 11th International Symposium on Digital Earth
The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium
Recent Developments in Smart Healthcare
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
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