Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Learning from imperfect data : incremental learning and Few-shot Learning

In recent years, artificial intelligence (AI) has achieved great success in many fields, e.g., computer vision, speech recognition, recommendation engines, and neural language processing. Although impressive advances have been made, AI algorithms still suffer from an important limitation: they rely on large-scale datasets. In contrast, human beings naturally possess the ability to learn novel knowledge from real-world and imperfect data such as a small number of samples or a non-static continual data stream. Attaining such an ability is particularly appealing. Specifically, an ideal AI system with human-level intelligence should work with the following imperfect data scenarios. 1)~The training data distribution changes while learning. In many real scenarios, data are streaming, might disappear after a given period of time, or even can not be stored at all due to storage constraints or privacy issues. As a consequence, the old knowledge is over-written, a phenomenon called catastrophic forgetting. 2)~The annotations of the training data are sparse. There are also many scenarios where we do not have access to the specific large-scale data of interest due to privacy and security reasons. As a consequence, the deep models overfit the training data distribution and are very likely to make wrong decisions when they encounter rare cases. Therefore, the goal of this thesis is to tackle the challenges and develop AI algorithms that can be trained with imperfect data. To achieve the above goal, we study three topics in this thesis. 1)~Learning with continual data without forgetting (i.e., incremental learning). 2)~Learning with limited data without overfitting (i.e., few-shot learning). 3)~Learning with imperfect data in real-world applications (e.g., incremental object detection). Our key idea is learning to learn/optimize. Specifically, we use advanced learning and optimization techniques to design data-driven methods to dynamically adapt the key elements in AI algorithms, e.g., selection of data, memory allocation, network architecture, essential hyperparameters, and control of knowledge transfer. We believe that the adaptive and dynamic design of system elements will significantly improve the capability of deep learning systems under limited data or continual streams, compared to the systems with fixed and non-optimized elements. More specifically, we first study how to overcome the catastrophic forgetting problem by learning to optimize exemplar data, allocate memory, aggregate neural networks, and optimize key hyperparameters. Then, we study how to improve the generalization ability of the model and tackle the overfitting problem by learning to transfer knowledge and ensemble deep models. Finally, we study how to apply incremental learning techniques to the recent top-performance transformer-based architecture for a more challenging and realistic vision, incremental object detection.Künstliche Intelligenz (KI) hat in den letzten Jahren in vielen Bereichen große Erfolge erzielt, z. B. Computer Vision, Spracherkennung, Empfehlungsmaschinen und neuronale Sprachverarbeitung. Obwohl beeindruckende Fortschritte erzielt wurden, leiden KI-Algorithmen immer noch an einer wichtigen Einschränkung: Sie sind auf umfangreiche Datensätze angewiesen. Im Gegensatz dazu besitzen Menschen von Natur aus die Fähigkeit, neuartiges Wissen aus realen und unvollkommenen Daten wie einer kleinen Anzahl von Proben oder einem nicht statischen kontinuierlichen Datenstrom zu lernen. Das Erlangen einer solchen Fähigkeit ist besonders reizvoll. Insbesondere sollte ein ideales KI-System mit Intelligenz auf menschlicher Ebene mit den folgenden unvollkommenen Datenszenarien arbeiten. 1)~Die Verteilung der Trainingsdaten ändert sich während des Lernens. In vielen realen Szenarien werden Daten gestreamt, können nach einer bestimmten Zeit verschwinden oder können aufgrund von Speicherbeschränkungen oder Datenschutzproblemen überhaupt nicht gespeichert werden. Infolgedessen wird das alte Wissen überschrieben, ein Phänomen, das als katastrophales Vergessen bezeichnet wird. 2)~Die Anmerkungen der Trainingsdaten sind spärlich. Es gibt auch viele Szenarien, in denen wir aus Datenschutz- und Sicherheitsgründen keinen Zugriff auf die spezifischen großen Daten haben, die von Interesse sind. Infolgedessen passen die tiefen Modelle zu stark an die Verteilung der Trainingsdaten an und treffen sehr wahrscheinlich falsche Entscheidungen, wenn sie auf seltene Fälle stoßen. Daher ist das Ziel dieser Arbeit, die Herausforderungen anzugehen und KI-Algorithmen zu entwickeln, die mit unvollkommenen Daten trainiert werden können. Um das obige Ziel zu erreichen, untersuchen wir in dieser Arbeit drei Themen. 1)~Lernen mit kontinuierlichen Daten ohne Vergessen (d. h. inkrementelles Lernen). 2) ~ Lernen mit begrenzten Daten ohne Überanpassung (d. h. Lernen mit wenigen Schüssen). 3) ~ Lernen mit unvollkommenen Daten in realen Anwendungen (z. B. inkrementelle Objekterkennung). Unser Leitgedanke ist Lernen lernen/optimieren. Insbesondere verwenden wir fortschrittliche Lern- und Optimierungstechniken, um datengesteuerte Methoden zu entwerfen, um die Schlüsselelemente in KI-Algorithmen dynamisch anzupassen, z. B. Auswahl von Daten, Speicherzuweisung, Netzwerkarchitektur, wesentliche Hyperparameter und Steuerung des Wissenstransfers. Wir glauben, dass das adaptive und dynamische Design von Systemelementen die Leistungsfähigkeit von Deep-Learning-Systemen bei begrenzten Daten oder kontinuierlichen Streams im Vergleich zu Systemen mit festen und nicht optimierten Elementen erheblich verbessern wird. Genauer gesagt untersuchen wir zunächst, wie das katastrophale Vergessensproblem überwunden werden kann, indem wir lernen, Beispieldaten zu optimieren, Speicher zuzuweisen, neuronale Netze zu aggregieren und wichtige Hyperparameter zu optimieren. Dann untersuchen wir, wie die Verallgemeinerungsfähigkeit des Modells verbessert und das Overfitting-Problem angegangen werden kann, indem wir lernen, Wissen zu übertragen und tiefe Modelle in Ensembles zusammenzufassen. Schließlich untersuchen wir, wie man inkrementelle Lerntechniken auf die jüngste transformatorbasierte Hochleistungsarchitektur für eine anspruchsvollere und realistischere Vision, inkrementelle Objekterkennung, anwendet

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Intelligent Management of Virtualised Computer Based Workloads and Systems

Managing the complexity within virtualised IT infrastructure platforms is a common problem for many organisations today. Computer systems are often highly consolidated into a relatively small physical footprint compared with previous decades prior to late 2000s, so much thought, planning and control is necessary to effectively operate such systems within the enterprise computing space. With the development of private, hybrid and public cloud utility computing this has become even more relevant; this work examines how such cloud systems are using virtualisation technology and embedded software to leverage advantages, and it uses a fresh approach of developing and creating an Intelligent decision engine (expert system). Its aim is to help reduce the complexity of managing virtualised computer-based platforms, through tight integration, high-levels of automation to minimise human inputs, errors, and enforce standards and consistency, in order to achieve better management and control. The thesis investigates whether an expert system known as the Intelligent Decision Engine (IDE) could aid the management of virtualised computer-based platforms. Through conducting a series of mixed quantitative and qualitative experiments in the areas of research, the initial findings and evaluation are presented in detail, using repeatable and observable processes and provide detailed analysis on the recorded outputs. The results of the investigation establish the advantages of using the IDE (expert system) to achieve the goal of reducing the complexity of managing virtualised computer-based platforms. In each detailed area examined, it is demonstrated how using a global management approach in combination with VM provisioning, migration, failover, and system resource controls can create a powerful autonomous system