A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

Understanding Teacher and Student Learning Situated in a School-wide Implementation of Fractions Instruction

Thesis (Ph.D.)--University of Washington, 2016-06

Learning from one another: evaluating the impact of horizontal knowledge exchange for environmental management and governance

There is increasing advocacy for inclusive community-based approaches to environmental management, and growing evidence that involving communities improves the sustainability of social-ecological systems. Most community-based approaches rely on partnerships and knowledge exchange between communities, civil society organizations, and professionals such as practitioners and/or scientists. However, few models have actively integrated more horizontal knowledge exchange from community to community. We reflect on the transferability of community owned solutions between indigenous communities by exploring challenges and achievements of community peer-to-peer knowledge exchange as a way of empowering communities to face up to local environmental and social challenges. Using participatory visual methods, indigenous communities of the North Rupununi (Guyana) identified and documented their community owned solutions through films and photostories. Indigenous researchers from this community then shared their solutions with six other communities that faced similar challenges within Guyana, Suriname, Venezuela, Colombia, French Guiana, and Brazil. They were supported by in-country civil society organizations and academics. We analyzed the impact of the knowledge exchange through interviews, field reports, and observations. Our results show that indigenous community members were significantly more receptive to solutions emerging from, and communicated by, other indigenous peoples, and that this approach was a significant motivating force for galvanizing communities to make changes in their community. We identified a range of enabling factors, such as building capacity for a shared conceptual and technical understanding, that strengthens the exchange between communities and contributes to a lasting impact. With national and international policy-makers mobilizing significant financial resources for biodiversity conservation and climate change mitigation, we argue that the promotion of community owned solutions through community peer-to-peer exchange may deliver more long-lasting, socially and ecologically integrated, and investment-effective strategies compared to top-down, expert led, and/or foreign-led initiatives.

The “social” aspect of social-ecological systems: a critique of analytical frameworks and findings from a multisite study of coastal sustainability

We evaluate whether society can adequately be conceptualized as a component of social-ecological systems, given social theory and the current outputs of systems-based research. A mounting critique from the social sciences posits that resilience theory has undertheorized social entities with the concept of social-ecological systems. We trace the way that use of the term has evolved, relating to social science theory. Scientometic and network analysis provide a wide range of empirical data about the origin, growth, and use of this term in academic literature. A content analysis of papers in Ecology and Society demonstrates a marked emphasis in research on institutions, economic incentives, land use, population, social networks, and social learning. These findings are supported by a review of systems science in 18 coastal assessments. This reveals that a systems-based conceptualization tends to limit the kinds of social science research favoring quantitative couplings of social and ecological components and downplaying interpretive traditions of social research. However, the concept of social-ecological systems remains relevant because of the central insights concerning the dynamic coupling between humans and the environment, and its salient critique about the need for multidisciplinary approaches to solve real world problems, drawing on heuristic devices. The findings of this study should lead to more circumspection about whether a systems approach warrants such claims to comprehensiveness. Further methodological advances are required for interdisciplinarity. Yet there is evidence that systems approaches remain highly productive and useful for considering certain social components such as land use and hybrid ecological networks. We clarify advantages and restrictions of utilizing such a concept, and propose a reformulation that supports engagement with wider traditions of research in the social sciences.

Integration of learning and practice as part of the educational system development in Eritrea. "School Management" - a distance learning program by technical university of Kaiserslautern

The distance learning program "School Management" supports decision makers at the school and ministerial levels in the shaping of formal and informal learning processes at different levels in schools and curricula in Eritrea. This paper examines how the distance learning program is interconnected to educational system development. (DIPF/Orig.)

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Finnish Preclinical and Clinical Medical Students as Strategic L2 English Learners: Language Learning Strategies and Communicating with Patients

This thesis focused on medical students’ language learning strategies for patient encounters. The research questions concerned the types of learning strategies that medical students use and the differences between the preclinical students and the clinical students, two groups who have had varying amounts of experience with patients. Additionally, strategy use was examined through activity systems to gain information on the context of language learning strategy use in order to learn language for patient encounters. In total, 130 first-year medical students (preclinical) and 39 fifth-year medical students (clinical) participated in the study by filling in a questionnaire on language learning strategies. In addition, two students were interviewed in order to create activity systems for the medical students at different stages of their studies. The study utilised both quantitative and qualitative research methods; the analysis of the results relies on Oxford’s Strategic Self-Regulation Model in the quantitative part and on activity theory in the qualitative part. The theoretical sections of the study introduced earlier research and theories regarding English for specific purposes, language learning strategies and activity theory. The results indicated that the medical students use affective, sociocultural-interactive and metasociocultural-interactive strategies often and avoid using negative strategies, which hinder language learning or cease communication altogether. Slight differences between the preclinical and clinical students were found, as clinical students appear to use affective and metasociocultural-interactive strategies more frequently compared to the preclinical students. The activity systems of the two students interviewed were rather similar. The students were at different stages of their studies, but their opinions were very similar. Both reported the object of learning to be mutual understanding between the patient and the doctor, which in part explains the preference for strategies that support communication and interaction. The results indicate that the nature of patient encounters affects the strategy use of the medical students at least to some extent.

Rule Generalisation in Intrusion Detection Systems using Snort

Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. The majority of IDSs use a set of signatures that define what suspicious traffic is, and Snort is one popular and actively developing open-source IDS that uses such a set of signatures known as Snort rules. Our aim is to identify a way in which Snort could be developed further by generalising rules to identify novel attacks. In particular, we attempted to relax and vary the conditions and parameters of current Snort rules, using a similar approach to classic rule learning operators such as generalisation and specialisation. We demonstrate the effectiveness of our approach through experiments with standard datasets and show that we are able to detect previously undetected variants of various attacks. We conclude by discussing the general effectiveness and appropriateness of generalisation in Snort based IDS rule processing. Keywords: anomaly detection, intrusion detection, Snort, Snort rules

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

Learning from the lagoon: Research in development in Solomon Islands

A major challenge for international agricultural research is to find ways to improve the nutrition and incomes of people left behind by the Green Revolution. To better address the needs of the most marginal and vulnerable people, the CGIAR Research Program on Aquatic Agricultural Systems (AAS) developed the research-in-development (RinD) approach. In 2012, WorldFish started to implement RinD in Solomon Islands. By building people’s capacity to analyze and address development problems, actively engaging relevant stakeholders, and linking research to these processes, RinD aims to develop an alternative approach to addressing hunger and poverty. This report describes the key principles and implementation process, and assesses the emergent outcomes of this participatory, systems-oriented and transformative research approach in Solomon Islands.

Students' perceptions about assessment using an e-learning platform.

SCAP’s Information Systems Department is composed of about twenty teachers who have, for several years, been using an e-learning environment (Moodle) combined with traditional assessment. A new e-assessment strategy was implemented recently in order to evaluate a practical topic, the use of spreadsheets to solve management problems. This topic is common to several courses of different undergraduate degree programs. Being e-assessment an outstanding task regarding theoretical topics, it becomes even more challenging when the topics under evaluation are practical. In order to understand the implications of this new type of assessment from the viewpoint of the students, questionnaires and interviews were undertaken. In this paper the analysis of the questionnaires are presented and discussed.

Learning Sustainability with EPS@ISEP – Development of an Insectarium

8th International Symposium on Project Approaches in Engineering Education (PAEE)

Learning Sustainability with EPS@ISEP – Development of a Water Disinfection System

Proceedings of the 8th International Symposium on Project Approaches in Engineering Education (PAEE), Guimarães, 2016

Rule Generalisation in Intrusion Detection Systems using Snort

Intrusion Detection Systems (IDSs) provide an important layer of security for computer systems and networks, and are becoming more and more necessary as reliance on Internet services increases and systems with sensitive data are more commonly open to Internet access. An IDS’s responsibility is to detect suspicious or unacceptable system and network activity and to alert a systems administrator to this activity. The majority of IDSs use a set of signatures that define what suspicious traffic is, and Snort is one popular and actively developing open-source IDS that uses such a set of signatures known as Snort rules. Our aim is to identify a way in which Snort could be developed further by generalising rules to identify novel attacks. In particular, we attempted to relax and vary the conditions and parameters of current Snort rules, using a similar approach to classic rule learning operators such as generalisation and specialisation. We demonstrate the effectiveness of our approach through experiments with standard datasets and show that we are able to detect previously undetected variants of various attacks. We conclude by discussing the general effectiveness and appropriateness of generalisation in Snort based IDS rule processing. Keywords: anomaly detection, intrusion detection, Snort, Snort rules

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.