Powerpoint presentation from a UBC Library Graduate Students' workshop on Current Awareness Tools for Researchers in the Faculty of Arts. Topics covered include Journal Table of Contents Alerts, Saved Search Alerts, Citation Alerts, Google Alerts, and RSS Feeds/RSS Readers.

Health applications are usually complicated and low end devices do not benefit from them. The focus of this thesis is on expandable health services platform for low end cell phones. Large number of mobile phones in the world are incapable using web or modern operating systems and pre-installed SMS application is the preferred communication medium. Moreover, SMS does not require a stable connection so text-based health information can still be available even during natural disasters. Although our platform is accessible through communication forms other than SMS. We propose a scalable platform for light weight health applications, providing novel and proactive client communication. Using cloud we assure the scalability, elasticity and reliability of the server side. Our multi-layered architecture provides separation of concerns and decoupling of communication and business logic. Furthermore, plug-ins can expand and customize functionalities.

Modern Electronic Health Record (EHR) systems are now integral to healthcare. Having evolved from hospital billing and laboratory systems in the 80's, EHR systems have grown considerably as we learn to represent more and more aspects of patient encounter, diagnosis and treatment digitally. EHR user interfaces, however, lag considerably behind their consumer-electronics counterparts in usability, most notably with respect to customizability. This limitation is especially evident in the implementation of audible alerts that are coupled to sensors or timing devices in intensive-care settings. The most current standard, (ISO/IEC 60601-1-8) has been designed for alerts that are intended to signal situations of varying priorities: however, it is not universally implemented, and has been criticized for the difficulty that healthcare providers have in discriminating between individual alarms, and for the failure to incorporate prior research with respect to "sense of urgency" as it applies to alarm efficacy. In the present work, however, we consider that there are more effective means to allow a user to identify an alarm correctly than "sense of urgency" response. This thesis focuses on the problem of correct identification of alerts: what happens when a human subject is allowed to create or designate (i.e., personalize) one's own alerts? Given the ubiquity, low costs and commoditization of consumer-electronics devices, we believe that it is just a matter of time before such devices become the norm in critical care and replace existing, special-purpose devices for information delivery at the point of patient care. We built a tool, PASA (Personalized Alert Study Application), that would allow us to capture and edit sounds and orchestrate studies that would contrast any two types of sounds. PASA facilitated a study where study participant's responses to "personalized" sounds were contrasted with sounds that meet the ISO/IEC 60601-1-8:2012 standard. We performed two sub-studies that contrasted responses to two banks of 6-alerts and 10-alerts. The 6-alert study was repeated with the same subjects after two weeks without training to measure recall. We observed that accuracy, reaction time, and retention were significantly improved with the personalized sounds. For example, the median errors for the 6-alert baseline study were 4 for personalized vs. 27 for standard alerts. For the 6-alert repeat study it was 7 vs. 43. The median for the 10-alert study was 1 for personalized vs. 55 for standard alerts. Accuracy for recognition, while remaining constant for personalized alerts, degraded considerably for standardized alerts as the number of alerts increased from 6 to 10. We conclude that personalization of alerts may improve information delivery and reduce cognitive overload on the health care provider. This potential positive effect at the point of patient care merits further studies in a clinical or simulated clinical setting.

The only way to simplify and promote the effective use of an alerting system that must be comprehensive in its coverage of hazardous or non-normal conditions is to convey top level information that provides an indication of criticality and identity. In an attempt to reduce the number of aural alerting signals presented in aircraft flight decks, this investigation pursued advances toward the development of a simple aural alert categorization scheme that provides flight deck function and urgency level information. In Experiment 1, 20 subjects having "normal" hearing threshold levels provided magnitude estimation urgency ratings for a series of aural alerts. These ratings revealed that subjects perceived low, moderate, and high urgency levels within each of four equally urgent aural alerting sets. In Experiment 2, 12 subjects having "normal" hearing threshold levels participated in a brief training session and then performed a sound identification task in conjunction with an automated and manual tracking task. Sound identification data revealed that subjects correctly identified the alerting set (i.e., major flight deck function) and urgency level associated with each of 12 aural alerts in 96.53% of the trials occurring during automated tracking and in 95.83% of the trials occurring during manual tracking; furthermore, subjects correctly identified each alerting set, urgency level, and aural alert equally often during each tracking task condition. Electroencephalogram (EEG) data recorded throughout the performance of each tracking task condition revealed that manual tracking required a significantly higher level of attentional engagement than automated tracking. Subjective assessments of workload collected after the performance of each tracking task condition revealed that a significantly higher level of workload was experienced during the manual condition of the tracking task than during the automated condition of the tracking task. Collectively, this investigation's results indicated that acoustic parameter manipulations can be used to create four distinctive alerting sets that each convey three levels of urgency and that these alerting sets and urgency levels can be accurately identified when two levels of workload and attentional engagement are experienced. / Ph. D.

Submitted by Luiza Maria Pereira de Oliveira (luiza.oliveira@ufpe.br) on 2015-05-15T14:58:14Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO Daniel Araújo Melo.pdf: 2348702 bytes, checksum: cdf9ac0421311267960355f9d6ca4479 (MD5) / Made available in DSpace on 2015-05-15T14:58:14Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) DISSERTAÇÃO Daniel Araújo Melo.pdf: 2348702 bytes, checksum: cdf9ac0421311267960355f9d6ca4479 (MD5) Previous issue date: 2014-09-08 / Modern virtual plagues, or malwares, have focused on internal host infection and em-ploy evasive techniques to conceal itself from antivirus systems and users. Traditional network security mechanisms, such as Firewalls, IDS (Intrusion Detection Systems) and Antivirus Systems, have lost efficiency when fighting malware propagation. Recent researches present alternatives to detect malicious traffic and malware propagation through traffic analysis, however, the presented results are based on experiments with biased artificial traffic or traffic too specific to generalize, do not consider the existence of background traffic related with local network services or demands previous knowledge of networks infrastructure. Specifically don’t consider a well-known intru-sion detection systems problem, the high false positive rate which may be responsible for 99% of total alerts. This dissertation proposes a framework (ARCA – Alerts Root Cause Analysis) capable of guide a security engineer, or system administrator, to iden-tify alerts root causes, malicious or not, and allow the identification of malicious traffic and false positives. Moreover, describes modern malwares propagation mechanisms, presents methods to detect malwares through analysis of IDS alerts and false positives reduction. ARCA combines an aggregation method based on Relative Uncertainty with Apriori, a frequent itemset mining algorithm. Tests with 2 real datasets show an 88% reduction in the amount of alerts to be analyzed without previous knowledge of network infrastructure.

The devastating southeast Asian tsunami of December 26, 2004 has brought home the destructive consequences of coastal hazards in an absence of effective warning systems. Since the 1946 tsunami that destroyed much of Hilo, Hawai'i, a network of pole mounted sirens has been used to provide an early public alert of future tsunamis. However, studies in the 1960s showed that understanding of the meaning of siren soundings was very low and that ambiguity in understanding had contributed to fatalities in the 1960 tsunami that again destroyed much of Hilo. The Hawaiian public has since been exposed to monthly tests of the sirens for more than 25 years and descriptions of the system have been widely published in telephone books for at least 45 years. However, currently there remains some uncertainty in the level of public understanding of the sirens and their implications for behavioral response. Here, we show from recent surveys of Hawai'i residents that awareness of the siren tests and test frequency is high, but these factors do not equate with increased understanding of the meaning of the siren, which remains disturbingly low (13%). Furthermore, the length of time people have lived in Hawai'i is not correlated systematically with understanding of the meaning of the sirens. An additional issue is that warning times for tsunamis gene rated locally in Hawai'i will be of the order of minutes to tens of minutes and limit the immediate utility of the sirens. Natural warning signs of such tsunamis may provide the earliest warning to residents. Analysis of a survey subgroup from Hilo suggests that awareness of natural signs is only moderate, and a majority may expect notification via alerts provided by official sources. We conclude that a major change is needed in tsunami education, even in Hawai'i, to increase public understanding of, and effective response to, both future official alerts and natural warning signs of future tsunamis.

More U.S. students are attending college than at any time in history, but many of these students are poorly prepared for college coursework. Higher education institutions are challenged to increase the overall student success rate. The study community college implemented an early alert system to identify students with potential course performance concerns, but it is not known whether other student data might also predict academic performance at the study site. Guided by Tinto's and Astin's respective works on student persistence, the purpose of this correlational study was to investigate the relationships between students' demographic, background, and environmental variables and course success, for students identified by the early alert system. Stratified random sampling of 4 academic years of student data using 50% of the early alert students in the top 25 courses with the highest number of early alert students yielded a sample of 3,873 students. Predictor variables were gender, race/ethnicity, age, income status, campus, faculty status, first generation to college, and course times. The dependent variable was the course outcome. A regression analysis examined the predictive ability of each variable, and race/ethnicity was the only predictor found to be statistically significant. African-American students were the highest risk students for failing a course. A white paper was developed to share the study findings with the administration at the study college regarding the early alert system and other factors related to course success. Increasing student success may produce an overall positive social impact on society by increasing students' job prospects and ability to contribute economically in their communities.

Based on Kim Witte's proven Extended Parallel Process Model, a mobile communication system was developed in which men were sent reminders about their health. This study focused on reminding men about testicular self-examination (TSE), a proactive behavior used to detect testicular cancer, through the use of text messaging. A cohort of 75 men were recruited for this study and placed into one of four groups. All participants were provided with information concerning TSE and told to perform the exam monthly; two of the four groups were sent reminders via text message while the other two groups were told once about the behavior. An original 30-item survey was used to measure intention. Proper data analysis could not be performed due to an attrition rate of 71%. Nonetheless, a significant relationship was observed between pre- and post-test adherence as reported by the participants. In addition, the measurement tool was assessed and determined to be useful in measuring intention to perform TSE. Internal consistency measures were reported as 0.672 and 0.626, both of which would have been higher with a larger sample size. While further research and analysis is recommended, this study has laid a foundation for a way to communicate with young men about their health.

Dans les systèmes d'informations, les outils de détection produisent en continu un grand nombre d'alertes.Des outils de corrélation permettent de réduire le nombre d'alertes et de synthétiser au sein de méta-alertes les informations importantes pour les administrateurs.Cependant, la complexité des règles de corrélation rend difficile leur écriture et leur maintenance.Cette thèse propose par conséquent une méthode pour générer des règles de corrélation de manière semi-automatique à partir d’un scénario d’attaque exprimé dans un langage de niveau d'abstraction élevé.La méthode repose sur la construction et l'utilisation d’une base de connaissances contenant une modélisation des éléments essentiels du système d’information (par exemple les nœuds et le déploiement des outils de détection). Le procédé de génération des règles de corrélation est composé de différentes étapes qui permettent de transformer progressivement un arbre d'attaque en règles de corrélation.Nous avons évalué ce travail en deux temps. D'une part, nous avons déroulé la méthode dans le cadre d'un cas d'utilisation mettant en jeu un réseau représentatif d'un système d'une petite entreprise.D'autre part, nous avons mesuré l'influence de fautes touchant la base de connaissances sur les règles de corrélation générées et sur la qualité de la détection. / Information systems produce continuously a large amount of messages and alerts. In order to manage this amount of data, correlation system are introduced to reduce the alerts number and produce high-level meta-alerts with relevant information for the administrators. However, it is usually difficult to write complete and correct correlation rules and to maintain them. This thesis describes a method to create correlation rules from an attack scenario specified in a high-level language. This method relies on a specific knowledge base that includes relevant information on the system such as nodes or the deployment of sensor. This process is composed of different steps that iteratively transform an attack tree into a correlation rule. The assessment of this work is divided in two aspects. First, we apply the method int the context of a use-case involving a small business system. The second aspect covers the influence of a faulty knowledge base on the generated rules and on the detection.

Made available in DSpace on 2015-03-26T13:32:04Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1562008 bytes, checksum: 8448998bfce6287c1003568729e72993 (MD5) Previous issue date: 2008-08-29 / The space-time models, developed for analyses of diseases, can also be used for others field of study, including concerns about forest and deforestation. Therefore, this dissertation uses a methodology for detection of space- time clusters of cases that were mapped through the investigation of deforestation in Amazonas State. The methodology includes the location and the year that the deforestation s alert occurred. These deforestation s alerts are mapped by the DETER (Detection System of Deforestation in Real Time in Amazonia). And to find historic information about clusters which have a cylinder format, through the retrospective analyses. Also to find significant cases of deforestation. The area of study, took place the south of Amazonas State, including Boca do Acre, Labrea, Canutama, Humaita, Manicore, Novo Aripuana e Apui County. This area has showed a significant change for the land cover which has increased the number of deforestation s alerts. Therefore this situation becomes a concern and gets more investigation, trying to stop factors that increase the number of cases in the area. The outcome shows an efficient model to detect space-time clusters of deforestation s alerts. The model was efficient to detect the location, the size, the order and characteristics about activities at the end of the study. Two clusters were considered alive clusters and kept alive until the end of the study. These clusters are located in Canutama and Lábrea County. / Os modelos espaço-temporais desenvolvidos para análises essencialmente empregadas, em dados epidemiológicos humanos, têm um grande potencial para aplicação em estudos florestais e de desmatamento. Neste sentido, esta dissertação aplica uma metodologia para detecção de conglomerados espaço-temporais de casos, em dados oriundos do monitoramento do desmatamento no estado do Amazonas. Com a utilização de um modelo que a metodologia consiste em incorporar simultaneamente a localização e o ano em que supostamente ocorreu o alerta de desmatamento, divulgado pelo DETER (Sistema de Detecção do Desmatamento em Tempo Real na Amazônia) e, verificar, através da abordagem retrospectiva, a existência de conglomerados, históricos e ativos de geometria cilíndrica, de ocorrência de casos significantes, bem como ordenar e localizá-los. Foi escolhida para este estudo a região sul do estado do Amazonas, compreendendo os municípios de Boca do Acre, Lábrea, Canutama, Humaitá, Manicoré, Novo Aripuanã e Apuí. Esta região tem apresentado mudanças constantes de uso do solo, o que tem gerado grande incidência de alertas de desmatamento nos últimos anos. Portanto, esta região faz jus ao estudo de vigilância no sentido de controlar e coibir essas ações antrópicas na região. Os resultados revelaram que o modelo se mostrou eficiente para detectar conglomerados spaço-temporais de alertas de desmatamento, bem como sua localização, tamanho, ordem e características referentes à sua atividade até o final do período estudado. Dos conglomerados detectados dois foram considerados ativos (permanecem ativos até o final do período estudado). Estes conglomerados ativos estão localizados nos municípios de Canutama e Lábrea.