College of Medicine Feedback from MSV & LCME

Notes, emails, and documents containing feedback on the LCME mock site visit.

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

QUALIDADE DE VIDA – UM ESTUDO REALIZADO COM DIRETORES DE ESCOLAS PÚBLICAS

A estrutura política e econômica brasileira promove uma sociedade marcada por desigualdades sociais, gerando indignações e diversos conflitos. Estresse, ansiedade, depressão, mal estar profissional, infraestrutura precária, alimentação inadequada, sedentarismo, (i)mobilidade urbana, fragilidade dos vínculos sociais, poluição, dentre outros, são fatores contemporâneos que afetam a qualidade de vida dos seres humanos. Este cenário merece atenção peculiar quando nos remetemos ao ambiente escolar. Este estudo teve por objetivo avaliar a qualidade de vida bem como identificar o grau de estresse percebido em diretores de Escolas Municipais de Educação Infantil (EMEIs) na Cidade de São Paulo. Participaram do estudo 86 Diretores de Escolas, correspondendo a 16,04% do total de diretores de EMEIs da Rede Municipal de Educação (RME). Os instrumentos utilizados foram: Questionário sociodemográfico, Instrumento de Avaliação de Qualidade de Vida-abreviado - WHOQOL-bref e a Escala de Estresse Percebido – PSS. Os resultados revelaram que, em média, 70,9% possuem uma excessiva rotina de trabalho, caracterizadas por: chegar mais cedo e/ou sair mais tarde do expediente normal; receber e/ou fazer ligações, mensagens, e-mails ou similares, relacionados à direção, fora do expediente de trabalho e levar serviços para casa e/ou se preocupar com questões relativas à direção, após encerrar o expediente. A maioria (60,05%) acredita que as condições de trabalho, enquanto Diretor de Escola influenciam negativamente na saúde pessoal. Tanto o índice geral da Qualidade de Vida quanto em relação aos domínios do WHOQOL-bref mostraram médias significativamente abaixo dos dados normativos brasileiros 12,7±3,1 (p<0,001). Quanto ao nível de estresse percebido, inicialmente analisamos as frequências referentes aos respectivos níveis. Os resultados mostraram que o nível de estresse percebido se situa entre 48,8% de “às vezes” para 41,9% de “quase sempre”. Este resultado se apresenta estatisticamente significativo (χ2 p<0,05). Com base neste estudo pudemos observar a escassez de estudos sobre QV e estresse com Diretores de Escolas e que a QV se apresentou significativamente baixa, bem como a percepção de estresse em quase metade da amostra estudada.

Spam campaign detection, analysis, and formalization

Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.

Active Data Collection Techniques to Understand Online Scammers and Cybercriminals

Nigerian scam, also known as advance fee fraud or 419 scam, is a prevalent form of online fraudulent activity that causes financial loss to individuals and businesses. Nigerian scam has evolved from simple non-targeted email messages to more sophisticated scams targeted at users of classifieds, dating and other websites. Even though such scams are observed and reported by users frequently, the community’s understanding of Nigerian scams is limited since the scammers operate “underground”. To better understand the underground Nigerian scam ecosystem and seek effective methods to deter Nigerian scam and cybercrime in general, we conduct a series of active and passive measurement studies. Relying upon the analysis and insight gained from the measurement studies, we make four contributions: (1) we analyze the taxonomy of Nigerian scam and derive long-term trends in scams; (2) we provide an insight on Nigerian scam and cybercrime ecosystems and their underground operation; (3) we propose a payment intervention as a potential deterrent to cybercrime operation in general and evaluate its effectiveness; and (4) we offer active and passive measurement tools and techniques that enable in-depth analysis of cybercrime ecosystems and deterrence on them. We first created and analyze a repository of more than two hundred thousand user-reported scam emails, stretching from 2006 to 2014, from four major scam reporting websites. We select ten most commonly observed scam categories and tag 2,000 scam emails randomly selected from our repository. Based upon the manually tagged dataset, we train a machine learning classifier and cluster all scam emails in the repository. From the clustering result, we find a strong and sustained upward trend for targeted scams and downward trend for non-targeted scams. We then focus on two types of targeted scams: sales scams and rental scams targeted users on Craigslist. We built an automated scam data collection system and gathered large-scale sales scam emails. Using the system we posted honeypot ads on Craigslist and conversed automatically with the scammers. Through the email conversation, the system obtained additional confirmation of likely scam activities and collected additional information such as IP addresses and shipping addresses. Our analysis revealed that around 10 groups were responsible for nearly half of the over 13,000 total scam attempts we received. These groups used IP addresses and shipping addresses in both Nigeria and the U.S. We also crawled rental ads on Craigslist, identified rental scam ads amongst the large number of benign ads and conversed with the potential scammers. Through in-depth analysis of the rental scams, we found seven major scam campaigns employing various operations and monetization methods. We also found that unlike sales scammers, most rental scammers were in the U.S. The large-scale scam data and in-depth analysis provide useful insights on how to design effective deterrence techniques against cybercrime in general. We study underground DDoS-for-hire services, also known as booters, and measure the effectiveness of undermining a payment system of DDoS Services. Our analysis shows that the payment intervention can have the desired effect of limiting cybercriminals’ ability and increasing the risk of accepting payments.

“Enjoy your baby” Internet-based CBT for mothers with babies: a feasibility randomised control trial

Background: Postnatal depression is a global health problem with lasting effects on the family. Government policy is focussed on early intervention and increasing access to psychological therapies. There is a growing evidence base for the use of computerised CBT packages and this study investigated the feasibility of a CBT-based self-help internet intervention for new mothers. Objective: To assess the ability to recruit mothers, deliver an internet course, obtain follow-up data and evaluate what mothers think of the course. Design: A feasibility randomised control design was used to compare a waiting list control group (delayed access= DA) to the Enjoy Your Baby course (immediate access= IA). Measures were administered at baseline and 8 week follow-up. Methods: Adverts were placed in the Metro freesheet, on charity web pages, on social media, posters were put up in the community, and leaflets were handed out at mother and baby groups. Participants had to be 18 years old or over with a child less than 18 months old. The IA arm was given access to the course straight away. After 8 weeks all participants were asked to recomplete the original measures and those in the IA arm also gave feedback on the course. Participants in the DA arm were given access after recompleting the questionnaires. Due to a lack of follow-up data a small discussion group was conducted. Intervention: The course contains 4 core modules including helping mothers understand why they feel the way they do and helping them build closeness to their babies. Additional modules, worksheets and homework tasks were available. The DA group were given a list of additional support resources and services, and encouraged to seek additional help if required. All participants received weekly automated emails for 12 weeks as they worked through the course. It was not possible to deliver individualised support. 34 Results: Despite using a number of recruitment strategies, recruitment was lower and slower than anticipated, and attrition was high. 41 women, primarily recruited via the internet, were randomised (IA n=21, DA n=20). No significant differences were observed between participants in either arm at baseline and no statistically significant differences were identified when the demographics and baseline measures of participants who logged-on to the course were compared to those who did not, or when participants who completed follow-up measures were compared to those who did not. Pre and post intervention scores on the EPDS approached statistical significance (P=.059, r=.444) favouring the intervention arm. The discussion group suggested strengths of the course and recommended areas for improvement, including making the course more mobile friendly. Conclusion: Internet interventions show promise; however it is difficult to recruit mothers, engagement is low and attrition high. A number of recommendations are made and a further pilot or an internal pilot of a larger substantive study should be conducted to confirm recruitment and retention. Trial ID: ISRCTN90927910.

Weight loss and attrition in overweight and obese young women during a 36- week internet-based lifestyle intervention

Background: Young women are at high risk of weight gain yet few studies have examined the long-term effectiveness of weight loss programs in this group. This study aimed to investigate the effects of a self-directed internet-based lifestyle program on body weight in young women.

Methods: Overweight or obese young women (BMI 33.4 ± 0.3 kg/m2, age 27.8 ± 0.3 years) were initially randomized to General lifestyle advice (G) or Structured lifestyle advice (S) via in-person and website support for 12 weeks (Phase I). After Phase I, all participants were supported through a self-directed internet-based program for 36 weeks (Phase II). The internet-based program included a structured hypocaloric diet, physical activity program, self-monitoring tools, peer group forum and monthly emails. Body weight, energy intake and physical activity were measured at week 0, week 12, week 24 and week 48. Adherence to self-regulatory behaviors was measured at week 48. Mixed model analyses were conducted to determine changes in body weight, energy intake and physical activity.

Results: A total of 203 overweight or obese young women commenced Phase I and 130 commenced Phase II. In Phase I, S group had significantly greater weight loss than G group (4.2 ± 0.6 kg vs 0.6 ± 0.3 kg, P<0.001). In Phase II, both groups had significant weight loss over time without significant group differences (-0.8 ± 1.1kg vs -0.8 ± 0.6, P>0.05). Forty-one percent (53/130) of the participants who commenced Phase II completed the internet-based intervention. Dropouts had a higher baseline BMI, were more likely to be married or in a de facto relationship, and more likely to have at least one child.

Conclusions: A self-directed internet-based program could be effective in providing support in maintaining weight loss on a structured lifestyle program in young women over 36 weeks. Further research is required to maintain engagement in young women who were married/in a de facto relationship or have children.

Database of Australian doctorates

Lists PhD theses created by Australian researchers within the period 1948-2006. Does not include professional doctorates.

Multilayer hybrid strategy for phishing email zero-day filtering

The cyber security threats from phishing emails have been growing buoyed by the capacity of their distributors to fine-tune their trickery and defeat previously known filtering techniques. The detection of novel phishing emails that had not appeared previously, also known as zero-day phishing emails, remains a particular challenge. This paper proposes a multilayer hybrid strategy (MHS) for zero-day filtering of phishing emails that appear during a separate time span by using training data collected previously during another time span. This strategy creates a large ensemble of classifiers and then applies a novel method for pruning the ensemble. The majority of known pruning algorithms belong to the following three categories: ranking based, clustering based, and optimization-based pruning. This paper introduces and investigates a multilayer hybrid pruning. Its application in MHS combines all three approaches in one scheme: ranking, clustering, and optimization. Furthermore, we carry out thorough empirical study of the performance of the MHS for the filtering of phishing emails. Our empirical study compares the performance of MHS strategy with other machine learning classifiers. The results of our empirical study demonstrate that MHS achieved the best outcomes and multilayer hybrid pruning performed better than other pruning techniques.

Potencial de novas cultivares e seleções de uvas de mesa na Serra Gaúcha.

O desenvolvimento de novas cultivares pode ser considerado como um dos fatores que têm impulsionado o crescimento do mercado de uvas de mesa no Brasil. O objetivo deste trabalho é avaliar o potencial de cultivo de cinco novas cultivares e três seleções de uvas de mesa na Serra Gaúcha. Estão sendo avaliadas a fenologia, a produção, a qualidade e o conteúdo de compostos relacionados à saúde (CRS) de genótipos de uvas com e sem sementes, produzidas na Embrapa Uva e Vinho. A aceitação das uvas foi avaliada por grupos de 32 a 63 consumidores, que atribuíram notas de 1 (ruim) a 9 (excelente) para sete características visuais e oito relacionadas ao sabor. Os resultados da avaliação sensorial e da qualidade das uvas foram submetidos à Análise de Componentes Principais (ACP).