Flash Flood Risk and Resilience Analysis of Tanguar Haor Adjacent Areas

Bangladesh faces various types of natural hazards from its birth due to geographical location and physiographic sitting. Flood is the most common event among them. North-eastern part of Bangladesh faces flash flood almost every year with large scale of damage. Tanguar Haor, a famous ramsar site of Bangladesh located in Sunamganj district. This wetland adjacent areas are the most vulnerable zone in terms of flash flood hazard. About more than 80% people are the direct victim of this hazard. This study tries to assess the risk and resilience status of flash flood using risk and resilience assessment matrix. To accomplish this research both primary and secondary data have used. Through this work the comparative view between risk and resilience status has tried to represent. That shows the actual penetration of the depth of risk reduction policy making to improve the condition and minimize the losses of flash flood in the study area

A Topic Modelling Based Approach Towards Personalized and Health-Aware Food Recommendation

In this thesis, we present our research addressing various food-domain specific challenges. The overall aim is to produce systems that support personalized, health-aware, and context-aware Food Recommendation (FR). Chapter 2 describes a systematic literature review that identifies the core challenges in FR research and summarizes the current state-of-the-art. To support our FR research, we created two large-scale recipe corpora with 230,876 recipes and 55,314 recipes, respectively. Chapter 3 summarizes the corpus generation process and describes various properties of each corpus. In chapter 4, we describe research on identifying significant food features, which are multi-domain attributes that have an impact on peoples' eating habits or food choices. We investigated Ensemble Topic Modeling (EnsTM), a natural language processing approach, to parse large recipe corpora and extract dominant food features. Chapter 4 also describes our proposed feature-vector based data representation format for food-items. The feature-vector based format reduces the data volume and computation complexity of recommenders. Finally, the chapter discusses an intelligent, interactive, and open user-modelling technique, which is built on the identified food features. To achieve a meaningful impact on a user's eating habit FRS needs to be useful (e.g., enable personalization) in both cold-start and on going use scenarios. Both are addressed in this thesis. In chapter 6, we describe research on achieving personalization in a cold-start scenario. Taking advantage of the identified food features, the feature-vector based data representation format, and the feature-based intelligent user-modelling, we designed three novel EnsTM based recommenders. To assess their effectiveness in a cold-start scenario the EnsTM based variations were evaluated through a user study, with 48 participants, comparing these against a conventional Content Based (CB) approach. The EnsTM based recommenders performed significantly better than the CB approach. Longer-term use is addressed in chapter 7. To ensure such use FRS need strategies to progressively learn users' preferences, identify changes in eating habit, and accommodate these changes in the future FR. In chapter 7, we discuss seven unique hybrid feature and/or topic based recommenders which progressively learn user-preference from users' interactions with the system. Through an offline study we compared the proposed hybrid recommenders with seminal baselines. Six of our proposed models outperformed the baseline models. Chapter 8 reports two complementary experiments that investigate the necessity and impact of smart-nudging in FR. The first experiment investigates how knowledgeable people are on the healthiness of different commonly consumed food-items?. During a user study with 52 participants we found that people typically have very poor judgement on the nutritional contents contained within various food-items. This inspired us to investigate tools that can better convey a recipe's healthiness to users. Our proposed solution is a combination of EnsTM based food-type identification and smart-nudging techniques to promote healthier food choices among users. We proposed three visual smart-health-nudging techniques to promote healthier food options over others. We conducted a user study with 72 participants comparing recommendation scenarios with each of the three novel nudging techniques and a baseline with no nudging present. Results demonstrated that users are more likely to visit and consume healthier recipes under the influence of visual nudging content. Overall the results in this thesis demonstrate that the proposed EnsTM based recommendation approach performs better than the previous state-of-the-art, and can support FR in both a cold-start and ongoing use scenarios. It also provided evidence that these approaches can be combined with visual smart-nudge techniques to support healthier food choices.Science Foundation Irelan

Human papillomavirus infection among Bangladeshi women with cervical intraepithelial neoplasia and chronic cervicitis

Background and objectives: Cervical cancer is one of the leading causes of morbidity and mortality. Human papillomavirus (HPV) is known to be associated with cervical intraepithelial neoplasia (CIN) and cancer. The objective of the present study was to determine the rate of HPV infection among the Bangladeshi women with different grades of CIN and cancer. Methods: Women aged 20 to 55 years, diagnosed as a case of chronic cervicits, cervical intraepithelial neoplasia (CIN) or invasive cancer by Papanicolaou (Pap) smear and colposcopy directed biopsy were enrolled in the study. High and intermediate risk oncogenic HPV were detected in cervical samples by real time PCR (rt-PCR). Results: Seventy two women with chronic cervicitis and different grades of CIN were included in the study. Out of 72 cases, 28 (38.9%) and 44 (61.1%) had chronic cervicitis and CIN respectively. Overall, the HPV infection rate was 43.1% (95% CI= 32%-54%) among the study population. CIN cases had significantly high (p<0.01) HPV infection (78.6%; 95% CI=60%-89%) compared to cases with chronic cervicitis (18.2%; 95% CI=11.1%-34.5%). Women between the age of 20-30 years had the highest positive rate (50.0%) followed by 31-40 years age group (43.6%). All CIN grade 2 and 3 had HPV infection. Conclusion: The study showed that HPV was strongly associated with different grades of CIN. Specific HPV types should be determined to find out the most prevalent HPV types among the Bangladeshi women with CIN and cervical cancers. IMC J Med Sci 2016; 10(1): 29-3

Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach

The 4th International Workshop on Health Recommender Systems (HealthRecSys 2019), Copenhagen, Denmark, 20 September 2019Food choices are personal and complex and have a significant impact on our long-term health and quality of life. By helping users to make informed and satisfying decisions, Recommender Systems (RS) have the potential to support users in making healthier food choices. Intelligent users-modeling is a key challenge in achieving this potential. This paper investigates Ensemble Topic Modelling (EnsTM) based Feature Identification techniques for efficient user-modeling and recipe recommendation. It builds on findings in EnsTM to propose a reduced data representation format and a smart user-modeling strategy that makes capturing user-preference fast, efficient and interactive. This approach enables personalization, even in a cold-start scenario. We compared three EnsTM based variations through a user study with 48 participants, using a large-scale, real-world corpus of 230,876 recipes, and compare against a conventional Content Based (CB) approach. EnsTM based recommenders performed significantly better than the CB approach. Besides acknowledging multi-domain contents such as taste, demographics and cost, our proposed approach also considers user’s nutritional preference and assists them finding recipes under diverse nutritional categories. Furthermore, it provides excellent coverage and enables implicit understanding of user’s food practices. Subsequent analysis also exposed correlation between certain features and healthier lifestyle.Science Foundation IrelandInsight Research Centr

THE TOXIC TURN? CONCEPTUAL AND METHODOLOGICAL ADVANCES ON PROBLEMATIC CONTENTS ON SOCIAL MEDIA

The ‘toxic turn’ in social media platforms continues unabated. Hate speech, mis- and disinformation, misogynistic and racist speech, images, memes and videos are all far too common on social media platforms and more broadly on the internet. While the diminishing popularity of populist politicians led to hopes for less social toxicity, the Covid-19 pandemic introduced new and more complex dimensions. Tensions have emerged around what constitutes problematic content and who gets to define it. Co-regulation models, such as for example the EC Code of Conduct against Illegal Hate Speech, focus on the legality of certain types of contents, while leaving other categories of problematic contents to be defined by platforms. In parallel, the social media ecosystem became more diverse, as new platforms with hands off moderation policies attracted users who felt too constrained by the policies of mainstream platforms. The proposed panel examines this complex and dynamic landscape by problematizing what is understood as toxic, deplatformed, removable and in general problematic content on platforms with the aim to probe the boundaries of what is constituted as acceptable discourse on platforms and to map its implications. In particular, this panel discusses the broad definition of ‘problematic content’ employed by social media platforms, a catch-all term that cuts across hate speech and propaganda, including more politically topical content such as mal-, mis-, and disinformation, hyperpartisan and polarising content, but also abusive, misogynistic, racist, and homophobic discourse. The term is also employed to refer to spam and content that infringes upon the Terms of Service or the Community Standards of social media platforms. As such, it is a broad category that resists a narrower classification given the operational scope of its use. Defining what constitutes problematic content is a key operation of platform content moderation policies but is also the subject of intense debates (de Gregorio, 2020; Gillespie, 2018; Gillespie et al., 2020; Gorwa et al., 2020). The panel interrogates the many definitions and applications of problematic content on social media platforms and applications through an empirically informed lens and focusing on deleted contents, complex mixed narratives, and grey areas, including hidden misinformation on voice applications. Problematic Content according to Twitter Compliance API presents ongoing work on the Twitter Compliance API and the Compliance Firehose, which allow researchers to identify content that has been deleted, deactivated, protected, or suspended from Twitter, a proxy for problematic content. In Multi-Part Narratives on Telegram Siapera presents ongoing research that probes the intersection between Covid-19 scepticism, far right and other political narratives in vaccine hesitant groups on Telegram. The third contribution, What if Bill Gates really is evil, people? Investigating the infodemic’s grey areas discusses the conceptual and methodological definitions of problematic content in relation to work on anti-vax and other conspiratorial narratives on Instagram and on Twitter. The fourth contribution, Misinformation and other Harmful Content in Third-Party Voice Applications focuses on problematic content that is yet to be identified on voice applications such as personal assistants. The article addresses the methodological challenges of identifying and defining such contents on applications that have currently no content moderation policies. All contributions foreground the difficulties and costs of identifying and dealing with problematic contents on social media. The panel fits with theme of decolonization in two ways: firstly, because it is concerned with the tensions around how toxic/problematic contents are defined and who gets to define them; and secondly, because of its focus on neo-colonial discourses or justifications for colonialism in both narratives hosted by platforms and in platforms’ attempts to regulate content. As some narratives are flagged for removal by social platforms, they also raise the question of who is deciding and what does problematic content mean, with far right discourses exploiting this tension and ironically denouncing any attempt to regulate the public discourse as ideological enforcement and justification for (neo)colonial practices performed by social media platforms. From this perspective, platforms' own claims about what constitutes acceptable content is uncomfortably close to colonial narratives of civilised discourse and brings to the fore the potential for neo-colonial narratives and practices in digital spaces. References De Gregorio, G. (2020). Democratising online content moderation: A constitutional framework. Computer Law & Security Review, 36, 105374. Gillespie, T. (2018). Custodians of the Internet. Yale University Press. Gillespie, T., Aufderheide, P., Carmi, E., Gerrard, Y., Gorwa, R., Matamoros-Fernández, A., ... & West, S. M. (2020). Expanding the debate about content moderation: Scholarly research agendas for the coming policy debates. Internet Policy Review, 9(4), Article-number. Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 2053951719897945