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Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
my report in Com 311: Seminar in Cross-Cultural Research at the College of Mass Communication, University of the Philippines Diliman - PhD Media Studies program
This study investigated the reciprocal relationships between social network characteristics, social support, and mental health among older adults in the United States. The study found reciprocal associations between social support and depressive symptoms, as well as between social support and certain measures of social network structure. While social support was protective of depression, depression could undermine received support over time. The strongest link between social networks and depression was indirect, through levels of social support. Future research should focus on social support as an important pathway through which social networks impact mental health.
This document provides an overview of organizational communication. It defines communication and describes effective communication and the communication process. It discusses three common theories of communication - electronic, social, and rhetorical. It also outlines different types of communication like downward, upward, horizontal, interpersonal, and computer-aided. The document then focuses on organizational communication and describes formal small group networks, the grapevine, and barriers to communication like filtering, selective perception, and cultural differences. It provides guidelines for overcoming barriers and discusses gender, cultural and politically correct communication.
A survey-based study 109 respondents – dialogue participants excluding facilitators and conveners – from 17 oblasts of Ukraine took part in the survey and provided information on 157 dialogues conducted by 66 different organisations during the period 2014-2018. The goal of the study was to obtain quantitative data in order to test hypotheses about the patterns and risks of track-three dialogues in Ukraine. The survey results have confirmed hypotheses as to four patterns and partly confirmed hypotheses as to the remaining two patterns. This research was carried out and published within the framework of the project Building a Community of Dialogue, which is funded by the British Embassy in Ukraine. The project is implemented by Peaceful Change initiative in partnership with the Institute for Peace and Common Ground, and the Donbass Dialogue initiative. The views expressed in this publication are those of the author(s) and may not coincide with the official position of the UK government.
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This is an inclusivity training for conservation scientists and practitioners. The goal is gender mainstreaming research methods and programmatic outputs. It was presented on December 8, 2021, for the International Congress for Conservation Biology's annual meeting.
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Engaging young advisors in creating strategies for increasing safety at the i...BASPCAN
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COMM5600 Interviews & Focus groups TO SHARE (1).pptRashiRashi21
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This document outlines a research study exploring intimacy in polyamorous relationships through participatory design. The researchers conducted interviews and affinity diagramming to identify key areas of communication, privacy, jealousy, and scheduling. They then held a participatory design workshop using a new "body prototyping" method, where participants designed communication technologies by prototyping concepts on their bodies. The goal was to empower the polyamorous community to design solutions for themselves through a future-focused scenario and collaborative process.
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1) Learners developed an identity and status within the community through helpful posts, responses to others' questions, and receiving votes from peers. The voting system encouraged participation and distinguished valuable contributions.
2) Newcomers were able to move from the periphery to the core of the community through active participation, learning from others, and demonstrating willingness to help peers. Older community members also helped by encouraging others and providing support.
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1. Experimental studies of reflective scaffolding tools in college classroom discussions, which found the tools improved use of deliberative skills like perspective taking.
2. A facilitator dashboard for visualizing dialogue quality and providing advice. It analyzes text using linguistic and discourse features and demographics.
3. Automated text classification of deliberative skills using machine learning, finding moderate success identifying skills from linguistic features of discussion text. Further analysis across domains is planned.
This report summarizes research from a two-year study on cyber violence and cyber sexual violence experienced by girls and young women. 288 surveys and 6 focus groups were conducted across Toronto. Key findings include: over 80% of respondents felt safe online, though many felt harassment was common; the most unsafe sites reported were Omegle and Tinder; most would report incidents to parents or police; and over 70% felt low confidence or appearance increased vulnerability. Recommendations are to address gender attitudes, treat young women as experts, and improve prevention with schools and online platforms. Limitations included lack of diversity in participants.
Term Paper on Changing Trend Of Marriage : A Sociological Research Based on D...Hibblu
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GENDER AND COMMUNICATION RESEARCH PRESENTATION.pptxrafikthomson5
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1. A study in the UAE found gender differences in communication styles at work, such as men using direct language while women prefer polite tones.
2. A study of football media coverage found semantic and syntactic differences between reporting on men's and women's games, focusing more on women's personal lives and using gendered language.
3. A Turkish study interviewed managers and found both genders prioritize salaries but female managers faced difficulties saying no and lacked self-confidence in leadership roles.
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This document explores different communication styles on Twitter by analyzing a sample of tweets. It identifies six potential styles: Advocator, Jester, Spokesperson, Provocateur, and Boundary Spanner. For each style, it provides a brief description based on the analysis of tweets, including the functions they serve, argumentation styles used, targets, and other attributes. It notes that an individual may exhibit different styles depending in the context and role, and that this is a first attempt at a taxonomy of communication styles on Twitter.
SAMPLE ANSWERPublic Attributions for Poverty in Canada – Reutt.docxrtodd599
SAMPLE ANSWER
Public Attributions for Poverty in Canada – Reutter et al (2006)
1. What is the main argument presented by the author?
Surveys and interviews reveal that respondents are most likely to attribute poverty to structural factors (such as lack of education, low wages, discrimination, and lack of social safety net benefits) and least likely to favour individualistic attributions (such as laziness). The education and income levels of the respondents were the most consistent predictors of types of attributions.
2. What is the author’s research method? (i.e. how does he/she collect the data?) What are the size and characteristics of the sample?
Method: phase one involved interviews and phase two involved telephone surveys.
Sample size: 119 interviews; 1671 surveys (839 in Edmonton; 832 in Toronto).
Sample characteristics: These surveys and interviews were conducted in Toronto and Edmonton. These cities have approximately the same rate of poverty (16%). Equal numbers of participants were chosen in each neighbourhood. Table 1 lists the characteristics of the survey sample in some detail (7).
59 low income and 60 higher income people were interviewed. 2/3 of respondents were women, 30 – 54 years old. The low-income participants were younger. Low-income people were more likely to have high-school education or less. In the sample of 34 low-income people in the six group interviews, 67.6% were female and 60.6% had a high-school education or less. Almost half were 30-44 years of age. The main sources of income were welfare and employment. (8)
3. List two pieces of data/information that the author uses to support his/her argument that is drawn from their research . Explain how the evidence is related to the main idea.
a) “I think that lack of education is probably one of the biggest factors. If you're not educated then you don't get the jobs that provide you with an income that you can live on (female, higher-income participant)” (12).
Explanation of relationship to the main idea: this quote supports the theme related to education – respondents indicated that they see education as a major structural factor related to poverty.
b) “I think most people who are living on a low income, many of them work just as hard as people who are making a high income. It's just for some reason their job does not pay them an adequate wage . . . they're unfortunate enough to be in a job that only pays eight bucks an hour (male, higher-income participant)” (10).
Explanation of the relationship to the main idea: this quote supports the theme related to the impact of structural factors such as low wages, inadequate social safety net or discrimination on causes of poverty.
4. Is the information provided verifiable and well-researched? How do you know? List the factors that you used to make your evaluation.
The authors provide a verifiable paper based on the following:
· The authors all work at one of the following universities: University .
The document discusses user research conducted to understand how data from Wikimedia projects can better support diversity efforts.
Key findings include developing 6 personas representing potential users, including diversity-focused editors, community leaders, developers, and researchers. Interviews revealed challenges around technical accessibility of tools, a need for customization and localization, and tracking underrepresented groups over time.
The research aims to provide actionable insights to inform diversity through an integrated tool called "humaniki" that merges existing projects. Overall, the research identified improving usability of existing data and expanding analysis dimensions as important themes to address gaps.
This document provides definitions and concepts related to gender analysis. It defines key terms like sex, gender, equality and equity. It discusses different frameworks for gender analysis like Women in Development (WID), Gender and Development (GAD), and Gender Mainstreaming. The document outlines the purpose and stages of conducting a gender analysis. It also discusses tools and questions to consider in a gender analysis and tips for implementation.
The current thesis’s scope is within the Natural Language Understanding sub-field of Natural Language Processing. From the multiple possible tasks from this domain, we stopped at Discourse Analysis. We analyzed the main approaches existent in this field and identified the flaws of each of the presented approaches. Starting from them, we proposed an adaptation of an existing framework (the Polyphonic framework) using ideas derived from the theory of a known linguist (Tannen) regarding the importance of repetitions in discourse. After presenting our adaptation, we showed how it would solve most of the indicated problems with the other approaches. In order to verify the effectiveness of the adapted framework, we presented a couple of developed applications that are meant to demonstrate its utility for discourse visualizations, for the identification and classification of the important moments of a discourse, for the assessment of chat conversations based on repetition and rhythmicity, for malapropism detection and correction, and for text recovery
- The document describes using time series analysis models like ARIMA to forecast daily sales quantities of products like paintings for an online retailer.
- The best model was found to be an ARIMA(7,0,2) model, which uses the previous 7 days' values to predict future values without differencing the data.
- This model provided more accurate predictions than the Facebook Prophet model based on error metrics, while converging during both training and testing.
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SAMPLE ANSWERPublic Attributions for Poverty in Canada – Reutt.docxrtodd599
SAMPLE ANSWER
Public Attributions for Poverty in Canada – Reutter et al (2006)
1. What is the main argument presented by the author?
Surveys and interviews reveal that respondents are most likely to attribute poverty to structural factors (such as lack of education, low wages, discrimination, and lack of social safety net benefits) and least likely to favour individualistic attributions (such as laziness). The education and income levels of the respondents were the most consistent predictors of types of attributions.
2. What is the author’s research method? (i.e. how does he/she collect the data?) What are the size and characteristics of the sample?
Method: phase one involved interviews and phase two involved telephone surveys.
Sample size: 119 interviews; 1671 surveys (839 in Edmonton; 832 in Toronto).
Sample characteristics: These surveys and interviews were conducted in Toronto and Edmonton. These cities have approximately the same rate of poverty (16%). Equal numbers of participants were chosen in each neighbourhood. Table 1 lists the characteristics of the survey sample in some detail (7).
59 low income and 60 higher income people were interviewed. 2/3 of respondents were women, 30 – 54 years old. The low-income participants were younger. Low-income people were more likely to have high-school education or less. In the sample of 34 low-income people in the six group interviews, 67.6% were female and 60.6% had a high-school education or less. Almost half were 30-44 years of age. The main sources of income were welfare and employment. (8)
3. List two pieces of data/information that the author uses to support his/her argument that is drawn from their research . Explain how the evidence is related to the main idea.
a) “I think that lack of education is probably one of the biggest factors. If you're not educated then you don't get the jobs that provide you with an income that you can live on (female, higher-income participant)” (12).
Explanation of relationship to the main idea: this quote supports the theme related to education – respondents indicated that they see education as a major structural factor related to poverty.
b) “I think most people who are living on a low income, many of them work just as hard as people who are making a high income. It's just for some reason their job does not pay them an adequate wage . . . they're unfortunate enough to be in a job that only pays eight bucks an hour (male, higher-income participant)” (10).
Explanation of the relationship to the main idea: this quote supports the theme related to the impact of structural factors such as low wages, inadequate social safety net or discrimination on causes of poverty.
4. Is the information provided verifiable and well-researched? How do you know? List the factors that you used to make your evaluation.
The authors provide a verifiable paper based on the following:
· The authors all work at one of the following universities: University .
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The document describes improvements made to an existing application used to identify important moments in student collaborative chats. The improvements include: 1) Implementing a redirection system to analyze utterance timestamps to identify intense discussion periods, 2) Overlapping graphics to correlate concepts with disputed chat parts to identify more important concepts, 3) Increasing availability by creating a web application and avoiding user intervention for moment detection. The improved application can better identify important moments by considering both concept distribution and dialogue intensity over time.
The document summarizes a research paper on developing digital services to emphasize pollution phenomena using statistics and time series analysis. The paper presented at the 8th International Conference on Exploring Services Science discusses how it extracts concepts related to pollution from literature, analyzes frequency of concepts over time, and identifies peaks that correspond to pollution events. It finds that awareness of pollution threats increased in the late 1960s and presents limitations such as delays in reporting events and difficulty identifying all factors influencing time series. The methodology could be improved by better distinguishing yearly events and developing predictive models.
These slides present an application for identifying English words whose use is cyclic or regularly varies in time. The purpose of the developed application was to build a cross-platform system for indexing and analyzing the graphs of words usage over time. For words indexing, we used the data provided by the Google Books N-grams Corpus, which was afterwards filtered using the WordNet lexical database. For identifying the cyclic or regularly varying words, we used two different algorithms: autocorrelation and dynamic time warping. The results of the analysis can be visualized using a web interface. The application also offers the possibility to view the evolution of the use frequency of different words in time.
These slides present an application designed to analyze news articles from Romanian mass media and extract opinions about political entities relevant to the major political stage. The application was created with the desire to study media polarization around important political events, such as legislative or presidential elections. The application uses different crawlers to extract the data from online newspapers and save it in the database. Then, it uses several Machine Learning techniques for identifying and classifying opinions about given entities over a long span of time. Based on this classification, it generates reports and charts that could be use not only to study political polarization, but also to identify partisan media
Language is a living corpus, words tending to be created, or to disappear over time. Even the degree of certain words' usage tends to fluctuate due to historical events, cultural movements or scientific discoveries. The changes from the lan-guage are reflected in the written texts and thus, by tracking them one can deter-mine the moment when these texts were written. In this paper, we present an ap-plication that uses time series analysis built on top of the Google Books N-gram corpus to determine the time period during which a text was written. The applica-tion is based on words' fingerprinting to find the time interval when they were most probable used and on word' importance for the given text. Combining the fingerprints for all the text's words according to their importance allows the time stamping of that text.
These slides address the issue of predicting the reselling price of cars based on ads extracted from popular websites for reselling cars. To obtain the most accurate predictions, we have used two machine learning algorithms (multiple linear regression and random forest) to build multiple models to reflect the importance of different combinations of features in the final price of the cars. The predictions are generated based on the models trained on the ads extracted from such sites. The developed system provides the user with an interface that allows navigation through ads to assess the fairness of prices compared to the predicted ones
These slides address the problem of capturing, processing and analyzing images from the video stream of the Hearthstone game in order to obtain relevant information on the conduct of parties in this game. Since the information needs to be presented to the user in real-time, we needed to find the most suitable methods of extracting this information. Therefore, techniques such as background subtraction, histograms comparisons, key points matching, optical character recognition were investigated. Driven by the required processing speed, we ended up using optical character recognition on limited areas of interest from the captured image. After developing the application, we tested it in real-world context, while real games were played and presented the obtained results. In the end, we also provided two examples where the application would prove useful for better decision making during the game.
These slides present Movie Recommender, a system which provides movie recommendations based on the information known about the users. These recommendations are done using the analysis of the users' psychological profile, their watching history and the movies scores from other websites. They are based on aggregate similarity calculation. The system uses both collaborative filtering and content filtering (using an approach based on different features of the movies from the database). Although there are similar applications available, they tend to ignore the data specific to the user, which in our opinion is essential for his/her behavior
Language suffers an everlasting process of change, both at a semantic level, where existing words acquire new meanings, and at a lexical level, where new concepts appear and old ones disappear or are used less frequently. New words (terms/concepts) may be added as a result of scientific discoveries or socio-cultural influences, while other words are ”forgotten” or are assigned alternative meanings. These changes in a vocabulary usually characterize important shifts in the environment or
the domain they are used in. For experts there is an evident connection between a new concept and some of the existing ones, but for regular people these relations remain hidden and need to be identified. In particular, in the medical domain new terms appear as a result of new discoveries and it becomes an important challenge to establish the connections between different concepts. Moreover, it is important to detect if such a relation even exists. In this paper, we present a graph-based approach to identify the semantic path (which is a chain of semantically related words) between the concepts that appeared in the bio-medicine publications available in the PubMed corpus over a time period of 20 years
Public data can be considered large and important sources of data that can be used for different purposes. In this paper we present a method for collecting and analyzing data within urban settlements. For more focused analysis and gathering of large amount of data we considered a case study of Bucharest. The main purpose of this analysis is to pick up important information about different streets, points of interests, details about urban planning, etc., with the goal of facilitating a quick and correct evaluation of specific areas and identifying suitable location for adding new points of interest. The prediction of suitable location involves using heuristics and data mining technics such as clustering algorithms, association rules
These slides present an application for identifying archaisms and neologisms in texts. The application also provides the ability to view graphically the evolution trends of these words for a better interpretation of the results. The presented solution consists of two phases: the learning phase in which we identify the general evolution trends of three categories of words (archaisms, neologisms and common words) and the classification phase in which we label new words with their corresponding category. For both phases, the application requires Internet access because it is using the Google Books N-gram Viewer to generate the images that back up the decisions
These slides present an automatic system used for the evaluation of Bachelor and Master thesis of Computer Science students. In order to be able to fulfill this task, we have used text complexity measures along with other factors to evaluate the students' thesis. Text complexity has been mainly used to predict the grade level for which a specific reading passage or text should be assigned to. Also, it has been used in evaluating students' writings in language classes. We have decided to try to use text complexity measures for evaluating students' graduation thesis. The main challenges of this task are to select the best features that accurately reflect student's performance in a specific domain, and to identify the optimal classifier to predict the student's score. Firstly, we investigated four sets of text complexity measures (lexical, syntactic, semantic, and character measures), cohesion metrics and a couple of features related to the thesis organization and to the references and bibliography. Secondly, we computed the correlation between the proposed features and we excluded the highly inter-correlated ones. After that, we used several classifiers to predict the students' grade levels and to compare their performances. Finally, we tested our work on a corpus of Bachelor and Master thesis from the students of the Computer Science Department of the University Politehnica of Bucharest that were written in English (as for English there is a high availability of open-source tools for natural language processing). We evaluated the quality of the presented application using Pearson's Rank Correlation to compare our results with the students' grades assigned by the evaluation committee for their thesis
Every country has its own topics of interest and its hot topics at different moments in time. In this paper we present a system that helps to understand and compare different countries, starting from the topics that are debated between their members. In order to do that, we recorded and analyzed the content of the messages that are sent on Twitter by people living in several countries, hoping that this way we will be able to capture the topics of interest for each culture and predict their hot topics. We did our analysis on English written tweets only, based on the fact that English has become a global language, being spoken even by Internet users from non-English speaking countries when they want to share their thoughts and have a global audience for their messages. Our study is trying to capture the topic models both for the tweets and for the URLs shared in them. Then we compare the distribution of topics across different countries both for the tweets and for the URLs to check how consistent these models are. For the topic modelling task, we designed a specialized way of developing them that is adapted for tweets (which have a maximum of 140 characters, being too short to apply classical topic modelling methods). Our system has been tested on a corpus consisting on English tweets, collected using the Twitter streaming API, that have a location attached to them and that also contain an URL. In order to eliminate our bias, we extracted tweets without any restrictions (including tweets written in other languages, tweets without URLs, tweets without location attached) and then we checked the percentage of our targeted tweets for each country. As a consequence, we extended the period of collecting the tweets to decrease the risk of dealing with abnormal events occurring in a certain country
These slides present a text segmentation system based on the sentiments expressed in the text. The system takes as input plain text (product review for instance) and uses two different resources for tagging the sentiment words: a sentiment words dictionary and SentiWordNet. Once the sentiment words are identified, the initial text is annotated with segmentation markers when polarity shifts. The system also outputs the counts of positive and negative sentiment words found in text and optionally annotates them with their valence
In these slides we present a model that was intended to discriminate creative from non-creative news articles. In order to build the classifier, we have combined nine different measures using a stepwise logistic regression model. The obtained model was tested in two experiments: the first one tried to discriminate between news articles about the US 2012 Elections from different newspapers versus articles taken from The Onion (a website providing satiric news) on the same subject, while the second one evaluated the capacity of the model to generalize over different topics and text genres. The experiments showed that the system achieves 80% accuracy, but the lack of true positives from the second experiment raised the question of whether we really identified creativity or in fact we detected satire (as the assumption for the training corpus was that the satiric news from The Onion were also creative).
The document presents a methodology for automatically assessing participants in chat conversations used for computer-supported collaborative learning (CSCL). It uses natural language processing techniques and heuristics to evaluate conversations based on participants' involvement, knowledge, and innovation. The heuristics were tested on a corpus of 7 chat conversations involving 35 students discussing web collaboration technologies. Correlations between the heuristic evaluations and expert human evaluations were generally high, particularly for involvement and innovation. The knowledge heuristic was less reliable. The methodology can help identify effective participation criteria and rank learners and conversations.
In this poster paper we propose a new method for identifying creativity that is based on analyzing a corpus of chat conversations on the same topic and extracting the new ideas expressed by participants. The application is a first step in supporting creativity in online group discussions by highlighting the novel concepts present in conversations (new ideas) and also by identifying topics that could have become important, if not forgotten during the debates (lost ideas)
Because of the ubiquity of metaphors in language, metaphor processing is a very important task in the field of natural language processing. The first step towards metaphor processing, and probably the most difficult one, is metaphor detection. In the first part of this paper, we review the theoretical background for metaphors and the models and implementations that have been proposed for their detection. We then build corpora for detecting three types of metaphors: IS-A metaphors, metaphors formed with the preposition ‘of’ and metaphors formed with a verb. For the first two tasks, we train supervised classifiers using semantic features. For the third task, we use features commonly used in text categorization
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
3. Introduction (I)
• In computer science, the number of females enlisted in undergraduate
and graduate studies is far less than that of males (Gharibyan &
Gunsaulus, 2006)
• We have tried to identify the influence of the gender distribution for the
individual and group outcomes in CSCL tasks involving online discussions.
• We used chat conversations as the chat is the main technological
substrate for collaborative knowledge building in small groups (Stahl,
2006)
• The composition of groups and their diversity have an important impact
on the collaboration accomplishment (Chidambaram & Carte, 2005)
• We focused on a single feature characterizing diversity in CSCL groups, the
fact if the participant is a male or a female.
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
4. Introduction (II)
• Hypotheses:
– males and females have different linguistic behaviors when
talking in interaction and
– the majority of one or the other category (or even the total
absence of one) in the CSCL teams has important influences on
the collaborative process and thus on the outcomes of the chat.
• Analysis done using the Polyphonic Framework based on
discourse modeling in the case of online chats with
multiple participants (Trausan-Matu & Rebedea, 2010)
• We have used a set of quantitative measures (Chiru et al.,
2011) to measure knowledge, innovation, involvement and
vocabulary for each participant and overall for each group.
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
5. Application’s purpose
• To determine a model for the way different
participants to CSCL conversations act and
‘talk’ in different conditions.
– identify if there are major differences between
the participation of male and female
– evaluate if the gender composition of the
discussion groups may influence their outcomes
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
6. Learning Scenario and Chat Corpus
• Chat conversations created by senior year undergraduate students
involved in a Human-Computer-Interaction (HCI) class.
• Students were asked to debate about different web-collaboration
technologies (forums, blogs, chats, wikis, wave, etc.), highlighting the
weaknesses and strengths of the existing tools and eventually devising a
way to combine them in order to obtain an instrument that would be
useful for sharing information and collaboration in a company
• Each student studied individually the given technologies and choose one
of them to support it in front of the other chat participants.
• Main purpose of these conversations was to ease the learning process
about the considered platforms.
• The corpus consisted of 21 different chat conversation, each of them
ranging from 158 to 579 replies, for a total of 7346 utterances.
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
7. Participant Analysis
• 114 students: 74 males (5 of them participated in 2 chats) and 40 females (2 of
them participated in 2 chats)
• 21 teams consisting of 4 to 11 participants, each group delivering one of the chat
conversations: smaller teams of 4 (6 teams) or 5 (7 teams) students and larger
groups: 2 teams of 6, 7 and 8 participants, and 1 team of 9 and 11 participants.
• They could chose nicknames that could be used to identify them for grading -
some of them preferred to choose the anonymity
– Only 17 out of 42 females provided their full names;
– For males, 51 out of the 79 nicknames contained the participant’s full name
– 4 participants whose sex could not be identified manually investigated by human experts
and classified as males (Freaky-wiki/Wikilie, ThirdUser, Me2, BRIO)
• Considering group formation: most partially anonymised participants were found
in small teams:
– 8 of the 13 teams of 4 or 5 participants consisted only on fully or partially anonymised
participants (37 of the 55 anonymised participants), 4 of them had 2 partially anonymised
participants the last one had no anonymised participants.
– the larger teams had at most 2 partially anonymised participants.
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
8. Heuristics used for participants’
evaluation
• We considered several heuristics that were previously suggested by
Chiru et al. (2011):
– Number of replies;
– Activity showing how complex one’s replies are;
– Absence from the conversation of a participant;
– Persistence of the user in the conversation (computed as the number
of consecutive replies issued by the participant);
– Repetition of other participants’ concepts;
– Usefulness of the participant in the conversation stating how much
the other participants benefited from this user’s replies;
– On topic assessing the seriousness of the participants.
• + another 2 heuristics:
– Participant’s innovation, expressing the number of concepts
introduced in the conversation by each participant;
– Participant’s knowledge, expressing the number of concepts
introduced in the conversation by each participant that are
semantically connected with the ones imposed for debating.
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
9. Analysis and Results
• Five different scenarios:
– conversations with only male participants,
– conversations with fewer females than males,
– conversations with equal number of males and females,
– conversations where the majority of participants were
females and
– conversations between females only.
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
Distribution
Males
Only
Majority Males
Equal
share
Majority
Females
Females
Only
Total
No. Chats 2 12 3 4 0 21
Girls 0
1+2+1+1+1+1+2+2+2+
1+1+2 = 17
2+3+4=9
3+3+7+3
=16
0 42
Boys
8+5=1
3
3+3+4+4+3+6+7+4+5+
3+3+3 = 48
2+3+4=9
2+2+4+1
= 9
0 79
18.06.2013
10. Analysis
• The user has the possibility to choose one of the 5 classes or the overall
statistics and several diagrams are presented: involvement (average value
of the scores received for number of replies, activity, absence, persistence,
repetition), innovation (usefulness), knowledge (usefulness + on topic),
seriousness (on topic) the percent of the vocabulary that was common and
the percent of the vocabulary that is on topic.
Identifying Gender Differences in CSCL Chat Conversation CSCL 201318.06.2013
11. Situation Analysis (I)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• Compare the activity of the same gender participants in different
scenarios:
• Females:
– Involvement: involved more in the cases where they did not represent
the participants’ majority (2.77 – equal share, 2.76 – majority males,
2.38 – majority females, 2.62 – average)
– Knowledge: highest levels of knowledge when the genders
distribution was not equal (0.554 – majority males, 0.512 – majority
females, 0.499 – equal share and 0.526 – average)
– Innovation: the more females were in the team, the more innovative
they were: 0.545 – majority females, 0.465 – equal share, 0.461 –
majority males and 0.494 – average
– Seriousness
• most serious in the case where gender distribution was equal (3.81%)
• and least serious when they represented the majority in the chat (2.93%)
18.06.2013
12. Situation Analysis (II)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• Males:
– Involvement (average = 2.71):
• least involvement in the teams formed exclusively by males (2.611)
• most involved in the equal share teams (2.82).
– Knowledge (average = 0.452):
• most knowledgeable males in the equal share teams (0.562)
• the opposite was found in the case of teams formed by a female’
majority (0.209)
– Innovation (average = 0.47):
• least innovative (0.43) in the teams formed exclusively by males
• most innovation was seen in the case of the female majority teams
(0.492)
– Seriousness
• most of the time on topic in the female majority teams (3.95%)
• and least of the time in chats where there were also females, but
males represented the majority (1.87%)
18.06.2013
13. Individual Situation Analysis (I)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• Comparative behavior of males and females in each of the situations
we considered 4 different situations: majority males, equal share, majority
females and overall.
• Majority males:
– very small differences in the way males and females acted considering their
involvement, with a small advantage for the females (females uttered a little
more content but they were missing longer periods of time, were more
persistent and used slightly more repetitions).
– Qualitative evaluation: a big difference between males and females (females
were more knowledgeable and more serious, while males were more
innovative).
• Equal share:
– Involvement: females uttered the most utterances, but the males’ ones were
more elaborate, females were absent more time and they used more
repetitions.
– Qualitative evaluation: males and females seemed to have similar knowledge,
but females were more innovative and more serious.
18.06.2013
14. Individual Situation Analysis (II)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• Majority females:
– Involvement: the largest gap between the average number of
utterances introduced by females (51.7) and males (42), but the males
introduced much more content, the females were more absent and
used more repetitions.
– Qualitative evaluation: females proved to be more knowledgeable and
innovative (this case having the largest gap in both statistics), the
participants had almost the same score in the case of seriousness.
• Overall (biased to favor males, since there was no chat having only
females as participants, while the opposite situation was found in 2
chats):
– Involvement: males were more involved, thought most of the time
females seemed to be more communicative than the males, but they
were also absent for larger periods of time from the chat, they used
more repetitions and proved to be more useful in the conversation.
– Qualitative evaluation: females were more knowledgeable, more
innovative and they also proved to be more serious.
18.06.2013
15. Vocabulary Analysis (I)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• The vocabulary size of the whole corpus was 5310, 2468 of the words
being used exclusively by males and 1074 words being exclusively used by
females.
• A mean of 252.85 words/chat, with the highest value obtained in the case
of chats between males only – 619.5, the next value being obtained in the
case of equal share of males and females – 524.66.
• An average of 25.57 words were exclusively used by females and 31.24
words exclusively used by males. The average number of words used by
both males and females was 14.61 (again is biased - in the case of only
males chats, the size of common used words was 0).
• % of vocabulary that was common:
– male majority: 66.52% of the female vocabulary was also used by males, while
only 36.24% of the male vocabulary was used by females.
– equal share of males and females: these values are 50% in the case of females
and 48.21% in the males’ case.
– female majority, the percentage of the female vocabulary that is common is
41.6%, while in the case of males is 58.65%.
18.06.2013
16. Vocabulary Analysis (II)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• % of vocabulary that was on topic:
– males only: 3.15%;
– male majority: 2.94% for females and 1.87% for males;
– equal share of males and females: 3.81% for females and 3.48% for
males;
– female majority: 2.93% for females and 3.95% for males.
– average values: 1.56% for the males and 2.22% for the females
• Part-of-speech analysis:
– most of the top frequency words are determiners, prepositions,
pronouns, negations and auxiliary verbs.
– also, the main topics that were imposed for debating can be found in
these top frequency words.
– chat, blog, wiki, blogs, forum where amongst the most frequent words
used by females, while chat, wiki, blogs where amongst the top
frequent words used by males.
18.06.2013
17. Conclusions (I)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• Involvement: gender-unbalanced chat teams decrease
participants’ involvement – the more balanced the teams
are, the more involved are the participants.
• Knowledge - males and females acted differently:
– the males proved to be more knowledgeable in gender-
balanced teams and in teams composed of only males;
– the females acted worst in the case of equal shares of males
and females and acted. much better in the other cases.
• Innovation: the best situation is to have teams where
females represent the majority, since in this situation both
males and females are the most innovative.
18.06.2013
18. Conclusions (II)
Identifying Gender Differences in CSCL Chat Conversation CSCL 2013
• Seriousness - males and females acted differently:
– males being the most serious conversations with majority
females;
– good tradeoff seems to be given by the situation when we have
equal shares of males and females, since in this case the women
seemed to be the most serious while for the men this was the
second best case.
• Overall:
– women tend to be more innovative, while men appear to
discuss more on topic when they are in heterogeneous groups.
– For each individual CSCL task, teams should be composed taking
into account these factors as, in most cases, gender distribution
influences the overall performance of the participants to the
task.
18.06.2013