Using machine learning to generate predictions based on the information extracted from automobile ads
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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
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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 thesisTweets topic modelling across different countries prezentarea
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
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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
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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).
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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.
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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)
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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
2012 Presidential Elections on Twitter - An Analysis of How the US and French...
2012 Presidential Elections on Twitter - An Analysis of How the US and French...University Politehnica Bucharest
The main objective of this paper is to compare
the sentiments that prevailed before and after the presidential
elections, held in both US and France in the year 2012. To
achieve this objective we extracted the content information from a
social medium such as Twitter and used the tweets from electoral
candidates and the public users (voters), collected by means of
crawling during the course of election. In order to gain useful
insights about the US elections, we scored the sentiments for
each tweet using different metrics and performed a time series
analysis for candidates and different topics (identified by specific
keywords). In addition to this, we compared some of our insights
obtained from the US election with what we have observed for
the French election. This deep dive analysis was done in order
to understand the inherent nature of elections and to bring out
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PhD Thesis - Influence of Repetitions on Discourse and Semantic Analysis
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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
Equivariant neural networks and representation theory
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
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When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
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Immersive Learning That Works: Research Grounding and Paths Forward
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EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
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were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
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- 1. Autor Conducător științific Universitatea Politehnica București Facultatea de Automatică și Calculatoare Catedra de Calculatoare Using Machine Learning to Generate Predictions Based on the Information Extracted from Automobile Ads Stere Caciandone and Costin-Gabriel CHIRU costin.chiru@cs.pub.ro
- 2. Introduction • Purpose: an application using ML to determine the correct reselling price of cars based on ads extracted from popular websites for this. • Background: Romanian market is dominated by previously owned cars (e.g.: Jan-Feb 2014 - 30,600 previously owned cars and only 8,770 new cars): VW 26%, Opel 18%, Ford 12%, BMW • Application: better inform the persons involved in the process (owners and potential buyers) if the price reflects the cars’ value 07.09.2016 AIMSA 2016 1
- 3. Similar Approaches • ML used for various tasks: determine the future value of goods (especially gold, oil and stocks), forecast the weather, predict the outcome of different sport events, etc. • Predict the resale value of cars: – Chen: Investigated the market of US for predicting the price of Toyota Corolla – linear regression – 9.2% err – Pudaruth, 2014: vehicles from Mauritius – Multiple Linear Regression, K-Nearest Neighbors, Decision Trees and Naïve Bayes small data, poor quality = bad results – Voß, & Lessmann, 2013: compared linear with non- linear learning algorithms better to use random forest07.09.2016 AIMSA 2016 2
- 4. Methodology • Extract the data related to car sale from ads posted on autovit.ro (largest online add platform for cars resale in Romania) • Analyze the ads to determine the links between the price of a car and its features using ML: – multiple linear regression and – random forest. • Use feature selection in order to obtain the best predictions. • Show the results using a web interface, allowing the user: – to see the car price predicted using ML (for buyers) – to enquire the system about the price for a particular model of car (for sellers) – To send to his/her e-mail account the links with “good” ads 07.09.2016 AIMSA 2016 3
- 5. The Data • Mined the content of the autovit.ro website using Scrappy web crawler over 16,000 car sale ads • Cleaned the data by removing: – ads that did not contain all the necessary data (e.g. number of kilometers, horsepower) – outliers (announcements about selling damaged cars for parts or ones containing different mistypes) • In the end the database consisted of 15,500 ads, each entry having the following information about the car: price, year of manufacture, mileage, horsepower, engine capacity, fuel type, model, transmission, norm euro, color, number of doors, the link to the page containing the image of the car and the one used for the sale announcement 07.09.2016 AIMSA 2016 4
- 6. Case Study – VW Passat (1) 07.09.2016 AIMSA 2016 5 • First, we needed to identify the relevant features of a car • Then to build the model using these features • Finally evaluate the predictive quality of the model • Used VW Passat (820 ads) for these steps. • Analyzed: the age of the car, mileage, horsepower, engine capacity, fuel type (petrol or diesel) and the type of transmission (automatic, manual): • Price = 16240 + Age * (-974.86) + No. km. * (-0.04) + Horse power * 54.29 + Diesel Fuel * 887.53 + Petrol Fuel *(-887.53) + Manual transm. * (-646.88) + Automated transm. * 424.31 • Average accuracy was 70.9% - 10-folds cross- validation
- 7. Case Study – VW Passat (2) 07.09.2016 AIMSA 2016 6
- 8. Case Study – VW Passat (3) 07.09.2016 AIMSA 2016 7 • Improve the model to obtain better accuracy by feature selection and feature engineering: – Price = 23 055 + Age * (-1108.55) + No. km. * (-0.04) 67.8% accuracy – Add horsepower accuracy 71.2% – Add transmission type: accuracy 71.4%: Price = 15 040 + Age * (-1047.05)+ No. km. * (-0.038) + Horsepower * 49.3 + Automated transm. * 974.07 + Manual transm. * 19.01 • Best model: used only 4 features (age, mileage, horsepower and type of transmission), but use logarithm(age) and logarithm(mileage): 82.3%
- 9. Case Study – VW Passat (4) • For model validation apply the obtained models on different brands / models of cars (using again 10-folds cross-validation): – VW Golf (945 ads): initial: 77%, improved: 86.6%; – Opel Astra (632 ads): initial: 75.1% , improved: 81.1% – Audi A4 (509 ads): initial: 78.2% , improved: 87%; – Ford Focus (474 ads): initial: 69.8% , improved: 82.7% – Skoda Octavia (398 ads): initial: 65.8% , improved: 75.1% 07.09.2016 AIMSA 2016 8
- 10. Results 07.09.2016 AIMSA 2016 9 Brand, model Average Brand, model Average Brand, model Average Audi A3 88.5% BMW X5 78.4% Skoda Fabia 90,4% Audi A4 87% BMW X6 72.1% Opel Astra 81,1% Audi A5 77.3% Ford Focus 82,7% Opel Corsa 89,6% Audi A6 84.2% Ford Fiesta 83,1% Mercedes C class 85.6% BMW Seria 3 76.3% Ford Mondeo 78,7 Mercedes S class 82.1% BMW Seria 5 74.2% Skoda Octavia 77,2% Renault Megane 76.2% VW Golf 86,6% VW Touareg 83,9% Dacia Logan 72.1% VW Passat 82,3% VW Polo 87.9% Dacia Sandero 68.1% Random Forest Regression Algorithm – best model used all the features Improved Multiple Linear Regression Algorithm Brand Audi BMW Ford Dacia Mercedes VW Skoda Renault Avg. 93.3% 92.2% 90.8% 89.3% 92.3% 93.3% 90.9% 91.2% Tested the models for brands / models having at least 50 + 8 * m entries (82 in our case)
- 11. Conclusions • We obtained good results by applying multiple linear regression on car models having a large number of ads (over 200) Prediction accuracy between 80 - 90 % depending on the brand / model • For the car models with a little over the minimum number of data samples required (82 ads) was obtained a prediction average accuracy of at least 70%. • For the car models with less than 82 ads the algorithm did not achieved satisfactory results confirming the formula suggested by Tabachnick & Fidell, 2013 • Most important part was related to the choice of the features and observing the type of relationship between the independent and dependent features • Predictions achieved by the random forest regression were much better - over 90% - confirming Voß, & Lessmann, 2013 07.09.2016 AIMSA 2016 10
- 12. Questions 07.09.2016 AIMSA 2016 11 Thank you very much!
Editor's Notes
- Tabachnick & Fidell, 2013