Language is constantly changing, with words being created or disappearing over time. Moreover, th... more Language is constantly changing, with words being created or disappearing over time. Moreover, the usage of different words tends to fluctuate due to influences from different fields, such as historical events, cultural movements or scientific discoveries. These changes are reflected in the written texts and thus, by tracking them, one can determine the moment when these texts were written. In this paper, we present an application based on time series analysis built on top of the Google Books N-gram corpus to determine the time stamp of different written texts. The application is using two heuristics: words’ fingerprinting, to find the time interval when they were most probable used, and words’ importance for the given text, to weight the influence of words’ fingerprinting for estimating the text time stamp. Combining these two heuristics allows time stamping of that text.
This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, ... more This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset.
Every culture has its own topics of interest and its hot topics. In this paper we present a syste... more Every culture has its own topics of interest and its hot topics. In this paper we present a system that helps for a better understanding of different cultures, 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 by the citizens of different countries on Twitter (a worldwide conversations system), 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, 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 understanding amongst the readers. Our study is trying to capture the topic model for the tweets and for the URL shared in those tweets separately and then to compare the distribution of topics across different countries for both the tweets an...
Intent classification is a central component of a Natural Language Understanding (NLU) pipeline f... more Intent classification is a central component of a Natural Language Understanding (NLU) pipeline for conversational agents. The quality of such a component depends on the quality of the training data, however, for many conversational scenarios, the data might be scarce; in these scenarios, data augmentation techniques are used. Having general data augmentation methods that can generalize to many datasets is highly desirable. The work presented in this paper is centered around two main components. First, we explore the influence of various feature vectors on the task of intent classification using RASA’s text classification capabilities. The second part of this work consists of a generic method for efficiently augmenting textual corpora using large datasets of unlabeled data. The proposed method is able to efficiently mine for examples similar to the ones that are already present in standard, natural language corpora. The experimental results show that using our corpus augmentation me...
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020
Users from the online environment can create different ways of expressing their thoughts, opinion... more Users from the online environment can create different ways of expressing their thoughts, opinions, or conception of amusement. Internet memes were created specifically for these situations. Their main purpose is to transmit ideas by using combinations of images and texts such that they will create a certain state for the receptor, depending on the message the meme has to send. These posts can be related to various situations or events, thus adding a funny side to any circumstance our world is situated in. In this paper, we describe the system developed by our team for SemEval-2020 Task 8: Memotion Analysis. More specifically, we introduce a novel system to analyze these posts, a multimodal multi-task learning architecture that combines ALBERT for text encoding with VGG-16 for image representation. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the three subtasks of the current competition, ranking 11 th for Subtask A (0.3453 macro F1-score), 1 st for Subtask B (0.5183 macro F1-score), and 3 rd for Subtask C (0.3171 macro F1-score) while exceeding the official baseline results by high margins.
In the context of constantly evolving carry-on technology and its increasing accessibility, namel... more In the context of constantly evolving carry-on technology and its increasing accessibility, namely smart-phones and tablets, a greater need for reliable authentication means comes into sight. The current study offers an alternative solution of uninterrupted testing towards verifying user legitimacy. A continuously collected dataset of 41 users’ touch-screen inputs provides a good starting point into modeling each user’s behavior and later differentiate among users. We introduce a system capable of processing features based on raw data extracted from user-screen interactions and attempting to assign each gesture to its originator. Achieving an accuracy of over 83 %, we prove that this type of authentication system is feasible and that it can be further integrated as a continuous way of disclosing intruders within given mobile applications.
Water resource management represents a fundamental aspect of a modern society. Urban areas presen... more Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods.
Water supply systems are essential for a modern society. This article presents an overview of the... more Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020
Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phe... more Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 th out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17 th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa.
Artificial Intelligence: Methodology, Systems, and Applications, 2018
In this paper, we present an approach to forecasting the number of paintings that will be sold da... more In this paper, we present an approach to forecasting the number of paintings that will be sold daily by Vivre Deco S.A. Vivre is an online retailer for Home and Lifestyle in Central and Eastern Europe. One of its concerns is related to the stocks that it needs to make at its own warehouse (considering its limited available space) to ensure a good product flow that would maximize both the company profit and the users’ satisfaction. Since stocks are directly connected to sales, the purpose is to predict the amount of sales from each category of products, given the selling history of these products. Thus, we have chosen a category of products (paintings) and used ARIMA for obtaining the predictions. We present different considerations regarding how we chose the model, along with the solver and the optimization method for fitting ARIMA. We also discuss the influence of the differencing on the obtained results, along with information about the runtime of different models.
This paper presents the current state of the gaming industry, which provides an important backgro... more This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is designed to provide entertainment value by means of gamified interaction with the environment, while the crowdsensing component involves a set of roles for finding, solving and validating water-related issues. The mathematical model of distance-constrained multi-depot vehicle routing problem with heterogeneous fleet capacity is evaluated in the context of the proposed scenario, with random initial...
In this paper, we present an application for identifying English words whose use is cyclic or reg... more In this paper, we 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.
In this paper, we present Streamer, a search application running over streams of Twitter messages... more In this paper, we present Streamer, a search application running over streams of Twitter messages. As opposed to most services that only do simple text search over conversations, Streamer aims to cluster messages together in order to simplify analyzing a large number of messages from similar topics. The novelty of Streamer is that, unlike most applications that use fixed corpus or categories when clustering, it works with streaming data that may debate about any number of topics: Twitter messages are continuously retrieved and the clusters are updated as more data comes in. The running time and clustering quality of the application were evaluated using purity and Silhouette coefficient.
Language is constantly changing, with words being created or disappearing over time. Moreover, th... more Language is constantly changing, with words being created or disappearing over time. Moreover, the usage of different words tends to fluctuate due to influences from different fields, such as historical events, cultural movements or scientific discoveries. These changes are reflected in the written texts and thus, by tracking them, one can determine the moment when these texts were written. In this paper, we present an application based on time series analysis built on top of the Google Books N-gram corpus to determine the time stamp of different written texts. The application is using two heuristics: words’ fingerprinting, to find the time interval when they were most probable used, and words’ importance for the given text, to weight the influence of words’ fingerprinting for estimating the text time stamp. Combining these two heuristics allows time stamping of that text.
This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, ... more This paper describes the solutions submitted by the UPB team to the AuTexTification shared task, featured as part of IberLEF-2023. Our team participated in the first subtask, identifying text documents produced by large language models instead of humans. The organizers provided a bilingual dataset for this subtask, comprising English and Spanish texts covering multiple domains, such as legal texts, social media posts, and how-to articles. We experimented mostly with deep learning models based on Transformers, as well as training techniques such as multi-task learning and virtual adversarial training to obtain better results. We submitted three runs, two of which consisted of ensemble models. Our best-performing model achieved macro F1-scores of 66.63% on the English dataset and 67.10% on the Spanish dataset.
Every culture has its own topics of interest and its hot topics. In this paper we present a syste... more Every culture has its own topics of interest and its hot topics. In this paper we present a system that helps for a better understanding of different cultures, 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 by the citizens of different countries on Twitter (a worldwide conversations system), 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, 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 understanding amongst the readers. Our study is trying to capture the topic model for the tweets and for the URL shared in those tweets separately and then to compare the distribution of topics across different countries for both the tweets an...
Intent classification is a central component of a Natural Language Understanding (NLU) pipeline f... more Intent classification is a central component of a Natural Language Understanding (NLU) pipeline for conversational agents. The quality of such a component depends on the quality of the training data, however, for many conversational scenarios, the data might be scarce; in these scenarios, data augmentation techniques are used. Having general data augmentation methods that can generalize to many datasets is highly desirable. The work presented in this paper is centered around two main components. First, we explore the influence of various feature vectors on the task of intent classification using RASA’s text classification capabilities. The second part of this work consists of a generic method for efficiently augmenting textual corpora using large datasets of unlabeled data. The proposed method is able to efficiently mine for examples similar to the ones that are already present in standard, natural language corpora. The experimental results show that using our corpus augmentation me...
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020
Users from the online environment can create different ways of expressing their thoughts, opinion... more Users from the online environment can create different ways of expressing their thoughts, opinions, or conception of amusement. Internet memes were created specifically for these situations. Their main purpose is to transmit ideas by using combinations of images and texts such that they will create a certain state for the receptor, depending on the message the meme has to send. These posts can be related to various situations or events, thus adding a funny side to any circumstance our world is situated in. In this paper, we describe the system developed by our team for SemEval-2020 Task 8: Memotion Analysis. More specifically, we introduce a novel system to analyze these posts, a multimodal multi-task learning architecture that combines ALBERT for text encoding with VGG-16 for image representation. In this manner, we show that the information behind them can be properly revealed. Our approach achieves good performance on each of the three subtasks of the current competition, ranking 11 th for Subtask A (0.3453 macro F1-score), 1 st for Subtask B (0.5183 macro F1-score), and 3 rd for Subtask C (0.3171 macro F1-score) while exceeding the official baseline results by high margins.
In the context of constantly evolving carry-on technology and its increasing accessibility, namel... more In the context of constantly evolving carry-on technology and its increasing accessibility, namely smart-phones and tablets, a greater need for reliable authentication means comes into sight. The current study offers an alternative solution of uninterrupted testing towards verifying user legitimacy. A continuously collected dataset of 41 users’ touch-screen inputs provides a good starting point into modeling each user’s behavior and later differentiate among users. We introduce a system capable of processing features based on raw data extracted from user-screen interactions and attempting to assign each gesture to its originator. Achieving an accuracy of over 83 %, we prove that this type of authentication system is feasible and that it can be further integrated as a continuous way of disclosing intruders within given mobile applications.
Water resource management represents a fundamental aspect of a modern society. Urban areas presen... more Water resource management represents a fundamental aspect of a modern society. Urban areas present multiple challenges requiring complex solutions, which include multidomain approaches related to the integration of advanced technologies. Water consumption monitoring applications play a significant role in increasing awareness, while machine learning has been proven for the design of intelligent solutions in this field. This paper presents an approach for monitoring and predicting water consumption from the most important water outlets in a household based on a proposed IoT solution. Data processing pipelines were defined, including K-means clustering and evaluation metrics, extracting consumption events, and training classification methods for predicting consumption sources. Continuous water consumption monitoring offers multiple benefits toward improving decision support by combining modern processing techniques, algorithms, and methods.
Water supply systems are essential for a modern society. This article presents an overview of the... more Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.
Proceedings of the Fourteenth Workshop on Semantic Evaluation, 2020
Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phe... more Sentiment analysis is a process widely used in opinion mining campaigns conducted today. This phenomenon presents applications in a variety of fields, especially in collecting information related to the attitude or satisfaction of users concerning a particular subject. However, the task of managing such a process becomes noticeably more difficult when it is applied in cultures that tend to combine two languages in order to express ideas and thoughts. By interleaving words from two languages, the user can express with ease, but at the cost of making the text far less intelligible for those who are not familiar with this technique, but also for standard opinion mining algorithms. In this paper, we describe the systems developed by our team for SemEval-2020 Task 9 that aims to cover two well-known code-mixed languages: Hindi-English and Spanish-English. We intend to solve this issue by introducing a solution that takes advantage of several neural network approaches, as well as pre-trained word embeddings. Our approach (multlingual BERT) achieves promising performance on the Hindi-English task, with an average F1-score of 0.6850, registered on the competition leaderboard, ranking our team 16 th out of 62 participants. For the Spanish-English task, we obtained an average F1-score of 0.7064 ranking our team 17 th out of 29 participants by using another multilingual Transformer-based model, XLM-RoBERTa.
Artificial Intelligence: Methodology, Systems, and Applications, 2018
In this paper, we present an approach to forecasting the number of paintings that will be sold da... more In this paper, we present an approach to forecasting the number of paintings that will be sold daily by Vivre Deco S.A. Vivre is an online retailer for Home and Lifestyle in Central and Eastern Europe. One of its concerns is related to the stocks that it needs to make at its own warehouse (considering its limited available space) to ensure a good product flow that would maximize both the company profit and the users’ satisfaction. Since stocks are directly connected to sales, the purpose is to predict the amount of sales from each category of products, given the selling history of these products. Thus, we have chosen a category of products (paintings) and used ARIMA for obtaining the predictions. We present different considerations regarding how we chose the model, along with the solver and the optimization method for fitting ARIMA. We also discuss the influence of the differencing on the obtained results, along with information about the runtime of different models.
This paper presents the current state of the gaming industry, which provides an important backgro... more This paper presents the current state of the gaming industry, which provides an important background for an effective serious game implementation in mobile crowdsensing. An overview of existing solutions, scientific studies and market research highlights the current trends and the potential applications for citizen-centric platforms in the context of Cyber–Physical–Social systems. The proposed solution focuses on serious games applied in urban water management from the perspective of mobile crowdsensing, with a reward-driven mechanism defined for the crowdsensing tasks. The serious game is designed to provide entertainment value by means of gamified interaction with the environment, while the crowdsensing component involves a set of roles for finding, solving and validating water-related issues. The mathematical model of distance-constrained multi-depot vehicle routing problem with heterogeneous fleet capacity is evaluated in the context of the proposed scenario, with random initial...
In this paper, we present an application for identifying English words whose use is cyclic or reg... more In this paper, we 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.
In this paper, we present Streamer, a search application running over streams of Twitter messages... more In this paper, we present Streamer, a search application running over streams of Twitter messages. As opposed to most services that only do simple text search over conversations, Streamer aims to cluster messages together in order to simplify analyzing a large number of messages from similar topics. The novelty of Streamer is that, unlike most applications that use fixed corpus or categories when clustering, it works with streaming data that may debate about any number of topics: Twitter messages are continuously retrieved and the clusters are updated as more data comes in. The running time and clustering quality of the application were evaluated using purity and Silhouette coefficient.
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Papers by Costin Chiru