Automatic Discovery of Network Applications: A hybrid Approach

I attended the Canadian AI conference between May 30, 2010 – June 2, 2010. On May 30, 2010, I jointly with Marina Sokolova from the Children Hospital of Eastern Ontario, co-chaired the Canadian AI graduate students symposium. The symposium had originally attracted about 23 submissions and had an acceptance rate of around 25%. There were about 30 participants from around Canada along with 5 professor panelists and 2 researchers from industry. The organization of this symposium both from an academic perspective and also logistics was done by myself and Marina. The symposium was a great success in terms of both the number of attendance and the quality of the work presented at the symposium. On the May 31, 2010, I presented my paper titled “Automatic Discovery of Network Applications: A hybrid Approach”. There were interesting issues raised during the Q&A period that can lead to the betterment of the work in the future including how the network packets were labeled before they were used in the classification algorithm and also about the possible applications of our work for network planning and alert correlation.Automatic discovery of network applications is a very challenging task which has received a lot of attentions due to its importance in many areas such as network security, QoS provisioning, and network management. In this paper, we propose an online hybrid mechanism for the classification of network flows, in which we employ a signature-based classifier in the first level, and then using the weighted unigram model we improve the performance of the system by labeling the unknown portion. Our evaluation on two real networks shows between 5% and 9% performance improvement applying the genetic algorithm based scheme to find the appropriate weights for the unigram model

Someone Who is Not Like Anyone

This paper is in the support of the Thesis Exhibition, Someone Who Is Not Like Anyone, which includes artworks in ballpoint pen and watercolors. In the exhibition, I explore the different moods of masculinity that I observed and sought to inhabit while in the closet in Iran but which I ultimately rejected. In the process, I recreate for the viewer the aesthetics/experiences of closeted men. The paper explores how art, under various circumstances, has been a savior in my life. This paper is also an overview of the development of my artistic growth throughout the closeted years to the coming out years. I divide my artistic life into different chapters: childhood; teen years; artistic discovery; immigration and thesis exhibition. This process has gradually moved from private to public, as well as from fear to confidence

A latent model for ad hoc table retrieval

© Springer Nature Switzerland AG 2020. The ad hoc table retrieval task is concerned with satisfying a query with a ranked list of tables. While there are strong baselines in the literature that exploit learning to rank and semantic matching techniques, there are still a set of hard queries that are difficult for these baseline methods to address. We find that such hard queries are those whose constituting tokens (i.e., terms or entities) are not fully or partially observed in the relevant tables. We focus on proposing a latent factor model to address such hard queries. Our proposed model factorizes the token-table co-occurrence matrix into two low dimensional latent factor matrices that can be used for measuring table and query similarity even if no shared tokens exist between them. We find that the variation of our proposed model that considers keywords provides statistically significant improvement over three strong baselines in terms of NDCG and ERR

Impact of document representation on neural ad hoc retrieval

© 2018 Association for Computing Machinery. Neural embeddings have been effectively integrated into information retrieval tasks including ad hoc retrieval. One of the benefits of neural embeddings is they allow for the calculation of the similarity between queries and documents through vector similarity calculation methods. While such methods have been effective for document matching, they have an inherent bias towards documents that are sized relatively similarly. Therefore, the difference between the query and document lengths, referred to as the query-document size imbalance problem, becomes an issue when incorporating neural embeddings and their associated similarity calculation models into the ad hoc document retrieval process. In this paper, we propose that document representation methods need to be used to address the size imbalance problem and empirically show their impact on the performance of neural embedding-based ad hoc retrieval. In addition, we explore several types of document representation methods and investigate their impact on the retrieval process. We conduct our experiments on three widely used standard corpora, namely Clueweb09B, Clueweb12B and Robust04 and their associated topics. Summarily, we find that document representation methods are able to effectively address the query-document size imbalance problem and significantly improve the performance of neural ad hoc retrieval. In addition, we find that a document representation method based on a simple term-frequency shows significantly better performance compared to more sophisticated representation methods such as neural composition and aspect-based methods

Extracting temporal and causal relations based on event networks

© 2020 Elsevier Ltd Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical structure patterns at the sentence-level. In this paper, we address these challenges by proposing an unsupervised event network representation for temporal and causal relation extraction that operates at the document level. More specifically, we benefit from existing Open IE systems to generate a set of triple relations that are then used to build an event network. The event network is bootstrapped by labeling the temporal disposition of events that are directly linked to each other. We then systematically traverse the event network to identify the temporal and causal relations between indirectly connected events. We perform experiments based on the widely adopted TempEval-3 and Causal-TimeBank corpora and compare our work with several strong baselines. We show that our method improves performance compared to several strong methods