Diyah Puspitaningrum

Diyah Puspitaningrum

This paper surveys several recent abstract summarization methods: T5, Pegasus, and ProphetNet. We implement the systems in two languages: English and Indonesian languages. We investigate the impact of pre-training models (one T5, three Pegasuses, three ProphetNets) on several Wikipedia datasets in English and Indonesian language and compare the results to the Wikipedia systems' summaries. The T5-Large, the Pegasus-XSum, and the ProphetNet-CNNDM provide the best summarization. The most significant factors that influence ROUGE performance are coverage, density, and compression. The higher the scores, the better the summary. Other factors that influence the ROUGE scores are the pre-training goal, the dataset's characteristics, the dataset used for testing the pre-trained model, and the cross-lingual function. Several suggestions to improve this paper's limitation are: 1) assure that the dataset used for the pre-training model must sufficiently large, contains adequate instances for handling cross-lingual purpose; 2) Advanced process (finetuning) shall be reasonable. We recommend using the large dataset consists of comprehensive coverage of topics from many languages before implementing advanced processes such as the train-infer-train procedure to the zero-shot translation in the training stage of the pre-training model.

Diyah Puspitaningrum

While working in collaborative team elsewhere sometimes the federated (huge) data are from heterogeneous cloud vendors. It is not only about the data privacy concern but also about how can those federated data can be querying from cloud directly in fast and securely way. Previous solution offered hybrid cloud between public and trusted private cloud. Another previous solution used encryption on MapReduce framework. But the challenge is we are working on heterogeneous clouds. In this paper, we present a novel technique for querying with privacy concern. Since we take execution time into account, our basic idea is to use the data mining model by partitioning the federated databases in order to reduce the search and query time. By using model of the database it means we use only the summary or the very characteristic patterns of the database. Modeling is the Preserving Privacy Stage I, since by modeling the data is being symbolized. We implement encryption on the database as preserving privacy Stage II. Our system, called "cSELENE" (stands for "cloud SELENE"), is designed to handle federated data on heterogeneous clouds: AWS, Microsoft Azure, and Google Cloud Platform with MapReduce technique. In this paper we discuss preserving-privacy system and threat model, the format of federated data, the parallel programming (GPU programming and shared/memory systems), the parallel and secure algorithm for data mining model in distributed cloud, the cloud infrastructure/architecture, and the UIX design of the cSELENE system. Other issues such as incremental method and the secure design of cloud architecture system (Virtual Machines across platform design) are still open to discuss. Our experiments should demonstrate the validity and practicality of the proposed high performance computing scheme.

Diyah Puspitaningrum, I. S. W. B. Prasetya, P. A. Wicaksono

A route recommendation system can provide better recommendation if it also takes collected user reviews into account, e.g. places that generally get positive reviews may be preferred. However, to classify sentiment, many classification algorithms existing today suffer in handling small data items such as short written reviews. In this paper we propose a model for a strongly relevant route recommendation system that is based on an MDL-based (Minimum Description Length) sentiment classification and show that such a system is capable of handling small data items (short user reviews). Another highlight of the model is the inclusion of a set of boosting factors in the relevance calculation to improve the relevance in any recommendation system that implements the model.

D. Puspitaningrum, G. Yulianti, I. S. W. B. Prasetya

This paper discusses the use of Wikipedia for building semantic ontologies to do Query Expansion (QE) in order to improve the search results of search engines. In this technique, selecting related Wikipedia concepts becomes important. We propose the use of network properties (degree, closeness, and pageRank) to build an ontology graph of user query concepts which is derived directly from Wikipedia structures. The resulting expansion system is called Wiki-MetaSemantik. We tested this system against other online thesauruses and ontology based QE in both individual and meta-search engines setups. Despite that our system has to build a Wikipedia ontology graph in order to do its work, the technique turns out to work very fast (1:281) compared to another ontology QE baseline (Wikipedia Persian ontology QE). It has thus the potential to be utilized online. Furthermore, it shows significant improvement in accuracy. Wiki-MetaSemantik also shows better performance in a meta-search engine (MSE) set up rather than in an individual search engine set up.