Implementation of Winnowing Algorithm and Simple Additive Weighting SAW for Publication Reference Journal Search System

Reference journals have several different writing arrangements. In the reference journal, there is some information or words needed in making scientific journals. With this information or word retrieval system, it can help to find journals that match the similarities between input and information or words in the journal that will be used as a reference. In this study, the input process of equations and information or words will be processed using the Winnowing algorithm as an algorithm that can find the similarity of words or texts with N-gram functions, rolling hash, and Jaccard Coefficient. In general, the search only uses the same words or text without any weighting on the composition of the reference journal. To be able to find journals and their level of importance in the order of journals, a weighting method is needed. This study also uses the Simple Additive Weighting (SAW) method as a process to determine the value of the order of urgency in journals so that it can provide results in the form of rankings based on searches and urgency weights in reference journals. The results of the similarity query with documents obtained 60% precision, 77% recall, and 81% accuracy, documents, and documents had 41% precision, 83% recall, and 66% accuracy. using Winnowing Algorithm, the search system can detect the similarity of text.

Scalable Multi-grained Cross-modal Similarity Query with Interpretability

Cross-modal similarity query has become a highlighted research topic for managing multimodal datasets such as images and texts. Existing researches generally focus on query accuracy by designing complex deep neural network models and hardly consider query efficiency and interpretability simultaneously, which are vital properties of cross-modal semantic query processing system on large-scale datasets. In this work, we investigate multi-grained common semantic embedding representations of images and texts and integrate interpretable query index into the deep neural network by developing a novel Multi-grained Cross-modal Query with Interpretability (MCQI) framework. The main contributions are as follows: (1) By integrating coarse-grained and fine-grained semantic learning models, a multi-grained cross-modal query processing architecture is proposed to ensure the adaptability and generality of query processing. (2) In order to capture the latent semantic relation between images and texts, the framework combines LSTM and attention mode, which enhances query accuracy for the cross-modal query and constructs the foundation for interpretable query processing. (3) Index structure and corresponding nearest neighbor query algorithm are proposed to boost the efficiency of interpretable queries. (4) A distributed query algorithm is proposed to improve the scalability of our framework. Comparing with state-of-the-art methods on widely used cross-modal datasets, the experimental results show the effectiveness of our MCQI approach.

On the Similarity Search With Hamming Space Sketches

This chapter focuses on data searching, which is nowadays mostly based on similarity. The similarity search is challenging due to its computational complexity, and also the fact that similarity is subjective and context dependent. The authors assume the metric space model of similarity, defined by the domain of objects and the metric function that measures the dissimilarity of object pairs. The volume of contemporary data is large, and the time efficiency of similarity query executions is essential. This chapter investigates transformations of metric space to Hamming space to decrease the memory and computational complexity of the search. Various challenges of the similarity search with sketches in the Hamming space are addressed, including the definition of sketching transformation and efficient search algorithms that exploit sketches to speed-up searching. The indexing of Hamming space and a heuristic to facilitate the selection of a suitable sketching technique for any given application are also considered.