As one of the popular social media that many people turn to in recent years, collaborative encyclopedia Wikipedia provides information in a more “Neutral Point of View” way than others. Towards this core principle, plenty of efforts have been put into collaborative contribution and editing. The trajectories of how such collaboration appears by revisions are valuable for group dynamics and social media research, which suggest that we should extract the underlying derivation relationships among revisions from chronologically-sorted revision history in a precise way. In this paper, we propose a revision graph extraction method based on supergram decomposition in the document collection of near-duplicates. The plain text of revisions would be measured by its frequency distribution of supergram, which is the variable-length token sequence that keeps the same through revisions. We show that this method can effectively perform the task than existing methods.

Providing various assistances for design modifications on HDL source codes is important for design reuse and quick design cycle in VLSI CAD. Program slicing is a software-engineering technique for analyzing, abstracting, and transforming programs. We show algorithms for extracting/removing behaviors of specified signals in VHDL descriptions. We also describe a VHDL slicing system and show experimental results of efficiently extracting components from VHDL descriptions.

Exploring enormous state graphs represented implicitly by ordered binary decision diagrams (OBDDs) is one of the most successful applications of OBDDs. However, our worst-case analysis of implicit graph representations by OBDDs shows that there are cases where OBDD representations are not optimal and require more space than adjacency lists. As an improvement, we propose a new type of BDDs, called Patricia BDDs, which are capable of implicit representation of graphs while their worst-case sizes are kept equal or less than adjacency lists and OBDDs.

Data sparsity has always been a problem in document classification, for which semi-supervised learning and few-shot learning are studied. An even more extreme scenario is to classify documents without any annotated data, but using only category names. In this paper, we introduce a nearest neighbor search-based method Con2Class to tackle this tough task. We intend to produce embeddings for predefined categories and predict category embeddings for all the unlabeled documents in a unified embedding space, such that categories can be easily assigned by searching the nearest predefined category in the embedding space. To achieve this, we propose confidence-driven contrastive learning, in which prompt-based templates are designed and MLM-maintained contrastive loss is newly proposed to finetune a pretrained language model for embedding production. To deal with the issue that no annotated data is available to validate the classification model, we introduce confidence factor to estimate the classification ability by evaluating the prediction confidence. The language model having the highest confidence factor is used to produce embeddings for similarity evaluation. Pseudo labels are then assigned by searching the semantically closest category name, which are further used to train a separate classifier following a progressive self-training strategy for final prediction. Our experiments on five representative datasets demonstrate the superiority of our proposed method over the existing approaches.