Models and Benchmarks for Representation Learning of Partially Observed Subgraphs

Subgraphs are rich substructures in graphs, and their nodes and edges can be partially observed in real-world tasks. Under partial observation, existing node- or subgraph-level message-passing produces suboptimal representations. In this paper, we formulate a novel task of learning representations of partially observed subgraphs. To solve this problem, we propose Partial Subgraph InfoMax (PSI) framework and generalize existing InfoMax models, including DGI, InfoGraph, MVGRL, and GraphCL, into our framework. These models maximize the mutual information between the partial subgraph's summary and various substructures from nodes to full subgraphs. In addition, we suggest a novel two-stage model with $k$-hop PSI, which reconstructs the representation of the full subgraph and improves its expressiveness from different local-global structures. Under training and evaluation protocols designed for this problem, we conduct experiments on three real-world datasets and demonstrate that PSI models outperform baselines.Comment: CIKM 2022 Short Paper (Camera-ready + Appendix

Translating Hanja Historical Documents to Contemporary Korean and English

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.Comment: 2022 EMNLP Finding

CReHate: Cross-cultural Re-annotation of English Hate Speech Dataset

English datasets predominantly reflect the perspectives of certain nationalities, which can lead to cultural biases in models and datasets. This is particularly problematic in tasks heavily influenced by subjectivity, such as hate speech detection. To delve into how individuals from different countries perceive hate speech, we introduce CReHate, a cross-cultural re-annotation of the sampled SBIC dataset. This dataset includes annotations from five distinct countries: Australia, Singapore, South Africa, the United Kingdom, and the United States. Our thorough statistical analysis highlights significant differences based on nationality, with only 59.4% of the samples achieving consensus among all countries. We also introduce a culturally sensitive hate speech classifier via transfer learning, adept at capturing perspectives of different nationalities. These findings underscore the need to re-evaluate certain aspects of NLP research, especially with regard to the nuanced nature of hate speech in the English language

PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players' in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.Comment: Under Review, Industry Trac

A Millimeter-Wave GaN MMIC Front End Module with 5G NR Performance Verification

This paper proposes a millimeter-wave (mmWave) 5G front end module (FEM) based on multiple gallium nitride (GaN) monolithic microwave integrated circuits (MMICs) with 5G new radio (NR) performance verification. The proposed structure is configured by a wide band GaN single-pole double-throw (SPDT) switch MMIC, a GaN low-noise amplifier (LNA) MMIC, and a GaN power amplifier (PA) MMIC with the target operation band from 26.5 GHz to 29.5 GHz. The LNA and PA MMICs are designed with 150 nm GaN/SiC technology, and the SPDT MMIC is designed with 100 nm GaN/Si. The LNA MMIC shows the measured noise figure less than or equal to 2.52 dB within the operation band. The PA MMIC is based on a two-stage configuration and shows about 35 dBm measured saturated power with power-added efficiency better than 34% within the operation band. Also, the SPDT MMIC is based on an artificial transmission line configuration for wideband performance and shows that the measured insertion loss is less than 1.6 dB, and the measured isolation is higher than 25 dB within the operation band. Furthermore, all MMICs are integrated within a single carrier as an FEM and successfully verified by 5G NR test signals

Age is a determinant factor in the susceptibility of domestic ducks to H5 clade 2.3.2.1c and 2.3.4.4e high pathogenicity avian influenza viruses

High pathogenicity avian influenza (HPAI) is a viral disease with devastating consequences for the poultry industry worldwide. Domestic ducks are a major source of HPAI viruses in many Eurasian countries. The infectivity and pathogenicity of HPAI viruses in ducks vary depending on host and viral factors. To assess the factors influencing the infectivity and pathogenicity of HPAI viruses in ducks, we compared the pathobiology of two HPAI viruses (H5N1 clade 2.3.2.1c and H5N6 clade 2.3.4.4e) in 5- and 25-week-old ducks. Both HPAI viruses caused mortality in a dose-dependent manner (104, 106, and 108 EID50) in young ducks. By contrast, adult ducks were infected but exhibited no mortality due to either virus. Viral excretion was higher in young ducks than in adults, regardless of the HPAI strain. These findings demonstrate the age-dependent mortality of clade 2.3.2.1c and clade 2.3.4.4e H5 HPAI viruses in ducks