Volume 16, Issue 4August 2022
Compression of Deep Learning Models for Text: A Survey
August 2022, Article No.: 61, pp 1–55https://doi.org/10.1145/3487045

In recent years, the fields of natural language processing (NLP) and information retrieval (IR) have made tremendous progress thanks to deep learning models like Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs) and Long Short-Term Memory (...

Forecasting the Market with Machine Learning Algorithms: An Application of NMC-BERT-LSTM-DQN-X Algorithm in Quantitative Trading
August 2022, Article No.: 62, pp 1–22https://doi.org/10.1145/3488378

Although machine learning (ML) algorithms have been widely used in forecasting the trend of stock market indices, they failed to consider the following crucial aspects for market forecasting: (1) that investors’ emotions and attitudes toward future market ...

Open Access
RHPTree—Risk Hierarchical Pattern Tree for Scalable Long Pattern Mining
August 2022, Article No.: 63, pp 1–33https://doi.org/10.1145/3488380

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle ...

Mixed Information Flow for Cross-Domain Sequential Recommendations
August 2022, Article No.: 64, pp 1–32https://doi.org/10.1145/3487331

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to ...

TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction
August 2022, Article No.: 65, pp 1–22https://doi.org/10.1145/3487047

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate ...

Causal Feature Selection with Missing Data
August 2022, Article No.: 66, pp 1–24https://doi.org/10.1145/3488055

Causal feature selection aims at learning the Markov blanket (MB) of a class variable for feature selection. The MB of a class variable implies the local causal structure among the class variable and its MB and all other features are probabilistically ...

A Trajectory Evaluator by Sub-tracks for Detecting VOT-based Anomalous Trajectory
August 2022, Article No.: 67, pp 1–19https://doi.org/10.1145/3490032

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous ...

A Self-Supervised Representation Learning of Sentence Structure for Authorship Attribution
August 2022, Article No.: 68, pp 1–16https://doi.org/10.1145/3491203

The syntactic structure of sentences in a document substantially informs about its authorial writing style. Sentence representation learning has been widely explored in recent years and it has been shown that it improves the generalization of different ...

Privacy-Preserving Mechanisms for Multi-Label Image Recognition
August 2022, Article No.: 69, pp 1–21https://doi.org/10.1145/3491231

Multi-label image recognition has been an indispensable fundamental component for many real computer vision applications. However, a severe threat of privacy leakage in multi-label image recognition has been overlooked by existing studies. To fill this ...

Domain-Specific Keyword Extraction Using Joint Modeling of Local and Global Contextual Semantics
August 2022, Article No.: 70, pp 1–30https://doi.org/10.1145/3494560

Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific ...

Adaptive Model Scheduling for Resource-efficient Data Labeling
August 2022, Article No.: 71, pp 1–22https://doi.org/10.1145/3494559

Labeling data (e.g., labeling the people, objects, actions, and scene in images) comprehensively and efficiently is a widely needed but challenging task. Numerous models were proposed to label various data and many approaches were designed to enhance the ...

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction
August 2022, Article No.: 72, pp 1–18https://doi.org/10.1145/3494565

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward ...

ABLE: Meta-Path Prediction in Heterogeneous Information Networks
August 2022, Article No.: 73, pp 1–21https://doi.org/10.1145/3494558

Given a heterogeneous information network (HIN) H, a head node h, a meta-path P, and a tail node t, the meta-path prediction aims at predicting whether h can be linked to t by an instance of P. Most existing solutions either require predefined meta-paths, ...

Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition
August 2022, Article No.: 74, pp 1–20https://doi.org/10.1145/3494568

Instrumental variables (IVs), sources of treatment randomization that are conditionally independent of the outcome, play an important role in causal inference with unobserved confounders. However, the existing IV-based counterfactual prediction methods ...

Real-Time Anomaly Detection in Edge Streams
August 2022, Article No.: 75, pp 1–22https://doi.org/10.1145/3494564

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising ...

sGrapp: Butterfly Approximation in Streaming Graphs
August 2022, Article No.: 76, pp 1–43https://doi.org/10.1145/3495011

We study the fundamental problem of butterfly (i.e., (2,2)-bicliques) counting in bipartite streaming graphs. Similar to triangles in unipartite graphs, enumerating butterflies is crucial in understanding the structure of bipartite graphs. This benefits ...

Multiple Graphs and Low-Rank Embedding for Multi-Source Heterogeneous Domain Adaptation
August 2022, Article No.: 77, pp 1–25https://doi.org/10.1145/3492804

Multi-source domain adaptation is a challenging topic in transfer learning, especially when the data of each domain are represented by different kinds of features, i.e., Multi-source Heterogeneous Domain Adaptation (MHDA). It is important to take ...

When Less Is More: Systematic Analysis of Cascade-Based Community Detection
August 2022, Article No.: 78, pp 1–22https://doi.org/10.1145/3494563

Information diffusion, spreading of infectious diseases, and spreading of rumors are fundamental processes occurring in real-life networks. In many practical cases, one can observe when nodes become infected, but the underlying network, over which a ...

Distributed Triangle Approximately Counting Algorithms in Simple Graph Stream
August 2022, Article No.: 79, pp 1–43https://doi.org/10.1145/3494562

Recently, the counting algorithm of local topology structures, such as triangles, has been widely used in social network analysis, recommendation systems, user portraits and other fields. At present, the problem of counting global and local triangles in a ...

Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification
August 2022, Article No.: 80, pp 1–19https://doi.org/10.1145/3494567

Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem. However, most of ...



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