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Bing Liu - IEEE Xplore Author Profile

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As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it encounters challenges in adapting to dynamic environments and meeting rising communication and computation needs. In this article, we propose a semantic-aware UAV-based architecture tailored to the dynamic Metaverse that leverages UAV swarms consisting of a collection UAV and edge UAV serve...Show More
Mining multiple longest common subsequences (MLCS) from a set of sequences of length three or more over a finite alphabet (a classical NP-hard problem) is an important task in many fields, e.g., bioinformatics, computational genomics, pattern recognition, information extraction, etc. Applications in these fields often involve generating very long sequences (length $\geqslant$⩾ 10,000), referred to...Show More
Automatically identifying the discourse relations can help many downstream NLP tasks such as reading comprehension and machine translation. It can be categorized into explicit and implicit discourse relation recognition (EDRR and IDRR). Due to the lack of connectives, IDRR remains to be a big challenge. A good number of methods have been developed to combine explicit data with implicit ones under ...Show More
Satellite-airborne-terrestrial edge computing networks (SATECNs) emerge as a global solution for Internet of Things (IoT) applications in 6G. However, their highly dynamic nature with uncertain varying topology and network traffic makes their management and control more challenging. In this paper, we consider a scenario in which IoT devices, UAVs, and satellites with different edge computing capab...Show More
Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the only challenge. This paper argues for another challenge in class-incremental learning (CIL), which we call cross-task class discrimination (CTCD), i.e., how to establish decision boundaries between the classes of the new task and old tasks with no (or limited) access to the old task data. CTCD is implicitl...Show More
With compact and discriminative fingerprint representation, fingerprint indexing can effectively reduce the search space and improve the efficiency in large-scale fingerprint identification. Previous fixed-length fingerprint representations do not combine the global and minutiae local information well, leading to unsatisfactory results. In this paper, we utilize an end-to-end network to extract Mi...Show More
Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for TIL, but not provided for CIL. Continual learning methods intended for one problem have limitations on the other problem. This paper proposes a novel unifie...Show More
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word sequence may introduce some noise that has little or no meaningful content. Meanwhile, the syntactic features are usually encoded via graph convolutional networks which have restricte...Show More
Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several cri...Show More
With ever-increasing data streams from various applications such as smart phones, network monitoring, Internet of Things (IoT), etc., unsupervised clustering of data streams has become an important problem for machine learning and big data analysis. As data streams are data-intensive, temporally ordered, and rapidly evolving, efficiently and effectively online clustering of data streams presents a...Show More
Multi-view graph-based clustering aims to provide clustering solutions to multi-view data. However, most existing methods do not give sufficient consideration to weights of different views and require an additional clustering step to produce the final clusters. They also usually optimize their objectives based on fixed graph similarity matrices of all views. In this paper, we propose a general Gra...Show More
Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promising for this task. However, most dependency tree structures involve only one directional propagation on the dependency tree. In this paper, we first propose a novel bidirectional dependency tree network to extract depen...Show More
Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. It aims at classifying the sentiment expressed on some target aspects/features of entities (e.g., products and services). Although a great deal of research has been done, this task remains to be very challenging. Recently, memory networks, a type of neural model, have been used for this task and have achieved state-...Show More
In this paper, we present our data collection process, annotation schemas and agreement results for extracting health goals from SMS conversations between a health coach and the patients. We also discuss our preliminary results for automatically detecting topic boundaries in health coaching dialogues. This is our first step towards building an autonomous virtual assistant health coach that learns ...Show More
Learning from categorical data plays a fundamental role in such areas as pattern recognition, machine learning, data mining, and knowledge discovery. To effectively discover the group structure inherent in a set of categorical objects, many categorical clustering algorithms have been developed in the literature, among which k-modes-type algorithms are very representative because of their good perf...Show More
Ordinal classification with a monotonicity constraint is a kind of classification tasks, in which the objects with better attribute values should not be assigned to a worse decision class. Several learning algorithms have been proposed to handle this kind of tasks in recent years. The rank entropy-based monotonic decision tree is very representative thanks to its better robustness and generalizati...Show More
As social media is becoming an increasingly important source of public information, companies, organizations and individuals are actively using social media platforms to promote their products, services, ideas and ideologies. Unlike promotional campaigns on TV or other traditional mass media platforms, campaigns on social media often appear in stealth modes. Campaign promoters often try to influen...Show More
Online reviews have become an increasingly important resource for decision making and product designing. But reviews systems are often targeted by opinion spamming. Although fake review detection has been studied by researchers for years using supervised learning, ground truth of large scale datasets is still unavailable and most of existing approaches of supervised learning are based on pseudo fa...Show More
Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach performs well. In this paper, we show that Logic Programming, particularly Answer Set Programming (ASP), can be used to elegantly and efficiently implement the key components of syntax based aspect extraction. Specifically, the well known double propagation (DP) me...Show More
Review aggregators and e-commerce sites are just two examples of businesses that rely on opinion mining to produce feature-based summaries of products' qualities. This model first identifies product features and then collects positive and negative opinions on them to produce a summary of good and bad points.Show More
Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. Previous attempts for spammer detection used reviewers' behaviors, text similarity, linguistics features and rating patterns. Those studies are able to identify certain types of spammers, e.g., those who post many simil...Show More
The problem of finding interesting and actionable patterns is a major challenge in data mining. It has been studied by many data mining researchers. The issue is that data mining algorithms often generate too many patterns, which make it very hard for the user to find those truly useful ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. At...Show More
Link-based ranking has contributed significantly to the success of Web search. PageRank and HITS are the best known link-based ranking algorithms. These algorithms do not consider an important dimension, the temporal dimension. They favor older pages because these pages have many in-links accumulated over time. Bringing new and quality pages to the users is important because most users want the la...Show More
Mining of opinions from product reviews, forum posts and blogs is an important research topic with many applications. However, existing research has been focused on extraction, classification and summarization of opinions from these sources. An important issue that has not been studied so far is the opinion spam or the trustworthiness of online opinions. In this paper, we study this issue in the c...Show More
This paper studies the problem of discovering latent associations among objects in text documents. Specifically, given two sets of objects and various types of co-occurrence data concerning the objects existing in texts, we aim to discover the hidden or latent associative relationships between the two sets of objects. Existing methods are not directly applicable as they are unable to consider all ...Show More