The prevalence of misinformation, both online and offline, has prompted a great demand of fact verification. Table-based fact verification aims to check whether a textual claim is supported or refuted based on relational tables. However, most of the existing approaches are in a closed-domain setting, which may not be realistic in practice. To address this problem, in this paper, we introduce OpenTFV, a user-friendly system that supports open domain table-based fact verification. Given a claim input by an end-user, OpenTFV retrieves the relevant tables, and provides a verification result for each table with an intuitive interpretation in natural language. We have implemented OpenTFV and demonstrated OpenTFV in two representative scenarios, COVID-19 claims fact verification based on academic tables and general fact verification on Wiki-tables.

Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-world ER applications, the situation changes dramatically, with a painful issue to collect large-scale labeled datasets. In this paper, we seek to answer: If we have a well-labeled source ER dataset, can we train a DL-based ER model for a target dataset, without any labels or with a few labels? This is known as domain adaptation (DA), which has achieved great successes in computer vision and natural language processing, but is not systematically studied for ER. Our goal is to systematically explore the benefits and limitations of a wide range of DA methods for ER. To this purpose, we develop a DADER (Domain Adaptation for Deep Entity Resolution) framework that significantly advances ER in applying DA. We define a space of design solutions for the three modules of DADER, namely Feature Extractor, Matcher, and Feature Aligner. We conduct so far the most comprehensive experimental study to explore the design space and compare different choices of DA for ER. We provide guidance for selecting appropriate design solutions based on extensive experiments.

In this paper we propose a mew greatly scheduling algorithm for crossbar terabit routers with virtual output queuing (VOQ) at input ports. The proposed global and dynamic round-robin (GDRR) scheduler is pipelined scheduler. Our empirical studies show that: 1) the scheduler provides minimum service rate (MSR) guarantees for high-priority confirming traffic, 2) by using the global round-robin method available bandwidth at any output port is fairly shared among input ports (the input port fairness), and 3) by using the dynamic round-robin method available bandwidth at any input port is fairly shared among VOQs (the output port fairness). Increasing the number of scheduling modules linearly increases the latency of the scheduler, while increasing the number of supported traffic classes does not affect its design.

This paper proposes a growable switch architecture with multiple modules comprising a CORE module to support highspeed switching and EDGE modules with large buffers, providing a large number of traffic classes. For open-loop traffic such as CBR and VBR, the proposed switch uses a weighted fair queueing scheduler at each EDGE buffer module based on the equivalent bandwidth allocated by the call admission control (CAC). On the other hand, for best effort class services (e.g., ABR and UBR), closed-loop control using adaptive rate control at the actual source and at the virtual source queue of the input EDGE module is proposed to avoid internal congestion and to achieve fair throughput performance among the flows competing on the same link. Simulation results show that the proposed architecture achieves ATM switches having hundreds of Gbps capacity, while providing a large number of QoS classes with high throughput and fair sharing of bandwidth by all flows.