obsess
发表于 2025-3-21 18:20:24
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隼鹰
发表于 2025-3-21 23:03:02
Learning the Structures of Online Asynchronous Conversations online via instant messaging applications on personal computers or mobile devices. Since these conversations are sequentially organized, which fails to show the logical relations between messages, they are called asynchronous conversations in previous studies. Unfortunately, the sequential layouts
女上瘾
发表于 2025-3-22 03:11:44
A Question Routing Technique Using Deep Neural Network for Communities of Question Answering asked by their users. Although CQA frameworks receive new questions in a linear rate, the rate of the unanswered questions increases in an exponential way. This variation eventually compromise effectiveness of the CQA frameworks as knowledge sharing platforms. The main cause for this challenge is t
APEX
发表于 2025-3-22 06:51:06
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enmesh
发表于 2025-3-22 11:51:39
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legislate
发表于 2025-3-22 13:34:44
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legislate
发表于 2025-3-22 20:47:16
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纠缠
发表于 2025-3-23 01:01:49
An Adaptive Data Partitioning Scheme for Accelerating Exploratory Spark SQL Queries next queries. Thus, data used in the previous queries are often reused, at least in part, in the next queries. This fact may be used to accelerate queries with data partitioning, a widely used technique that enables skipping the irrelevant data for better I/O performance. For getting effective part
tic-douloureux
发表于 2025-3-23 03:58:29
Semi-Supervised Network Embeddingachine learning tasks such as node classification, link prediction, and social recommendation. Current popular network embedding methods normally first transform the network into a set of node sequences, and then input them into an unsupervised feature learning model to generate a distributed repres
斜
发表于 2025-3-23 06:23:52
CirE: Circular Embeddings of Knowledge Graphscontinuous vector spaces and then constructs . triples. However, KG embedding models are sensitive to infrequent and uncertain objects. Furthermore, there is a contradiction between learning ability and learning cost. To this end, we propose circular embeddings (CirE) to learn representations of ent