269 research outputs found
Double Permutation Equivariance for Knowledge Graph Completion
This work provides a formalization of Knowledge Graphs (KGs) as a new class
of graphs that we denote doubly exchangeable attributed graphs, where node and
pairwise (joint 2-node) representations must be equivariant to permutations of
both node ids and edge (& node) attributes (relations & node features).
Double-permutation equivariant KG representations open a new research direction
in KGs. We show that this equivariance imposes a structural representation of
relations that allows neural networks to perform complex logical reasoning
tasks in KGs. Finally, we introduce a general blueprint for such equivariant
representations and test a simple GNN-based double-permutation equivariant
neural architecture that achieve state-of-the-art Hits@10 test accuracy in the
WN18RR, FB237 and NELL995 inductive KG completion tasks, and can accurately
perform logical reasoning tasks that no existing methods can perform, to the
best of our knowledge
SignReLU neural network and its approximation ability
Deep neural networks (DNNs) have garnered significant attention in various
fields of science and technology in recent years. Activation functions define
how neurons in DNNs process incoming signals for them. They are essential for
learning non-linear transformations and for performing diverse computations
among successive neuron layers. In the last few years, researchers have
investigated the approximation ability of DNNs to explain their power and
success. In this paper, we explore the approximation ability of DNNs using a
different activation function, called SignReLU. Our theoretical results
demonstrate that SignReLU networks outperform rational and ReLU networks in
terms of approximation performance. Numerical experiments are conducted
comparing SignReLU with the existing activations such as ReLU, Leaky ReLU, and
ELU, which illustrate the competitive practical performance of SignReLU
AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts
In the past few years, several attacks against the vulnerabilities of EOSIO
smart contracts have caused severe financial losses to this prevalent
blockchain platform. As a lightweight test-generation approach, grey-box
fuzzing can open up the possibility of improving the security of EOSIO smart
contracts. However, developing a practical grey-box fuzzer for EOSIO smart
contracts from scratch is time-consuming and requires a deep understanding of
EOSIO internals. In this work, we proposed AntFuzzer, the first highly
extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer
implements a novel approach that interfaces AFL to conduct AFL-style grey-box
fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools,
AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an
improvement in code coverage on 37.5% of smart contracts in our benchmark
dataset. AntFuzzer provides unified interfaces for users to easily develop new
detection plugins for continually emerging vulnerabilities. We have implemented
6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart
contracts. In our large-scale fuzzing experiments on 4,616 real-world smart
contracts, AntFuzzer successfully detected 741 vulnerabilities. The results
demonstrate the effectiveness and efficiency of AntFuzzer and our detection p
TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition
Recent achievements in language models have showcased their extraordinary
capabilities in bridging visual information with semantic language
understanding. This leads us to a novel question: can language models connect
textual semantics with IoT sensory signals to perform recognition tasks, e.g.,
Human Activity Recognition (HAR)? If so, an intelligent HAR system with
human-like cognition can be built, capable of adapting to new environments and
unseen categories. This paper explores its feasibility with an innovative
approach, IoT-sEnsors-language alignmEnt pre-Training (TENT), which jointly
aligns textual embeddings with IoT sensor signals, including camera video,
LiDAR, and mmWave. Through the IoT-language contrastive learning, we derive a
unified semantic feature space that aligns multi-modal features with language
embeddings, so that the IoT data corresponds to specific words that describe
the IoT data. To enhance the connection between textual categories and their
IoT data, we propose supplementary descriptions and learnable prompts that
bring more semantic information into the joint feature space. TENT can not only
recognize actions that have been seen but also ``guess'' the unseen action by
the closest textual words from the feature space. We demonstrate TENT achieves
state-of-the-art performance on zero-shot HAR tasks using different modalities,
improving the best vision-language models by over 12%.Comment: Preprint manuscript in submissio
A Brief Analysis on the Building of Mental-Health Counselor Team
Mental-health counselors are the main force of mental-health education team in university, and the building of mental-health counselor team also serves as the main content of mental-health education in university. Through the analysis of the necessity and status quo of the building of mental-health counselor team in university, this paper puts forward several opinions and suggestions in order to promote the sustainable development of the professionalization and normalization of mental-health work in university
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