237 research outputs found
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
Axis-symmetric Onsager Clustered States of Point Vortices in a Bounded Domain
We study axis-symmetric Onsager clustered states of a neutral point vortex
system confined to a two-dimensional disc. Our analysis is based on the mean
field of bounded point vortices in the microcanonical ensemble. The clustered
vortex states are specified by the inverse temperature and the rotation
frequency , which are the conjugate variables of energy and angular
momentum . The formation of the axis-symmetric clustered vortex states
(azimuthal angle independent) involves the separating of vortices with opposite
circulation and the clustering of vortices with same circulation around origin
and edge. The state preserves symmetry and breaks
symmetry. We find that, near the uniform state, the rotation free state
() emerges at particular values of and . At large
energies, we obtain asymptotically exact vortex density distributions, whose
validity condition gives rise the lower bound of for the rotation free
states. Noticeably, the obtained vortex density distribution near the edge at
large energies provides a novel exact vortex density distribution for the
corresponding chiral vortex system.Comment: 6 pages, 4 figure
FTA: Stealthy and Robust Backdoor Attack with Flexible Trigger on Federated Learning
Current backdoor attacks against federated learning (FL) strongly rely on
universal triggers or semantic patterns, which can be easily detected and
filtered by certain defense mechanisms such as norm clipping, comparing
parameter divergences among local updates. In this work, we propose a new
stealthy and robust backdoor attack with flexible triggers against FL defenses.
To achieve this, we build a generative trigger function that can learn to
manipulate the benign samples with an imperceptible flexible trigger pattern
and simultaneously make the trigger pattern include the most significant hidden
features of the attacker-chosen label. Moreover, our trigger generator can keep
learning and adapt across different rounds, allowing it to adjust to changes in
the global model. By filling the distinguishable difference (the mapping
between the trigger pattern and target label), we make our attack naturally
stealthy. Extensive experiments on real-world datasets verify the effectiveness
and stealthiness of our attack compared to prior attacks on decentralized
learning framework with eight well-studied defenses
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on
neuromorphic chips with high energy efficiency by introducing neural dynamics
and spike properties. As the emerging spiking deep learning paradigm attracts
increasing interest, traditional programming frameworks cannot meet the demands
of the automatic differentiation, parallel computation acceleration, and high
integration of processing neuromorphic datasets and deployment. In this work,
we present the SpikingJelly framework to address the aforementioned dilemma. We
contribute a full-stack toolkit for pre-processing neuromorphic datasets,
building deep SNNs, optimizing their parameters, and deploying SNNs on
neuromorphic chips. Compared to existing methods, the training of deep SNNs can
be accelerated , and the superior extensibility and flexibility of
SpikingJelly enable users to accelerate custom models at low costs through
multilevel inheritance and semiautomatic code generation. SpikingJelly paves
the way for synthesizing truly energy-efficient SNN-based machine intelligence
systems, which will enrich the ecology of neuromorphic computing.Comment: Accepted in Science Advances
(https://www.science.org/doi/10.1126/sciadv.adi1480
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Multi-object tracking in traffic videos is a crucial research area, offering
immense potential for enhancing traffic monitoring accuracy and promoting road
safety measures through the utilisation of advanced machine learning
algorithms. However, existing datasets for multi-object tracking in traffic
videos often feature limited instances or focus on single classes, which cannot
well simulate the challenges encountered in complex traffic scenarios. To
address this gap, we introduce TrafficMOT, an extensive dataset designed to
encompass diverse traffic situations with complex scenarios. To validate the
complexity and challenges presented by TrafficMOT, we conducted comprehensive
empirical studies using three different settings: fully-supervised,
semi-supervised, and a recent powerful zero-shot foundation model Tracking
Anything Model (TAM). The experimental results highlight the inherent
complexity of this dataset, emphasising its value in driving advancements in
the field of traffic monitoring and multi-object tracking.Comment: 17 pages, 7 figure
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
Histopathological tissue classification is a fundamental task in
computational pathology. Deep learning-based models have achieved superior
performance but centralized training with data centralization suffers from the
privacy leakage problem. Federated learning (FL) can safeguard privacy by
keeping training samples locally, but existing FL-based frameworks require a
large number of well-annotated training samples and numerous rounds of
communication which hinder their practicability in the real-world clinical
scenario. In this paper, we propose a universal and lightweight federated
learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve
superior classification performance with limited training samples and only
one-round communication. By simply associating a pre-trained deep learning
feature extractor, a fast and lightweight broad learning inference system and a
classical federated aggregation approach, FedDBL can dramatically reduce data
dependency and improve communication efficiency. Five-fold cross-validation
demonstrates that FedDBL greatly outperforms the competitors with only
one-round communication and limited training samples, while it even achieves
comparable performance with the ones under multiple-round communications.
Furthermore, due to the lightweight design and one-round communication, FedDBL
reduces the communication burden from 4.6GB to only 276.5KB per client using
the ResNet-50 backbone at 50-round training. Since no data or deep model
sharing across different clients, the privacy issue is well-solved and the
model security is guaranteed with no model inversion attack risk. Code is
available at https://github.com/tianpeng-deng/FedDBL
- …