219 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
Brainformers: Trading Simplicity for Efficiency
Transformers are central to recent successes in natural language processing
and computer vision. Transformers have a mostly uniform backbone where layers
alternate between feed-forward and self-attention in order to build a deep
network. Here we investigate this design choice and find that more complex
blocks that have different permutations of layer primitives can be more
efficient. Using this insight, we develop a complex block, named Brainformer,
that consists of a diverse sets of layers such as sparsely gated feed-forward
layers, dense feed-forward layers, attention layers, and various forms of layer
normalization and activation functions. Brainformer consistently outperforms
the state-of-the-art dense and sparse Transformers, in terms of both quality
and efficiency. A Brainformer model with 8 billion activated parameters per
token demonstrates 2x faster training convergence and 5x faster step time
compared to its GLaM counterpart. In downstream task evaluation, Brainformer
also demonstrates a 3% higher SuperGLUE score with fine-tuning compared to GLaM
with a similar number of activated parameters. Finally, Brainformer largely
outperforms a Primer dense model derived with NAS with similar computation per
token on fewshot evaluations
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be
utilized to add learnable parameters to Large Language Models (LLMs) without
increasing inference cost. Instruction tuning is a technique for training LLMs
to follow instructions. We advocate combining these two approaches, as we find
that MoE models benefit more from instruction tuning than dense models. In
particular, we conduct empirical studies across three experimental setups: (i)
Direct finetuning on individual downstream tasks devoid of instruction tuning;
(ii) Instructiontuning followed by in-context few-shot or zero-shot
generalization on downstream tasks; and (iii) Instruction tuning supplemented
by further finetuning on individual downstream tasks. In the first scenario,
MoE models overall underperform dense models of identical computational
capacity. This narrative, however, dramatically changes with the introduction
of instruction tuning (second and third scenario), used independently or in
conjunction with task-specific finetuning. Our most powerful model,
FLAN-MOE-32B, surpasses the performance of FLAN-PALM-62B on four benchmark
tasks, while using only a third of the FLOPs. The advancements embodied
byFLAN-MOE inspire a reevaluation of the design principles of large-scale,
high-performance language models in the framework of task-agnostic learning.Comment: Preprin
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