21,260 research outputs found
Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection
Spiking Neural Networks (SNNs) have garnered widespread interest for their
energy efficiency and brain-inspired event-driven properties. While recent
methods like Spiking-YOLO have expanded the SNNs to more challenging object
detection tasks, they often suffer from high latency and low detection
accuracy, making them difficult to deploy on latency sensitive mobile
platforms. Furthermore, the conversion method from Artificial Neural Networks
(ANNs) to SNNs is hard to maintain the complete structure of the ANNs,
resulting in poor feature representation and high conversion errors. To address
these challenges, we propose two methods: timesteps compression and
spike-time-dependent integrated (STDI) coding. The former reduces the timesteps
required in ANN-SNN conversion by compressing information, while the latter
sets a time-varying threshold to expand the information holding capacity. We
also present a SNN-based ultra-low latency and high accurate object detection
model (SUHD) that achieves state-of-the-art performance on nontrivial datasets
like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30%
mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS
COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based
object detection model to date that achieves ultra low timesteps to complete
the lossless conversion.Comment: 14 pages, 10 figure
The Perspective of Software Professionals on Algorithmic Racism
Context. Algorithmic racism is the term used to describe the behavior of
technological solutions that constrains users based on their ethnicity. Lately,
various data-driven software systems have been reported to discriminate against
Black people, either for the use of biased data sets or due to the prejudice
propagated by software professionals in their code. As a result, Black people
are experiencing disadvantages in accessing technology-based services, such as
housing, banking, and law enforcement. Goal. This study aims to explore
algorithmic racism from the perspective of software professionals. Method. A
survey questionnaire was applied to explore the understanding of software
practitioners on algorithmic racism, and data analysis was conducted using
descriptive statistics and coding techniques. Results. We obtained answers from
a sample of 73 software professionals discussing their understanding and
perspectives on algorithmic racism in software development. Our results
demonstrate that the effects of algorithmic racism are well-known among
practitioners. However, there is no consensus on how the problem can be
effectively addressed in software engineering. In this paper, some solutions to
the problem are proposed based on the professionals' narratives. Conclusion.
Combining technical and social strategies, including training on structural
racism for software professionals, is the most promising way to address the
algorithmic racism problem and its effects on the software solutions delivered
to our society
Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work
The increasing prevalence of Artificial Intelligence (AI) in safety-critical
contexts such as air-traffic control leads to systems that are practical and
efficient, and to some extent explainable to humans to be trusted and accepted.
The present structured literature analysis examines n = 236 articles on the
requirements for the explainability and acceptance of AI. Results include a
comprehensive review of n = 48 articles on information people need to perceive
an AI as explainable, the information needed to accept an AI, and
representation and interaction methods promoting trust in an AI. Results
indicate that the two main groups of users are developers who require
information about the internal operations of the model and end users who
require information about AI results or behavior. Users' information needs vary
in specificity, complexity, and urgency and must consider context, domain
knowledge, and the user's cognitive resources. The acceptance of AI systems
depends on information about the system's functions and performance, privacy
and ethical considerations, as well as goal-supporting information tailored to
individual preferences and information to establish trust in the system.
Information about the system's limitations and potential failures can increase
acceptance and trust. Trusted interaction methods are human-like, including
natural language, speech, text, and visual representations such as graphs,
charts, and animations. Our results have significant implications for future
human-centric AI systems being developed. Thus, they are suitable as input for
further application-specific investigations of user needs
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
Measuring and evaluating source code similarity is a fundamental software
engineering activity that embraces a broad range of applications, including but
not limited to code recommendation, duplicate code, plagiarism, malware, and
smell detection. This paper proposes a systematic literature review and
meta-analysis on code similarity measurement and evaluation techniques to shed
light on the existing approaches and their characteristics in different
applications. We initially found over 10000 articles by querying four digital
libraries and ended up with 136 primary studies in the field. The studies were
classified according to their methodology, programming languages, datasets,
tools, and applications. A deep investigation reveals 80 software tools,
working with eight different techniques on five application domains. Nearly 49%
of the tools work on Java programs and 37% support C and C++, while there is no
support for many programming languages. A noteworthy point was the existence of
12 datasets related to source code similarity measurement and duplicate codes,
of which only eight datasets were publicly accessible. The lack of reliable
datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm
languages are the main challenges in the field. Emerging applications of code
similarity measurement concentrate on the development phase in addition to the
maintenance.Comment: 49 pages, 10 figures, 6 table
Gloss Attention for Gloss-free Sign Language Translation
Most sign language translation (SLT) methods to date require the use of gloss
annotations to provide additional supervision information, however, the
acquisition of gloss is not easy. To solve this problem, we first perform an
analysis of existing models to confirm how gloss annotations make SLT easier.
We find that it can provide two aspects of information for the model, 1) it can
help the model implicitly learn the location of semantic boundaries in
continuous sign language videos, 2) it can help the model understand the sign
language video globally. We then propose \emph{gloss attention}, which enables
the model to keep its attention within video segments that have the same
semantics locally, just as gloss helps existing models do. Furthermore, we
transfer the knowledge of sentence-to-sentence similarity from the natural
language model to our gloss attention SLT network (GASLT) to help it understand
sign language videos at the sentence level. Experimental results on multiple
large-scale sign language datasets show that our proposed GASLT model
significantly outperforms existing methods. Our code is provided in
\url{https://github.com/YinAoXiong/GASLT}
Detection of Groups with Biased Representation in Ranking
Real-life tools for decision-making in many critical domains are based on
ranking results. With the increasing awareness of algorithmic fairness, recent
works have presented measures for fairness in ranking. Many of those
definitions consider the representation of different ``protected groups'', in
the top- ranked items, for any reasonable . Given the protected groups,
confirming algorithmic fairness is a simple task. However, the groups'
definitions may be unknown in advance. In this paper, we study the problem of
detecting groups with biased representation in the top- ranked items,
eliminating the need to pre-define protected groups. The number of such groups
possible can be exponential, making the problem hard. We propose efficient
search algorithms for two different fairness measures: global representation
bounds, and proportional representation. Then we propose a method to explain
the bias in the representations of groups utilizing the notion of Shapley
values. We conclude with an experimental study, showing the scalability of our
approach and demonstrating the usefulness of the proposed algorithms
Smart Sports Predictions via Hybrid Simulation: NBA Case Study
Increased data availability has stimulated the interest in studying sports
prediction problems via analytical approaches; in particular, with machine
learning and simulation. We characterize several models that have been proposed
in the literature, all of which suffer from the same drawback: they cannot
incorporate rational decision-making and strategies from teams/players
effectively. We tackle this issue by proposing hybrid simulation logic that
incorporates teams as agents, generalizing the models/methodologies that have
been proposed in the past. We perform a case study on the NBA with two goals:
i) study the quality of predictions when using only one predictive variable,
and ii) study how much historical data should be kept to maximize prediction
accuracy. Results indicate that there is an optimal range of data quantity and
that studying what data and variables to include is of extreme importance.Comment: Sent to the Winter Simulation Conference 202
Continual Learning, Fast and Slow
According to the Complementary Learning Systems (CLS)
theory~\cite{mcclelland1995there} in neuroscience, humans do effective
\emph{continual learning} through two complementary systems: a fast learning
system centered on the hippocampus for rapid learning of the specifics,
individual experiences; and a slow learning system located in the neocortex for
the gradual acquisition of structured knowledge about the environment.
Motivated by this theory, we propose \emph{DualNets} (for Dual Networks), a
general continual learning framework comprising a fast learning system for
supervised learning of pattern-separated representation from specific tasks and
a slow learning system for representation learning of task-agnostic general
representation via Self-Supervised Learning (SSL). DualNets can seamlessly
incorporate both representation types into a holistic framework to facilitate
better continual learning in deep neural networks. Via extensive experiments,
we demonstrate the promising results of DualNets on a wide range of continual
learning protocols, ranging from the standard offline, task-aware setting to
the challenging online, task-free scenario. Notably, on the
CTrL~\cite{veniat2020efficient} benchmark that has unrelated tasks with vastly
different visual images, DualNets can achieve competitive performance with
existing state-of-the-art dynamic architecture
strategies~\cite{ostapenko2021continual}. Furthermore, we conduct comprehensive
ablation studies to validate DualNets efficacy, robustness, and scalability.
Code will be made available at \url{https://github.com/phquang/DualNet}.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0017
HistRED: A Historical Document-Level Relation Extraction Dataset
Despite the extensive applications of relation extraction (RE) tasks in
various domains, little has been explored in the historical context, which
contains promising data across hundreds and thousands of years. To promote the
historical RE research, we present HistRED constructed from Yeonhaengnok.
Yeonhaengnok is a collection of records originally written in Hanja, the
classical Chinese writing, which has later been translated into Korean. HistRED
provides bilingual annotations such that RE can be performed on Korean and
Hanja texts. In addition, HistRED supports various self-contained subtexts with
different lengths, from a sentence level to a document level, supporting
diverse context settings for researchers to evaluate the robustness of their RE
models. To demonstrate the usefulness of our dataset, we propose a bilingual RE
model that leverages both Korean and Hanja contexts to predict relations
between entities. Our model outperforms monolingual baselines on HistRED,
showing that employing multiple language contexts supplements the RE
predictions. The dataset is publicly available at:
https://huggingface.co/datasets/Soyoung/HistRED under CC BY-NC-ND 4.0 license
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