12,120 research outputs found
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders,
have attracted a lot of research interest; however, these methods have limited
applications in the real world due to the challenge of incorporating human
knowledge. This work presents a semi-supervised neural topic modeling method,
vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and
optimal transport. When a few keywords per topic are provided, vONTSS in the
semi-supervised setting generates potential topics and optimizes topic-keyword
quality and topic classification. Experiments show that vONTSS outperforms
existing semi-supervised topic modeling methods in classification accuracy and
diversity. vONTSS also supports unsupervised topic modeling. Quantitative and
qualitative experiments show that vONTSS in the unsupervised setting
outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered
and coherent topics on benchmark datasets. It is also much faster than the
state-of-the-art weakly supervised text classification method while achieving
similar classification performance. We further prove the equivalence of optimal
transport loss and cross-entropy loss at the global minimum.Comment: 24 pages, 12 figures, ACL findings 202
Towards A Practical High-Assurance Systems Programming Language
Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation.
Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code.
To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process
Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer
How to obtain informative representations of transactions and then perform
the identification of fraudulent transactions is a crucial part of ensuring
financial security. Recent studies apply Graph Neural Networks (GNNs) to the
transaction fraud detection problem. Nevertheless, they encounter challenges in
effectively learning spatial-temporal information due to structural
limitations. Moreover, few prior GNN-based detectors have recognized the
significance of incorporating global information, which encompasses similar
behavioral patterns and offers valuable insights for discriminative
representation learning. Therefore, we propose a novel heterogeneous graph
neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for
transaction fraud detection problems. Specifically, we design a temporal
encoding strategy to capture temporal dependencies and incorporate it into the
graph neural network framework, enhancing spatial-temporal information modeling
and improving expressive ability. Furthermore, we introduce a transformer
module to learn local and global information. Pairwise node-node interactions
overcome the limitation of the GNN structure and build up the interactions with
the target node and long-distance ones. Experimental results on two financial
datasets compared to general GNN models and GNN-based fraud detectors
demonstrate that our proposed method STA-GT is effective on the transaction
fraud detection task
Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries
Although existing techniques have proposed automated approaches to alleviate
the path explosion problem of symbolic execution, users still need to optimize
symbolic execution by applying various searching strategies carefully. As
existing approaches mainly support only coarse-grained global searching
strategies, they cannot efficiently traverse through complex code structures.
In this paper, we propose Eunomia, a symbolic execution technique that allows
users to specify local domain knowledge to enable fine-grained search. In
Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint
local searching strategies to different parts of the target program. To further
optimize local searching strategies, we design an interval-based algorithm that
automatically isolates the context of variables for different local searching
strategies, avoiding conflicts between local searching strategies for the same
variable. We implement Eunomia as a symbolic execution platform targeting
WebAssembly, which enables us to analyze applications written in various
languages (like C and Go) but can be compiled into WebAssembly. To the best of
our knowledge, Eunomia is the first symbolic execution engine that supports the
full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated
microbenchmark suite for symbolic execution and six real-world applications.
Our evaluation shows that Eunomia accelerates bug detection in real-world
applications by up to three orders of magnitude. According to the results of a
comprehensive user study, users can significantly improve the efficiency and
effectiveness of symbolic execution by writing a simple and intuitive Aes
script. Besides verifying six known real-world bugs, Eunomia also detected two
new zero-day bugs in a popular open-source project, Collections-C.Comment: Accepted by ACM SIGSOFT International Symposium on Software Testing
and Analysis (ISSTA) 202
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
RSGPT: A Remote Sensing Vision Language Model and Benchmark
The emergence of large-scale large language models, with GPT-4 as a prominent
example, has significantly propelled the rapid advancement of artificial
general intelligence and sparked the revolution of Artificial Intelligence 2.0.
In the realm of remote sensing (RS), there is a growing interest in developing
large vision language models (VLMs) specifically tailored for data analysis in
this domain. However, current research predominantly revolves around visual
recognition tasks, lacking comprehensive, large-scale image-text datasets that
are aligned and suitable for training large VLMs, which poses significant
challenges to effectively training such models for RS applications. In computer
vision, recent research has demonstrated that fine-tuning large vision language
models on small-scale, high-quality datasets can yield impressive performance
in visual and language understanding. These results are comparable to
state-of-the-art VLMs trained from scratch on massive amounts of data, such as
GPT-4. Inspired by this captivating idea, in this work, we build a high-quality
Remote Sensing Image Captioning dataset (RSICap) that facilitates the
development of large VLMs in the RS field. Unlike previous RS datasets that
either employ model-generated captions or short descriptions, RSICap comprises
2,585 human-annotated captions with rich and high-quality information. This
dataset offers detailed descriptions for each image, encompassing scene
descriptions (e.g., residential area, airport, or farmland) as well as object
information (e.g., color, shape, quantity, absolute position, etc). To
facilitate the evaluation of VLMs in the field of RS, we also provide a
benchmark evaluation dataset called RSIEval. This dataset consists of
human-annotated captions and visual question-answer pairs, allowing for a
comprehensive assessment of VLMs in the context of RS
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A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics. Sentiment analysis is a typical example of such domains. In the last few years, by leveraging the modeling power of quantum probability (a non-classical probability stemming from quantum mechanics methodology) and deep neural networks, a range of novel quantum-cognitively inspired models for sentiment analysis have emerged and performed well. This survey presents a timely overview of the latest developments in this fascinating cross-disciplinary area. We first provide a background of quantum probability and quantum cognition at a theoretical level, analyzing their advantages over classical theories in modeling the cognitive aspects of sentiment analysis. Then, recent quantum-cognitively inspired models are introduced and discussed in detail, focusing on how they approach the key challenges of the sentiment analysis task. Finally, we discuss the limitations of the current research and highlight future research directions
BOLD: A Benchmark for Linked Data User Agents and a Simulation Framework for Dynamic Linked Data Environments
The paper presents the BOLD (Buildings on Linked Data) benchmark for Linked
Data agents, next to the framework to simulate dynamic Linked Data
environments, using which we built BOLD. The BOLD benchmark instantiates the
BOLD framework by providing a read-write Linked Data interface to a smart
building with simulated time, occupancy movement and sensors and actuators
around lighting. On the Linked Data representation of this environment, agents
carry out several specified tasks, such as controlling illumination. The
simulation environment provides means to check for the correct execution of the
tasks and to measure the performance of agents. We conduct measurements on
Linked Data agents based on condition-action rules
A Parent's Autoethnography: Examining My Experiences and Identity as Parent, Educator, and Researcher While Teaching Literacy to My Adolescent Sons Who Have Autism and Use Augmentative and Alternative Communication
This autoethnography was completed from my unique perspective as a mother to two adolescent
sons with autism spectrum disorder (ASD) who have complex communication needs and use
augmentative and alternative communication (AAC) to communicate. Although literacy is a
human right (Ontario Human Rights Commission, 2022a), it often has been overlooked in my
sons’ self-contained classrooms in high school. As my sons’ parent and educator, I gathered my
reflections, observations, descriptions, journals, lesson plans, and artifacts to examine the
experiences I encountered in developing their literacy. Initially, I conducted a pilot project based
on Erickson and Koppenhaver’s (2007) Children With Disabilities: Reading and Writing the
Four Blocks® Way, the results of which guided my planning in teaching literacy with an
adaptation of the more recent Comprehensive Literacy for All: Teaching Students With
Significant Disabilities to Read and Write (Erickson & Koppenhaver, 2020). I coded by hand
each line of the collected data to extract categories and then streamline these into the meaningful
themes to respond to my two research questions: (a) What are the experiences of a parent
educator who has been teaching literacy awareness and skills to her adolescent sons who both
have autism and use AAC devices? (b) Does the experience shape her identity as a parent,
educator, and researcher? Thematic findings pertaining to the first question revealed experiences
related to planning and questioning and my own transformational learning and mindshift.
Thematic findings related to the second question include: Parental concerns; Educator:
advocating and imposter syndrome; Researcher: Lesson planning and questioning; and
Transformational learning and mindshift. Findings are discussed in light of the literature on
experiences of parents as educators of children with exceptionalities. The study also presents
implications for theory, practice, and research, as well as limitations and future directions
Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System
In the realm of search systems, multi-stage cascade architecture is a
prevalent method, typically consisting of sequential modules such as matching,
pre-ranking, and ranking. It is generally acknowledged that the model used in
the pre-ranking stage must strike a balance between efficacy and efficiency.
Thus, the most commonly employed architecture is the representation-focused
vector product based model. However, this architecture lacks effective
interaction between the query and document, resulting in a reduction in the
effectiveness of the search system. To address this issue, we present a novel
pre-ranking framework called RankDFM. Our framework leverages DeepFM as the
backbone and employs a pairwise training paradigm to learn the ranking of
videos under a query. The capability of RankDFM to cross features provides
significant improvement in offline and online A/B testing performance.
Furthermore, we introduce a learnable feature selection scheme to optimize the
model and reduce the time required for online inference, equivalent to a tree
model. Currently, RankDFM has been deployed in the search system of a
shortvideo App, providing daily services to hundreds of millions users
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