pwc-archive/papers-with-abstracts huggingface dataset:
Robotic Tracking Control with Kernel Trick-based Reinforcement Learning
Authors: Yazhou Hu, 1 Wenxue Wang† 2, Hao Liu3 and Lianqing Liu 2
Date: 2019-11-04 00:00:00
Proceeding: Conference 2019 11
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, reinforcement learning has been
developed dramatically and is widely used to solve control
problems, e.g., playing games. However, there are still some
problems for reinforcement learning to perform robotic control
tasks. Fortunately, the kernel trick-based methods provide a
chance to deal with those challenges. This work aims at developing a kernel trick-based learning control method to carry
out robotic tracking control tasks. A reward system, in this
work, is presented in order to speed up the learning processes.
And then, a kernel trick-based reinforcement learning tracking
controller is presented to perform tracking control tasks on a
robotic manipulator system. To evaluate the policy and assist
the reward system to accelerate the speed of finding the optimal
control policy, a critic system is introduced. Finally, from
the comparison with the benchmark, the simulation results
illustrate that our algorithm has faster convergence rate and can
execute tracking control tasks effectively, the reward function
and the critic system proposed in this work is efficient.
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model
Authors: Yazhou Hu, Wenxue Wang, Hao liu, and Lianqing Liu, Member, IEEE
Date: 2020-01-09 00:00:00
Proceeding: TRANSACTION 2020 1
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is an efficient learning
approach to solving control problems for a robot by interacting
with the environment to acquire the optimal control policy.
However, there are many challenges for RL to execute continuous
control tasks. In this article, without the need to know and
learn the dynamic model of a robotic manipulator, a kernelbased dynamic model for RL is proposed. In addition, a new
tuple is formed through kernel function sampling to describe a
robotic RL control problem. In this algorithm, a reward function
is defined according to the features of tracking control in order to
speed up the learning process, and then an RL tracking controller
with a kernel-based transition dynamic model is proposed.
Finally, a critic system is presented to evaluate the policy whether
it is good or bad to the RL control tasks. The simulation results
illustrate that the proposed method can fulfill the robotic tracking
tasks effectively and achieve similar and even better tracking
performance with much smaller inputs of force/torque compared
with other learning algorithms, demonstrating the effectiveness
and efficiency of the proposed RL algorithm.
Index Terms— Kernel function, reinforcement learning (RL),
reward function, robotics tracking control.
Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling
Authors: Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan, Frank Rodriz, Rose Yu
Date: 2023-03-17 00:00:00

Understanding player shooting profiles is an essential part of basketball analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive gameplans from their opponents; understanding where their players are most comfortable can lead them to developing more effective offensive strategies. An automatic tool that can provide these performance profiles in a timely manner can become invaluable for coaches to maximize both the effectiveness of their game plan as well as the time dedicated to practice and other related activities. Additionally, basketball is dictated by many variables, such as playstyle and game dynamics, that can change the flow of the game and, by extension, player performance profiles. It is crucial that the performance profiles can reflect the diverse playstyles, as well as the fast-changing dynamics of the game. We present a tool that can visualize player performance profiles in a timely manner while taking into account factors such as play-style and game dynamics. Our approach generates interpretable heatmaps that allow us to identify and analyze how non-spatial factors, such as game dynamics or playstyle, affect player performance profiles.
Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
Authors: Masakazu Ohno, Riki Ukyo, Tatsuya Amano, Hamada Rizk, Hirozumi Yamaguchi
Date: 2023-03-17 00:00:00
Tasks: Metric Learning, Multi-Object Tracking, Privacy Preserving

The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tracking systems in large areas. Although the ubiquity of camera-based systems, they are not a preferable solution due to the vulnerability of leaking the privacy of pedestrians. In this paper, we introduce a novel privacy-preserving system for pedestrian tracking in smart environments using multiple distributed LiDARs of non-overlapping views. The system is designed to leverage LiDAR devices to track pedestrians in partially covered areas due to practical constraints, e.g., occlusion or cost. Therefore, the system uses the point cloud captured by different LiDARs to extract discriminative features that are used to train a metric learning model for pedestrian matching purposes. To boost the system's robustness, we leverage a probabilistic approach to model and adapt the dynamic mobility patterns of individuals and thus connect their sub-trajectories. We deployed the system in a large-scale testbed with 70 colorless LiDARs and conducted three different experiments. The evaluation result at the entrance hall confirms the system's ability to accurately track the pedestrians with a 0.98 F-measure even with zero-covered areas. This result highlights the promise of the proposed system as the next generation of privacy-preserving tracking means in smart environments.
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing
Authors: Jinggang Chen, Xiaoyang Qu, Junjie Li, Jianzong Wang, Jiguang Wan, Jing Xiao
Date: 2023-03-17 00:00:00
Tasks: Out of Distribution (OOD) Detection

Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data. Previous progress has introduced various approaches where the in-distribution training data and even several OOD examples are prerequisites. However, due to privacy and security, auxiliary data tends to be impractical in a real-world scenario. In this paper, we propose a data-free method without training on natural data, called Class-Conditional Impressions Reappearing (C2IR), which utilizes image impressions from the fixed model to recover class-conditional feature statistics. Based on that, we introduce Integral Probability Metrics to estimate layer-wise class-conditional deviations and obtain layer weights by Measuring Gradient-based Importance (MGI). The experiments verify the effectiveness of our method and indicate that C2IR outperforms other post-hoc methods and reaches comparable performance to the full access (ID and OOD) detection method, especially in the far-OOD dataset (SVHN).
Sematch: Semantic Entity Search from Knowledge Graph
Authors: Ganggao Zhu and Carlos A. Iglesias
Date: 2015-06-01 00:00:00
Proceeding: Joint Proceedings of the 1st International Workshop on Summarizing and Presenting Entities and Ontologies and the 3rd International Workshop on Human Semantic Web Interfaces (SumPre 2015, HSWI 2015) co-located with the 12th Extended Semantic Web Conferen | SumPre 2015 - 1st International Workshop on Summarizing and Presenting Entities and Ontologies | 1/06/2015 | Portoroz, Slovenia 2015 6
Tasks: Entity Linking, Natural Language Queries, Semantic Similarity, Semantic Textual Similarity

As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the underlying ontology of Linked Data knowledge bases. In this article, we propose the Sematch framework for entity search in the knowledge graph that combines natural language query processing, entity linking, entity type linking and semantic similarity based query expansion. The system has
been validated in a dataset and a prototype has been developed that translates natural language queries into SPARQL.
SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection
Authors: Yuhang Wang, Li Wang, Yanjie Yang, Tao Lian
Date: 2020-10-03 00:00:00
Proceeding: Expert Systems with Applications 2020 10
Tasks: Fake News Detection, Sentence

The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods.
Synthetic Traffic Generation with Wasserstein Generative Adversarial Networks
Authors: Chao–Lun Wu, Yu–Ying Chen, Po–Yu Chou, Chih–Yu Wang
Date: 2022-12-05 00:00:00
Proceeding: IEEE Global Communications Conference 2022 12
Tasks: Intelligent Communication, Synthetic Data Generation

Network traffic data are critical for network research. With the help of synthetic traffic, researchers can readily generate data for network simulation and performance evaluation. However, the state-of-the-art traffic generators are either too simple to generate realistic traffic or require the implementation of original applications and user operations. We propose Synthetic PAcket Traffic Generative Adversarial Networks (SPATGAN) that are capable of generating synthetic traffic. The framework includes a server agent and a client agent, which transmit synthetic packets to each other and take the opponent's synthetic packets as conditional labels for the built-in Timing Synthesis Generative Adversarial Networks (TSynGAN) and a Packet Synthesis Generative Adversarial Networks (PSynGAN) to generate synthetic traffic. The evaluations demonstrate that the proposed framework can generate traffic whose distribution resembles real traffic distribution.
Data-centric Artificial Intelligence: A Survey
Authors: Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, Xia Hu
Date: 2023-03-17 00:00:00
Tasks: Survey

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI
Hybrid Classic-Quantum Computing for Staging of Invasive Ductal Carcinoma of Breast
Authors: Vicente Moret-Bonillo, Eduardo Mosqueira-Rey, Samuel Magaz-Romero, Diego Alvarez-Estevez
Date: 2023-03-17 00:00:00

Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do not provide with optimal solutions. For this, one promising option could be the use of the concepts and ideas of Quantum Mechanics, for the construction of quantum-based Artificial Intelligence systems. From a hybrid classical-quantum perspective, this article deals with the application of quantum computing techniques for the staging of Invasive Ductal Carcinoma of the breast. It includes: (1) a general explanation of a classical, and well-established, approach for medical reasoning, (2) a description of the clinical problem, (3) a conceptual model for staging invasive ductal carcinoma, (4) some basic notions about Quantum Rule-Based Systems, (5) a step-by-step explanation of the proposed approach for quantum staging of the invasive ductal carcinoma, and (6) the results obtained after running the quantum system on a significant number of use cases. A detailed discussion is also provided at the end of this paper.
Distill n' Explain: explaining graph neural networks using simple surrogates
Authors: Tamara Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri Souza
Date: 2023-03-17 00:00:00
Tasks: Knowledge Distillation

Explaining node predictions in graph neural networks (GNNs) often boils down to finding graph substructures that preserve predictions. Finding these structures usually implies back-propagating through the GNN, bonding the complexity (e.g., number of layers) of the GNN to the cost of explaining it. This naturally begs the question: Can we break this bond by explaining a simpler surrogate GNN? To answer the question, we propose Distill n' Explain (DnX). First, DnX learns a surrogate GNN via knowledge distillation. Then, DnX extracts node or edge-level explanations by solving a simple convex program. We also propose FastDnX, a faster version of DnX that leverages the linear decomposition of our surrogate model. Experiments show that DnX and FastDnX often outperform state-of-the-art GNN explainers while being orders of magnitude faster. Additionally, we support our empirical findings with theoretical results linking the quality of the surrogate model (i.e., distillation error) to the faithfulness of explanations.
Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data
Authors: Carlos Gemmell, Jeffrey Dalton
Date: 2023-03-17 00:00:00
Tasks: Question Answering

Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data. Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resulting in information loss as table size increases. In this paper we propose ToolWriter to generate query specific programs and detect when to apply them to transform tables and align them with the TQA model's capabilities. Focusing ToolWriter to generate row-filtering tools improves the state-of-the-art for WikiTableQuestions and WikiSQL with the most performance gained on long tables. By investigating headroom, our work highlights the broader potential for programmatic tools combined with neural components to manipulate large amounts of structured data.
She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models
Authors: Christoph Treude, Hideaki Hata
Date: 2023-03-17 00:00:00

Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, are affected by implicit gender bias embedded in large language models. We systematically translated each task from English into a genderless language and back, and investigated the pronouns associated with each task. Based on translating each task 100 times in different permutations, we identify a significant disparity in the gendered pronoun associations with different tasks. Specifically, requirements elicitation was associated with the pronoun "he" in only 6% of cases, while testing was associated with "he" in 100% of cases. Additionally, tasks related to helping others had a 91% association with "he" while the same association for tasks related to asking coworkers was only 52%. These findings reveal a clear pattern of gender bias related to software development tasks and have important implications for addressing this issue both in the training of large language models and in broader society.
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Authors: Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock
Date: 2023-03-17 00:00:00

We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.
$\textit{Clingraph}$: A System for ASP-based Visualization
Authors: Susana Hahn, Orkunt Sabuncu, Torsten Schaub, Tobias Stolzmann
Date: 2023-03-17 00:00:00

We present the ASP-based visualization tool $\textit{clingraph}$ which aims at visualizing various concepts of ASP by means of ASP itself. This idea traces back to the $\textit{aspviz}$ tool and $\textit{clingraph}$ redevelops and extends it in the context of modern ASP systems. More precisely, $\textit{clingraph}$ takes graph specifications in terms of ASP facts and hands them over to the graph visualization system $\textit{graphviz}$. The use of ASP provides a great interface between logic programs and/or answer sets and their visualization. Also, $\textit{clingraph}$ offers a $\textit{python}$ API that extends this ease of interfacing to $\textit{clingo}$'s API, and in turn to connect and monitor various aspects of the solving process.
A multidomain relational framework to guide institutional AI research and adoption
Authors: Vincent J. Straub, Deborah Morgan, Youmna Hashem, John Francis, Saba Esnaashari, Jonathan Bright
Date: 2023-03-17 00:00:00

Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace. Yet, most research and policy efforts aimed at understanding the implications of adopting AI tend to prioritize only a handful of ideas; they do not fully connect all the different perspectives and topics that are potentially relevant. In this position paper, we contend that this omission stems, in part, from what we call the relational problem in socio-technical discourse: fundamental ontological issues have not yet been settled--including semantic ambiguity, a lack of clear relations between concepts and differing standard terminologies. This contributes to the persistence of disparate modes of reasoning to assess institutional AI systems, and the prevalence of conceptual isolation in the fields that study them including ML, human factors, social science and policy. After developing this critique, we offer a way forward by proposing a simple policy and research design tool in the form of a conceptual framework to organize terms across fields--consisting of three horizontal domains for grouping relevant concepts and related methods: Operational, Epistemic, and Normative. We first situate this framework against the backdrop of recent socio-technical discourse at two premier academic venues, AIES and FAccT, before illustrating how developing suitable metrics, standards, and mechanisms can be aided by operationalizing relevant concepts in each of these domains. Finally, we outline outstanding questions for developing this relational approach to institutional AI research and adoption.