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arXiv 2022年

作者： Fortunato, Santo Newman, M.E.J. United States Luddy School of Informatics Computing and Engineering Indiana University United States Department of Physics Center for the Study of Complex Systems University of Michigan United States

A fundamental technical challenge in the analysis of network data is the automated discovery of communities - groups of nodes that are strongly connected or that share similar features or roles. In this commentary we ...

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Refinement of an Epilepsy Dictionary through Human Annotation of Health-related posts on Instagram

arXiv

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arXiv 2024年

作者： Min, Aehong Wang, Xuan Correia, Rion Brattig Rozum, Jordan Miller, Wendy R. Rocha, Luis M. Donald Bren School of Information & Computer Sciences University of California IrvineCA United States Luddy School of Informatics Computing & Engineering Indiana University BloomingtonIN United States Instituto Gulbenkian de Ciência Oeiras Portugal Dept. of Systems Science & Industrial Engineering Binghamton University BinghamtonNY United States School of Nursing Indiana University IndianapolisIN United States

Objective — To (1) identify health-related terms used on social media posts that do not precisely match the health-related meaning of terms in a biomedical dictionary, (2) decide which terms need to be removed in order to improve the quality of the dictionary in the scope of biomedical text mining tasks, (3) evaluate the effect of removing imprecise terms on such tasks, and (4) discuss how human-centered annotation complements automated annotation in social media mining for biomedical purposes. Materials and Methods — We used a dictionary built from biomedical terminology extracted from various sources such as DrugBank, MedDRA, MedlinePlus, TCMGeneDIT, to tag more than 8 million Instagram posts by users who have mentioned an epilepsy-relevant drug at least once, between 2010 and early 2016. A random sample of 1,771 posts with 2,947 term matches was evaluated by human annotators to identify false-positives. Frequent terms with a high false-positive rate were removed from the dictionary. To study the effect of removing those terms, we constructed knowledge networks using the refined and the original dictionaries and performed an eigenvector-centrality analysis on both networks. OpenAI’s GPT series models were compared against human annotation. Results — Analysis of the estimated false-positive rates of the annotated terms revealed 8 ambiguous terms (plus synonyms) used in Instagram posts, which were removed from the original dictionary. We show that the refined dictionary thus produced leads to a significantly different rank of important terms, as measured by their eigenvector-centrality of the knowledge networks. Furthermore, the most important terms obtained after refinement are of greater medical relevance. In addition, we show that OpenAI’s GPT series models fare worse than human annotators in this task. Discussion — Dictionaries built from traditional clinical terminology are not tailored for social media language and can bias results when used in biomedical infe

关键词： Eigenvalues and eigenfunctions

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A Blockchain Approach to Identifying Compromised Nodes in Collaborative Intrusion Detection systems

A Blockchain Approach to Identifying Compromised Nodes in Co...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Chandralekha Yenugunti Stephen S. Yau School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe USA

ISBN: (数字)9781728166094

ISBN: (纸本)9781728166100

Large organizations have multiple networks that are subject to attacks which can be detected by Intrusion Detection systems. Collaborative Intrusion Detection systems (CIDS) are used for efficient detection of distributed attacks in large networks by having a global view of the attacks in the networks. However, CIDS are vulnerable to various attacks, which compromise some of the nodes of CIDS. The major challenge caused by these attacks on CIDS is due to insider attacks. These insider attacks decrease the mutual trust among the nodes in CIDS, which is required for sharing critical and sensitive alert data. The compromised nodes will further decrease the accuracy of CIDS by generating false positives and false negatives of the traffic classifications. In this paper, an approach based on trust score system is presented to identify and suspend the compromised nodes in CIDS to improve the trust among the nodes for collaboration. This approach is implemented on a private blockchain because private blockchain provides the features to satisfy the accountability, integrity and privacy requirements of CIDS.

关键词： Privacy Intrusion detection Collaboration Blockchain Organizations Big Data

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Mold - A Framework for Entity Extraction and Summarization

Mold - A Framework for Entity Extraction and Summarization

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International Conference on Semantic computing

作者： Sarthak Tiwari Bharat Goel Srividya Bansal School of Computing Informatics Decision Systems Engineering Arizona State University Mesa Arizona

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

With the dawn of Wikis, the collaboration of endless users and the rise of automated agents scraping thorough the internet in search of actionable information, it has become a necessity to create a framework that could serve as a translator between these two parties. Currently, moderators or developers serve this purpose by manually adding the required structure to natural language input while being assisted by one or more assisting applications. In short, the process still requires human intervention and that is what we are aiming to eliminate using the framework suggested in this paper. The framework is divided into two parts, one natural language processing module that uses state-of-the-art Part-of-Speech (PoS) taggers and our own algorithms to convert a natural language text into triples made up of predefined predicates, which are provided to the machine users (autonomous agents). The generated triples are also complemented with their own schema thus enabling machine-based reasoning on the text. The other part i.e. the summarization takes the triples generated by the previous step and creates a natural language text out of it so that the human users of the system could access the information without any knowledge of underlying triples and schema. The framework is trained on a large corpus of English text to optimally find the subject and the object of a given sentence along with the most probable predicate. The predicates will be stored in a separate place in an XML based syntax giving the users the functionality to add/update the schema and predicates as the need arises enabling easy adaptation of the framework to a specific domain.

关键词： Ontologies Natural languages Data mining Information retrieval Resource description framework Blood Semantics

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Ensemble Learning on Deep Neural Networks for Image Caption Generation

Ensemble Learning on Deep Neural Networks for Image Caption ...

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International Conference on Semantic computing

作者： Harshitha Katpally Ajay Bansal School of Computing Informatics and Decision Systems Engineering Arizona State University Mesa AZ

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

Capturing the information in an image into a natural language sentence is considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the field of computer vision paired with natural language processing is crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words that are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data. The main focus of this paper is to reduce the variance factor that will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in our work. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.

关键词： Feature extraction Predictive models Training Computer architecture Computational modeling Machine learning

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You Only Train Once: A highly generalizable reinforcement learning method for dynamic job shop scheduling problem

TechRxiv

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TechRxiv 2022年

作者： Zeng, Yunhui Liao, Zijun Li, Xiu Yuan, Bo The Intelligent Computing Lab Division of Informatics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

Research in artificial intelligence demonstrates the applicability and flexibility of the reinforcement learning (RL) technique for the dynamic job shop scheduling problem (DJSP). However, the RL-based method will always overfit to the training environment and cannot generalize well to novel unseen situations at deployment time, which is unacceptable in real-world production. For this reason, this paper proposes a highly generalizable reinforcement learning framework named Train Once For All (TOFA) for the dynamic job shop scheduling problem. The trivial and non-trivial states are distinguished when the DJSP is formulated as a semi-Markov decision process, defining the size-agnostic state, action, and reward function. A novel graph representation learning method based on attention mechanism and spatial pyramid pooling is implemented to compress the disjunctive graphs of different-size DJSP into fixed-length feature vectors. Combining the proposed dynamic frame skipping and an improved prioritized experience replay method that considers the sample quality difference at different training phases. TOFA shows superb generalization capability, outperforms practically favored dispatching rules and even instance-by-instance training RL-based schedulers on various benchmark DJSP. Additionally, we proved that TOFA acquires a transferable scheduling policy that can be used to schedule a whole new DJSP without additional training. © , CC BY.

关键词： Reinforcement learning

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Online Explanation Generation for Planning Tasks in Human-Robot Teaming

Online Explanation Generation for Planning Tasks in Human-Ro...

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2020 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Mehrdad Zakershahrak Ze Gong Nikhillesh Sadassivam Yu Zhang School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

As AI becomes an integral part of our lives, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to justify its behavior is one of the key requirements of explainable agency. Prior work on explanation generation has been focused on supporting the rationale behind the robot's decision or behavior. These approaches, however, fail to consider the mental demand for understanding the received explanation. In other words, the human teammate is expected to understand an explanation no matter how much information is presented. In this work, we argue that explanations, especially those of a complex nature, should be made in an online fashion during the execution, which helps spread out the information to be explained and thus reduce the mental workload of humans in highly cognitive demanding tasks. However, a challenge here is that the different parts of an explanation may be dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented with three variations satisfying different "online" properties. The new explanation generation methods are based on a model reconciliation setting introduced in our prior work. We evaluated our methods both with human subjects in a simulated rover domain, using NASA Task Load Index (TLX), and synthetically with ten different problems across two standard IPC domains. Results strongly suggest that our methods generate explanations that are perceived as less cognitively demanding and much preferred over the baselines and are computationally efficient.

关键词： NASA Planning Task analysis Artificial intelligence Standards Intelligent robots Load modeling

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DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving systems

DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tes...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sai Krishna Bashetty Heni Ben Amor Georgios Fainekos School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper.

关键词： Three-dimensional displays Trajectory Cameras Videos Tracking Vehicle crash testing Data mining

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Achieving Multi-Tasking Robots in Multi-Robot Tasks

arXiv

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arXiv 2020年

作者： Zhang, Yu Smith, Winston School of Computing Informatics and Decision Systems Engineering Arizona State University United States

One simplifying assumption made in distributed robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While such a sanguine assumption is innocent to make in situations with sufficient resources so that the robots can operate independently, it becomes impractical when they must share their capabilities. In this paper, we consider multi-tasking robots with multirobot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap and task synergies to be exploited by reasoning about the physical constraints that can be synergistically satisfied for achieving the tasks. The key contribution of this work is a general and flexible framework to achieve this ability for multi-robot systems in resource-constrained situations to extend their capabilities. The proposed approach is built on the information invariant theory, which specifies the interactions between information requirements. In our work, we map physical constraints to information requirements, thereby allowing task synergies to be identified via the information invariant framework. We show that our algorithm is sound and complete under a problem setting with multi-tasking robots. Simulation results show its effectiveness under resource-constrained situations and in handling challenging situations in a multi-UAV simulator. Copyright © 2020, The Authors. All rights reserved.

关键词： Industrial robots

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ASU at TextGraphs 2019 shared task: Explanation regeneration using language models and iterative re-ranking 13

ASU at TextGraphs 2019 shared task: Explanation regeneration...

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13th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2019, in conjunction with the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019

作者： Banerjee, Pratyay School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781950737864

In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-theart language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set. © 2019 EMNLP-IJCNLP 2019 - Graph-Based Methods for Natural Language Processing - Proceedings of the 13th Workshop. All rights reserved.

关键词： Iterative methods

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