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Two Watts is All You Need: Enabling In-Detector Real-Time Machine Learning for Neutrino Telescopes Via Edge Computing

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arXiv 2023年

作者： Jin, Miaochen Hu, Yushi Argüelles, C.A. Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Department of Electrical and Computer Engineering & Natural Language Processing Group University of Washington SeattleWA98195 United States

The use of machine learning techniques has significantly increased the physics discovery potential of neutrino telescopes. In the upcoming years, we are expecting upgrade of currently existing detectors and new telescopes with novel experimental hardware, yielding more statistics as well as more complicated data signals. This calls out for an upgrade on the software side needed to handle this more complex data in a more efficient way. Specifically, we seek low power and fast software methods to achieve real-time signal processing, where current machine learning methods are too expensive to be deployed in the resource-constrained regions where these experiments are located. We present the first attempt at and a proof-of-concept for enabling machine learning methods to be deployed in-detector for water/ice neutrino telescopes via quantization and deployment on Google Edge Tensor processing Units (TPUs). We design a recursive neural network with a residual convolutional embedding, and adapt a quantization process to deploy the algorithm on a Google Edge TPU. This algorithm can achieve similar reconstruction accuracy compared with traditional GPU-based machine learning solutions while requiring the same amount of power compared with CPU-based regression solutions, combining the high accuracy and low power advantages and enabling real-time in-detector machine learning in even the most power-restricted environments. © 2023, CC BY-NC-SA.

关键词： Machine learning

From Correctness to Comprehension: AI Agents for Personalized Error Diagnosis in Education

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arXiv 2025年

作者： Zhang, Yi-Fan Li, Hang Song, Dingjie Sun, Lichao Xu, Tianlong Wen, Qingsong National Laboratory of Pattern Recognition University of Chinese Academy of Sciences China Computer Science department from Michigan State University United States CUHK-Shenzhen Natural Language Processing group China Computer Science and Engineering Lehigh University United States Squirrel Ai Learning group

Large language Models (LLMs), such as GPT-4, have demonstrated impressive mathematical reasoning capabilities, achieving near-perfect performance on benchmarks like GSM8K. However, their application in personalized education remains limited due to an overemphasis on correctness over error diagnosis and feedback generation. Current models fail to provide meaningful insights into the causes of student mistakes, limiting their utility in educational contexts. To address these challenges, we present three key contributions. First, we introduce MathCCS (Mathematical Classification and Constructive Suggestions), a multi-modal benchmark designed for systematic error analysis and tailored feedback. MathCCS includes real-world problems, expert-annotated error categories, and longitudinal student data. Evaluations of state-of-the-art models, including Qwen2-VL, LLaVA-OV, Claude-3.5-Sonnet and GPT-4o, reveal that none achieved classification accuracy above 30% or generated high-quality suggestions (average scores below 4/10), highlighting a significant gap from human-level performance. Second, we develop a sequential error analysis framework that leverages historical data to track trends and improve diagnostic precision. Finally, we propose a multi-agent collaborative framework that combines a Time Series Agent for historical analysis and an MLLM Agent for real-time refinement, enhancing error classification and feedback generation. Together, these contributions provide a robust platform for advancing personalized education, bridging the gap between current AI capabilities and the demands of real-world teaching. Copyright © 2025, The Authors. All rights reserved.

关键词： Systematic errors

Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

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arXiv 2023年

作者： Rysbek, Zhanibek Oh, Ki Hwan Shervedani, Afagh Mehri Klemenčič, Timotej Žefran, Miloš Di Eugenio, Barbara Robotics Lab Department of Electrical and Computer Engineering The University of Illinois at Chicago ChicagoIL60607 United States Laboratory of Robotics Faculty of Electrical Engineering University of Ljubljana Slovenia Natural Language Processing Lab Department of Computer Science The University of Illinois at Chicago ChicagoIL60607 United States

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established, but communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, and how the robot can communicate task goals, comprehend human intent, and take the lead as needed. To achieve this, we formulate a task that requires active force communication and propose a taxonomy that extends existing literature. Also, we conducted a study to observe how humans behave during collaborative manipulation tasks. An important contribution of this work is the novel features based on force-kinematic signals that demonstrate predictive power to recognize symbolic human intent. Further, we show the feasibility of developing a real-time intent classifier based on the novel features and speculate the role it plays in high-level robot controllers for physical Human-Robot Interaction (pHRI). This work provides important steps to achieve more human-like fluid interaction in physical co-manipulation tasks that are applicable and not limited to humanoid, assistive robots, and human-in-the-loop automation. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

Stateless and Rule-Based Verification For Compliance Checking Applications

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arXiv 2022年

作者： Besharati, Mohammad Reza Izadi, Mohammad Asgari, Ehsaneddin Computer Engineering Department Sharif University of Technology Tehran Iran Natural Language Processing and Digital Humanities Laboratory Computer Engineering Department - AI Group Sharif University of Technology Tehran Iran

Underlying computational model has an important role in any computation. The state and transition (such as in automata) and rule and value (such asin Lisp andlogic programming) are two comparable andcounterpart computational models. Both ofdeductive andmodel checkingverificationtechniquesare relyingona notion of state andasa result, their underlyingcomputationalmodelsare state dependent. Some verificationproblems(such ascompliance checkingby which an undercompliance systemisverifiedagainst someregulationsand rules) have not a strongnotionof state nortransition. Behalf of it, these systemshave a strongnotionof value symbolsanddeclarative rules definedon them. SARV(StatelessAndRule-Based Verification) isa verificationframeworkthat designedto simplify the overall processof verificationfor statelessandrule-basedverificationproblems(e.g. compliancechecking). In this paper, a formal logic-based framework for creatingintelligent compliancecheckingsystemsispresented. We define andintroduce thisframework,report a case study andpresent resultsof an experiment onit. The case study isabout protocol compliance checkingfor smart cities. Usingthissolution,a Rescue Scenario use case and itscompliance checkingare sketchedandmodeled. An automationengine for anda compliance solution with SARV are introduced. Basedon 300data experiments,the SARV-basedcompliance solutionoutperformsfamousmachine learning methods on a 3125-records software quality dataset. © 2022, CC BY.

关键词： Semantics

An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration

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An End-to-End Human Simulator for Task-Oriented Multimodal H...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Afagh Mehri Shervedani Siyu Li Natawut Monaikul Bahareh Abbasi Barbara Di Eugenio Miloš Žefran Department of Electrical and Computer Engineering Robotics Laboratory University of Illinois Chicago Chicago IL USA Department of Computer Science Natural Language Processing Laboratory University of Illinois Chicago Chicago IL USA Computer Science Department California State University Channel Islands Camarillo CA USA

This paper proposes a neural network-based user simulator that can provide a multimodal interactive environment for training Reinforcement Learning (RL) agents in collaborative tasks involving multiple modes of communication. The simulator is trained on the existing ELDERLY-AT-HOME corpus and accommodates multiple modalities such as language, pointing gestures, and haptic-ostensive actions. The paper also presents a novel multimodal data augmentation approach, which addresses the challenge of using a limited dataset due to the expensive and time-consuming nature of collecting human demonstrations. Overall, the study highlights the potential for using RL and multimodal user simulators in developing and improving domestic assistive robots.

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An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration

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arXiv 2023年

作者： Shervedani, Afagh Mehri Li, Siyu Monaikul, Natawut Abbasi, Bahareh Di Eugenio, Barbara Žefran, Miloš The Robotics Laboratory Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States The Natural Language Processing Laboratory Department of Computer Science University of Illinois Chicago ChicagoIL60607 United States The Computer Science Department California State University Channel Islands CamarilloCA93012 United States

关键词： Reinforcement learning

Enhancing Media Literacy: The Effectiveness of (Human) Annotations and Bias Visualizations on Bias Detection

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arXiv 2024年

作者： Spinde, Timo Wu, Fei Gaissmaier, Wolfgang Demartini, Gianluca Giese, Helge Department of Computer Science University of Göttingen Göttingen Germany Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Psychology University of Konstanz Konstanz Germany Cluster for the Advanced Study of Collective Behavior University of Konstanz Konstanz Germany School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia Heisenberg Chair for Medical Risk Literacy and Evidence-Based Decisions Charité – Universitätsmedizin Berlin Berlin Germany

Marking biased texts is a practical approach to increase media bias awareness among news consumers. However, little is known about the generalizability of such awareness to new topics or unmarked news articles, and the role of machine-generated bias labels in enhancing awareness remains unclear. This study tests how news consumers may be trained and pre-bunked to detect media bias with bias labels obtained from different sources (Human or AI) and in various manifestations. We conducted two experiments with 470 and 846 participants, exposing them to various bias-labeling conditions. We subsequently tested how much bias they could identify in unlabeled news materials on new topics. The results show that both Human (t(467) = 4.55, p 2part = 0.048). Our experimental framework provides theoretical implications for systematically assessing the generalizability of learning effects in identifying media bias. These findings also provide practical implications for developing news-reading platforms that offer bias indicators and designing media literacy curricula to enhance media bias awareness. © 2024, CC BY.

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Evaluating Multimodal Interaction of Robots Assisting Older Adults

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arXiv 2022年

作者： Shervedani, Afagh Mehri Oh, Ki-Hwan Abbasi, Bahareh Monaikul, Natawut Rysbek, Zhanibek Eugenio, Barbara Di Žefran, Miloš The Robotics Lab Electrical and Computer Engineering Department University of Illinois Chicago ChicagoIL60607 United States The Computer Science Department California State University Channel Islands CamarilloCA93012 United States The Natural Language Processing Lab Computer Science Department University of Illinois Chicago ChicagoIL60607 United States

We outline our work on evaluating robots that assist older adults by engaging with them through multiple modalities that include physical interaction. Our thesis is that to increase the effectiveness of assistive robots: 1) robots need to understand and effect multimodal actions, 2) robots should not only react to the human, they need to take the initiative and lead the task when it is necessary. We start by briefly introducing our proposed framework for multimodal interaction and then describe two different experiments with the actual robots. In the first experiment, a Baxter robot helps a human find and locate an object using the Multimodal Interaction Manager (MIM) framework. In the second experiment, a NAO robot is used in the same task, however, the roles of the robot and the human are reversed. We discuss the evaluation methods that were used in these experiments, including different metrics employed to characterize the performance of the robot in each case. We conclude by providing our perspective on the challenges and opportunities for the evaluation of assistive robots for older adults in realistic settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Human robot interaction

Discovering Phonetic Inventories with Crosslingual Automatic Speech Recognition

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arXiv 2022年

作者： Żelasko, Piotr Feng, Siyuan Velázquez, Laureano Moro Abavisani, Ali Bhati, Saurabhchand Scharenborg, Odette Hasegawa-Johnson, Mark Dehak, Najim Center of Language and Speech Processing The Johns Hopkins University 3400 North Charles Street BaltimoreMD21218 United States Human Language Technology Center of Excellence The Johns Hopkins University 810 Wyman Park Drive BaltimoreMD21218 United States Multimedia Computing Group Delft University of Technology Van Mourik Broekmanweg 6 Delft2628 XE Netherlands Department of Electrical and Computer Engineering University of Illinois 405 N Mathews UrbanaIL61801 United States

The high cost of data acquisition makes Automatic Speech Recognition (ASR) model training problematic for most existing languages, including languages that do not even have a written script, or for which the phone inventories remain unknown. Past works explored multilingual training, transfer learning, as well as zero-shot learning in order to build ASR systems for these low-resource languages. While it has been shown that the pooling of resources from multiple languages is helpful, we have not yet seen a successful application of an ASR model to a language unseen during training. A crucial step in the adaptation of ASR from seen to unseen languages is the creation of the phone inventory of the unseen language. The ultimate goal of our work is to build the phone inventory of a language unseen during training in an unsupervised way without any knowledge about the language. In this paper, we 1) investigate the influence of different factors (i.e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language;2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation;and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way. To that end, we conducted mono-, multi-, and crosslingual experiments on a set of 13 phonetically diverse languages and several in-depth analyses. We found a number of universal phone tokens (IPA symbols) that are well-recognized cross-linguistically. Through a detailed analysis of results, we conclude that unique sounds, similar sounds, and tone languages remain a major challenge for phonetic inventory discovery. © 2022, CC BY-NC-ND.

关键词： Telephone sets