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arXiv 2022年

作者： Nikolaos, Antoniou Georgiou, Efthymios Potamianos, Alexandros School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Institute for Language and Speech Processing Athena Research Center Athens Greece

Designing powerful adversarial attacks is of paramount importance for the evaluation of p-bounded adversarial defenses. Projected Gradient Descent (PGD) is one of the most effective and conceptually simple algorithms to generate such adversaries. The search space of PGD is dictated by the steepest ascent directions of an objective. Despite the plethora of objective function choices, there is no universally superior option and robustness overestimation may arise from ill-suited objective selection. Driven by this observation, we postulate that the combination of different objectives through a simple loss alternating scheme renders PGD more robust towards design choices. We experimentally verify this assertion on a synthetic-data example and by evaluating our proposed method across 25 different ∞-robust models and 3 datasets. The performance improvement is consistent, when compared to the single loss counterparts. In the CIFAR-10 dataset, our strongest adversarial attack outperforms all of the white-box components of AutoAttack (AA) ensemble [1], as well as the most powerful attacks existing on the literature, achieving state-of-the-art results in the computational budget of our study (T = 100, no restarts). Copyright © 2022, The Authors. All rights reserved.

关键词： Budget control

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Challenges in real-time-embedded IoT Command Recognition 7

Challenges in real-time-embedded IoT Command Recognition

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7th IEEE World Forum on Internet of Things, WF-IoT 2021

作者： Younis, Hazem Hansen, John H.L. The University of Texas at Dallas CRSS-CI Lab: Center for Robust Speech Systems - Cochlear Implant Processing Lab Department of Electrical and Computer Engineering Richardson United States

ISBN: (纸本)9781665444316

Automatic speech recognition (ASR) is an effective means of communicating information for command and control of IoT based electronic devices. Voice enabled electronic devices offer the prospect of greater user control and increased system support for deployed platforms. The internet of things (IoT) is an internet-based architecture that allows for data transmission over wireless networks for control and monitoring. By combining previously stated architectures, we can perform many daily tasks remotely, eliminating the need for physical presence. Some challenges associated with command recognition include command word perplexity as well as noise levels/distortion in the environmental setting. In this study, system formulation consisted of (i) a Raspberry-pi low resource microprocessor with fixed computing capacity, and (ii) a ReSpeaker microphone system, which provided an integrated hardware setup for algorithm development to combine aspects of ASR and IoT, allowing users to dictate a variety of commands remotely. The platform eliminates the need for physical interaction with the device by simply listening to commands and performing actions to fulfill the command. The platform is suitable for several settings including residential and commercial spaces due to its low computational resource model. Trade-offs in machine learning based CNN network design and training are explored. Also, challenges and factors that affect speech-command recognition for IoT related applications are considered. A summary of phonetic and computational limiting factors for success in command recognition devices also discussed. The study highlights several ASR based neural net architectures which are integrated within a low-resource computing platform, along with benchmark performance evaluations. © 2021 IEEE.

关键词： Internet of things

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Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson’s disease

Concurrent validity of instrumented insoles measuring gait a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sophia A. Watkinson Anthony Anderson Michael Caiola David Eguren Michael Gonzalez Laureano Moro Velazquez Najim Dehak Chelsea Motley Emile Moukheiber Kelly Mills Brittney Muir Ankur Butala Kimberly Kontson Division of Biomedical Physics US Food and Drug Administration MD USA Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA Department of Veterans Affairsy Center for Limb Loss and Mobility Seattle WA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M; mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson’s disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson’s correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems.

关键词： Measurement Legged locomotion Correlation coefficient Instruments Turning Sensor systems engineering in medicine and biology Diseases

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Eye movement patterns are similar during accurate multiple-target tracking

Eye movement patterns are similar during accurate multiple-t...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Kamyar Bagha Shiva Kamkar Hamid Abrishami Moghaddam Lauri Oksama Jie Li Jukka Hyönä Computer Engineering Department Khatam University Tehran Iran Machine Vision and Medical Image Processing (MVMIP) Laboratory Faculty of Electrical Engineering K.N.Toosi University of Technology Tehran Iran Center for International Scientific Studies and Collaboration (CISSC) Tehran Iran Department of Psychology and Speech-Language Pathology University of Turku Turku Finland Center for Cognition and Brain Disorders Hangzhou Normal University Hangzhou China

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Understanding how the brain works is a base of cognitive info-communication. To this aim we focus on multiple target tracking (MTT) as a key task that involves two important cognitive factors, attention and memory. Humans track multiple objects in their daily life while facing various challenges including occlusion and set-size. Eye movement research has shown that there are within and between subjects’ differences in scanpaths while performing MTT tasks. However, it is unclear if there is a winning scan pattern that would lead to a successful tracking of targets. To answer this question, we used dynamic time warping to compare the similarities between subjects’ scan patterns during an MTT task with different challenges. We studied the effect of set-size, occlusion, and trial response on the similarities. Then a mixed effect analysis was applied on the output to measure whether the findings were statistically significant. Results demonstrated that scan patterns were more similar when MTT task was performed correctly. It suggests that there is a common tracking strategy adopted by the viewers that leads to a correct response. Decoding this strategy has countless applications in the fields including human-computer interaction, brain-modeling and cognitive info-communication.

关键词： Human computer interaction Visualization Target tracking Accuracy Predictive models Communications technology Time measurement Cognition Decoding Cognitive science

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Decoding speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

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IEEE Signal processing in Medicine and Biology Symposium (SPMB)

作者： M. Mahmud M. Hasan M. Yeasin G. Bidelman Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Speech Language and Hearing Sciences Indiana University Bloomington IN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing.

关键词： Support vector machines Accuracy Hidden Markov models Phonetics Signal processing Electroencephalography Real-time systems Decoding speech processing Random forests

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SPECTRE: Visual speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Panagiotis P. Filntisis George Retsinas Foivos Paraperas-Papantoniou Athanasios Katsamanis Anastasios Roussos Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece School of Electrical & Computer Engineering National Technical University of Athens Greece Imperial College London UK Institute for Language and Speech Processing Athena R.C. Greece Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece College of Engineering Mathematics and Physical Sciences University of Exeter UK

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading" loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/

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PQLM - MULTILINGUAL DECENTRALIZED PORTABLE QUANTUM language MODEL FOR PRIVACY PROTECTION

arXiv

引用

arXiv 2022年

作者： Li, Shuyue Stella Zhang, Xiangyu Zhou, Shu Shu, Hongchao Liang, Ruixing Liu, Hexin Garcia, Leibny Paola Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Department of Physics Hong Kong University of Science and Technology Hong Kong School of Electrical and Electronic Engineering Nanyang Technological University Singapore

With careful manipulation, malicious agents can reverse engineer private information encoded in pre-trained language models. Security concerns motivate the development of quantum pre-training. In this work, we propose a highly portable quantum language model (PQLM) that can easily transmit information to downstream tasks on classical machines. The framework consists of a cloud PQLM built with random Variational Quantum Classifiers (VQC) and local models for downstream applications. We demonstrate the ad hoc portability of the quantum model by extracting only the word embeddings and effectively applying them to downstream tasks on classical machines. Our PQLM exhibits comparable performance to its classical counterpart on both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy) metrics. We also perform ablation studies on the factors affecting PQLM performance to analyze model stability. Our work establishes a theoretical foundation for a portable quantum pre-trained language model that could be trained on private data and made available for public use with privacy protection guarantees. Copyright © 2022, The Authors. All rights reserved.

关键词： Modeling languages

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Affective learning and the charismatic lecturer

Affective learning and the charismatic lecturer

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International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Vered Aharonson Aviad Malachi Tal Katz-Navon Department of Computer Engineering Afeka Tel Aviv Academic College of Engineering Tel Aviv Israel School of Electrical and Information Engineering Univeristy of the Witwatersrand Johannesburg South Africa Afeka Center of Language Processing Afeka Tel Aviv Academic College of Engineering Tel Aviv Israel Arison School of Business Reichman University Herzlia Israel

Students can be inspired, engaged and motivated by lecturers who possess the elusive property of charisma. This study harnessed affective computing to map students’ perception into quantitative voice features of lecturers. A cohort of 900 students listened to 200 short audio extracts from academic lecture recordings. The students provided rating of the lecturers’ charisma using two validated scales. Explainable prosodic voice features were extracted from the lecturers’ speech recordings. A random forest classifier was trained on the lecturers’ voice features and students rating labels. The features were ranked by the classifier according to their contribution to the prediction F1-score. Six out of the ten upper-ranked features were rhythm change features. The prediction accuracy exceeded 0.9 for all charisma rating classes. The results portray the potential of affective computing to explain the property of charisma in lecturers’ voice. Our findings highlight features in the production and perception of charismatic voice of lecturers. This affective model can potentially be used for machine-guided practice for lecturers and improve their teaching effectivity and their interaction with learners.

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FedPOIRec: Privacy preserving federated POI recommendation with social influence

arXiv

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arXiv 2021年

作者： Perifanis, Vasileios Drosatos, George Stamatelatos, Giorgos Efraimidis, Pavlos S. Institute for Language and Speech Processing Athena Research Center Xanthi Kimmeria67100 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Kimmeria67100 Greece

With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a centralized approach that requires the transmission and collection of users' private data. In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-N POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner's device, while the local updates are blindly aggregated by a parameter server. Second, the local recommenders get personalized by allowing users to exchange their learned parameters, enabling knowledge transfer among friends. To this end, we propose a privacy preserving protocol for integrating the preferences of a user's friends after the federated computation, by exploiting the properties of the CKKS fully homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach into five real-world datasets using two recommendation models. Extensive experiments demonstrate that FedPOIRec achieves comparable recommendation quality to centralized approaches, while the social integration protocol incurs low computation and communication overhead on the user side. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge management

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Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol

arXiv

引用

arXiv 2025年

作者： Wang, Weiqi Ou, Jiefu Song, Yangqiu Van Durme, Benjamin Khashabi, Daniel Center for Speech and Language Processing Johns Hopkins University BaltimoreMD United States Department of Computer Science and Engineering HKUST Hong Kong

Literature review tables are essential for summarizing and comparing collections of scientific papers. We explore the task of generating tables that best fulfill a user’s informational needs given a collection of scientific papers. Building on recent work (Newman et al., 2024), we extend prior approaches to address real-world complexities through a combination of LLM-based methods and human annotations. Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified;(ii) Retrieved candidate papers frequently contain irrelevant content;and (iii) Task evaluation should move beyond shallow text similarity techniques and instead assess the utility of inferred tables for information-seeking tasks (e.g., comparing papers). To support reproducible evaluation, we introduce ARXIV2TABLE, a more realistic and challenging benchmark for this task, along with a novel approach to improve literature review table generation in real-world scenarios. Our extensive experiments on this benchmark show that both open-weight and proprietary LLMs struggle with the task, highlighting its difficulty and the need for further advancements. Our dataset and code are available at https://***/JHU-CLSP/arXiv2Table. Copyright © 2025, The Authors. All rights reserved.

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