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Invertibility of Discrete-Time Linear Systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control.

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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Analysis Method for Probabilistic Verification for Smart IoT Systems with Process Algebra 5

Analysis Method for Probabilistic Verification for Smart IoT...

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5th IEEE International Conference on Smart Internet of Things, SmartIoT 2021

作者： Lee, Sunghyeon Song, Junsup Karagiannis, Dimitri Lee, Moonkun Department of Computer Science and Engineering Jeonbuk National University Jeonju Korea Republic of Research Group Knowledge Engineering University of Vienna Vienna Austria

ISBN: (纸本)9781665445115

Process algebra is one of the most suitable formal methods to model Smart IoT Systems to specify their probabilistic behavior, since process algebra may include probability property for choice operation as shown in PAROMA and PACSR, which, however, do not have enough analytic methods to control the degree of nondeterminism for the operation. In order to overcome the limitation, the paper presents a new formal method, called dTP-Calculus, and an analytic method to control the nondeterminism in order to satisfy safety and security requirements of the IoT systems. To prove the concept of the approach in the paper, the SAVE tool suite has been developed on the ADOxx and is used to demonstrate its applicability with a Smart EMS (Emergence Medical Service) Example. © 2021 IEEE.

关键词： Internet of things

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Book Recommendation System Using Hybrid Content and Collaborative Filtering Techniques

Book Recommendation System Using Hybrid Content and Collabor...

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Disruptive Technologies (ICDT), International Conference on

作者： Urvi Gupta Tripti Singh Vidyush Singh Umang Rastogi Sushil Kumar Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad UP India CSE Department KIET Group of Institutions Delhi-NCR Ghaziabad India CSE Department Data Science & Deep Learning Lab KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In this study, the process of producing a book recommendation system outlines the process of finding books to provide personalized suggestions according to user preference and behavior. The system increases user satisfaction by tailoring recommendations to their tastes, improving the overall reading experience, and enhancing engagement and retention on plat-forms. It tracks users with collaborative filtering. interactions, content-based filtering to study book characteristics such as genre and author, and mixed methods to combine both techniques that will help ensure accuracy and circumvent similar limitations of cold start problems. Machine learning models-, pre-processing of data algorithms, and accuracy and recall measures of analysis ensure the effectiveness of the system in providing individualized and relevant recommendations that are 85% accurate. The findings reveal test the system's capacity to generate good suggestions, improving user experience and engagement. Improvements in the future could include real-time recommendations and broader feedback integration towards further optimization in accuracy and usability.

关键词： Measurement Accuracy Filtering Collaborative filtering User experience Real-time systems User preference Usability Recommender systems Optimization

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IKGN: Intention-aware knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Mitigating Translationese in Low-resource Languages: The Storyboard Approach 30

Mitigating Translationese in Low-resource Languages: The Sto...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Kuwanto, Garry Urua, Eno-Abasi Amuok, Priscilla Muhammad, Shamsuddeen Hassan Aremu, Anuoluwapo Otiende, Verrah Nanyanga, Loice Nyoike, Teresiah Akpan, Aniefon Udouboh, Nsima Archibong, Idongesit Moses, Idara Ige, Ifeoluwatayo Ajibade, Benjamin Awokoya, Olumide Abdulmumin, Idris Aliyu, Saminu Mohammad Iro, Ruqayya Ahmad, Ibrahim Said Smith, Deontae Michaels, Praise E.L. Adelani, David Ifeoluwa Wijaya, Derry Tanti Andy, Anietie Department of Computer Science Boston University United States University of Uyo Nigeria Imperial College London United Kingdom Rochester Institute of Technology United States Data Science for Social Impact Research Group University of Pretoria South Africa Bayero University Kano Nigeria Institute For Experiential AI Northeastern University United States Department of Electrical Engineering and Computer Science Howard University United States University College London United Kingdom Monash University Indonesia Indonesia Masakhane

ISBN: (纸本)9782493814104

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Translation (languages)

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series 41

An Empirical Examination of Balancing Strategy for Counterfa...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright 2024 by the author(s)

关键词： Time series analysis

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Improving Zero-Shot Semantic Segmentation using Dynamic Kernels

Improving Zero-Shot Semantic Segmentation using Dynamic Kern...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Tauseef Tajwar Muftiqur Rahman Taukir Azam Chowdhury Sabbir Ahmed Moshiur Farazi Md. Hasanul Kabir Department of Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Data61-CSIRO Imaging and Computer Vision Group Canberra Australia

Zero-shot Semantic Segmentation (ZS3) is a challenging task that segments objects belonging to classes that are completely unseen during training. An established and intuitive approach is to formulate ZS3 as a combination of two subtasks where, at first, mask proposals are generated and then each pixel in those regions is assigned a class label. Most of the existing works struggle to generate masks with high generalization capability, which results in significant underperformance in unseen classes. In this connection, we propose the use of ‘Dynamic Kernels’ to help a ZS3 model better ‘understand’ the objects in the training phase by taking advantage of their inherent inductive biases to generate better mask proposals. They act as specialized agents that are updated based on their corresponding contents from the seen classes and then utilize that knowledge to understand unseen objects. The proposed pipeline also leverages the Contrastive Language-Image Pre-Training (CLIP) architecture to perform segment classification which further improves the generalization performance by exploiting its cross-modal training. Dynamic kernels go hand-in-hand with CLIP since it is able to process the granularity of CLIP from image level to pixel level resulting in performance improvement for both the seen and unseen classes. Our method, ‘Zero-Shot dynamic Kernel Network’ (ZSK-Net), outperforms the previous works by achieving +6.4h IoU on the Pascal VOC dataset. It also achieves state-of-the-art result on the COCO-Stuff dataset by +0.9h IoU on a single prompt setting.

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Next-Gen Mental Health Detection through Deep Learning and NLP in Social Media

Next-Gen Mental Health Detection through Deep Learning and N...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Jagan. A Senthil Kumar D S. Karunakaran Nageswari D Regina Shoba Dass J. Seetha Computer Science and Engineering Surya Group of Institutions Vikiravandi Villupuram Artificial Intelligence and Data Science St. Joseph's College of Engineering OMR Chennai Department of Computer Technology Kongu Engineering College Perundurai Erode Department of Science and Humanities R.M.K. College of Engineering and Technology Psychology CMR University Bangalore Department of Computer Science and Business Systems Panimalar Engineering College Chennai

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Social media proliferation promotes new modalities of understanding and intervening in mental health. This paper presents a new approach to mental health detection using some deep learning and natural language processing techniques over social media data. Enriched by extensive textual content shared across social media platforms such as Twitter, Reddit, and Facebook, we propose to design an automated system that can detect traces of mental health conditions like depression, anxiety, and stress. In line with the proposed methodology, Bi-LSTM networks and attention mechanisms shall be applied to exploit linguistic patterns and semantic features from user posts. Such techniques involve the integration of sentiment analysis and topic modelling, which increase the accuracy and contextual comprehension of detected signals. Either way, the system is trained against a curated dataset of annotated social media posts validated further against clinical assessments for strength and reliability, with quite promising preliminary results— Rubin et al.'s model achieves high precision and recall in mental health indicator detection. The results reinforce that AI-driven tools have an assay of potential in rendering early detection and intervention support, especially for mental illness, which has contributed to improved mental health outcomes globally.

关键词： Deep learning Technological innovation Sentiment analysis Accuracy Social networking (online) Blogs Semantics Mental health Web sites Synthetic data

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Automated Health Monitoring: Integrating AI and IoT for Continuous Patient Observation

Automated Health Monitoring: Integrating AI and IoT for Cont...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： E V N Jyothi Sighakolli Sailaja M. SriRama Lakshmi Reddy T. Sunitha Department of CSE (AI&ML) CMR College of Engineering & Technology Hyderabad Telangana India Department of Computer Science and Engineering Rise krishna sai prakasam group of institutions Ongole A.P India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad Telangana India Department of Computer Science and Engineering (Data Science) Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Integrating Artificial Intelligence (AI) with the Internet of Things (IoT) has recently become more advantageous for various complex fields like innovative health, intrusion detection in various devices, and continuous monitoring of crops yielding in various farming lands. From the health point of view, it is essential to monitor multiple patients' health according to their abnormal health conditions. The proposed Integrated Health Monitoring System (IHMS) is developed in this work by combining AI-based models such as Recurrent Neural Networks (RNNs) and advanced IoT sensors. The IHMS is mainly used in health care applications such as heart care analysis and other abnormal health conditions like blood pressure, levels of oxygen, and glucose readings, and it transmits this information to AI-powered systems. The IHMS mainly analyzes abnormal patterns, detects anomalies, and provides timely analytical analysis of patient data. Finally, the proposed approach IHMS analyzes several datasets and shows the performance in terms of classification.

关键词： Recurrent neural networks Medical services Sepsis Real-time systems Blood pressure Sensor systems Internet of Things Biomedical monitoring Monitoring Wearable sensors

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Exploring the Racial Bias in Pain Detection with a computer Vision Model 44

Exploring the Racial Bias in Pain Detection with a Computer ...

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44th Annual Meeting of the Cognitive science Society: Cognitive Diversity, CogSci 2022

作者： Fabi, Sarah Xu, Xiaojing de Sa, Virginia R. Department of Cognitive Science Halicioglu Data Science Institute University of California San Diego United States Neuro-Cognitive Modeling Group University of Tübingen Germany Department of Electrical and Computer Engineering University of California San Diego United States

People detect painful expressions more easily in members of their racial ingroup than outgroup. Here, we wanted to investigate this racial bias with a machine learning model trained to detect activations of different action units of painful facial expressions. We examined whether the model detected higher action unit activation for European than African faces when trained on datasets with mostly White faces. To control for confounding variables, pictures of faces were generated with the FaceGen Modeller. Results revealed that there exist differences in the visual detectability of some facial muscle activations due to skin color or other race-dependent facial features. Despite the bias towards European looking faces in the training data, some activations were more easily detectable in African faces. Thus, neither the perceptual detectability, nor the larger exposure to own-race faces seems to solely explain the racial bias in pain detection. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Chemical activation

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