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arXiv 2024年

作者： Kunisky, Dmitriy Spielman, Daniel A. Wein, Alexander S. Yu, Xifan Department of Applied Mathematics & Statistics Johns Hopkins University United States Department of Computer Science Yale University United States Department of Mathematics UC Davis United States

We study the problem of detecting or recovering a planted ranked subgraph from a directed graph, an analog for directed graphs of the well-studied planted dense subgraph model. We suppose that, among a set of n items, there is a subset S of k items having a latent ranking in the form of a permutation π of S, and that we observe a fraction p of pairwise orderings between elements of {1, . . ., n} which agree with π with probability 1/2 + q between elements of S and otherwise are uniformly random. Unlike in the planted dense subgraph and planted clique problems where the community S is distinguished by its unusual density of edges, here the community is only distinguished by the unusual consistency of its pairwise orderings. We establish computational and statistical thresholds for both detecting and recovering such a ranked community. In the log-density setting where k, p, and q all scale as powers of n, we establish the exact thresholds in the associated exponents at which detection and recovery become statistically and computationally feasible. These regimes include a rich variety of behaviors, exhibiting both statistical-computational and detection-recovery gaps. We also give finer-grained results for two extreme cases: (1) p = 1, k = n, and q small, where a full tournament is observed that is weakly correlated with a global ranking, and (2) p = 1, q = 1/2 , and k small, where a small "ordered clique" (totally ordered directed subgraph) is planted in a random tournament. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

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A Dynamic Approach for Handling UAV in Crowded Environment Using Deep Q-Networks 4

A Dynamic Approach for Handling UAV in Crowded Environment U...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Upadhyay, Yash Gahlawat, Muskan Nivetha, S. Rana, Saurabh Natarajan, B. Gupta, Pratik Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Mathematics Chandigarh University India Department of Computer Science and Engineering RVS College of Engineering & Technology Coimbatore641402 India Amrita Vishwa Vidyapeetham Chennai601103 India Department of Applied Mathematics Gautam Buddha University Noida India

ISBN: (纸本)9798350305258

The demand for wireless connections is rising, and the advent of mobile internet has sparked a hunt for fresh, creative ways to expand the capacity and reach of cellular networks. One strategy to provide ground mobile users with long-term communication coverage is to employ Unmanned Aerial vehicles (UAV) as mobile base stations. The proposed method incorporates a Deep Reinforcement Learning (DRL) method to address the challenges that persist with traditional approaches. We introduced Deep Q-Learning with an attention mechanism-based multi-agent model to develop an energy-efficient control system for UAVs in cellular networks. Due to the proposed strategy's dynamic nature, it efficiently handles the crowded environment by resolving scalability and mobility issues. The researchers evaluated the proposed model using the benchmark dataset, producing promising results. Further, the researchers tested the proposed model using the sum rate metric to analyze the capability of the proposed algorithm to provide a reasonable throughput rate. © 2023 IEEE.

关键词： Cellular Communication Convolutional Neural Network Deep Q-Networks Deep Reinforcement Learning Deployment Optimization Energy Consumption Optimum Transport Target Network Unmanned Aerial Vehicle

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Design of Drone Remote-Control System using Human Motion Recognition 5

Design of Drone Remote-Control System using Human Motion Rec...

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5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Chen, Shyh-Wei Lai, Yu-Chun Liu, Chia-Hui Kuo, Chin-Guo Tunghai University Department of Computer Science Taichung Taiwan Tunghai University Master Program of Digital Innovation Taichung Taiwan Chinese Culture University Department of Applied Mathematics Taipei Taiwan National Taiwan Normal University Department of Industrial Education Taipei Taiwan

ISBN: (纸本)9781665433181

At present, the most common and low-cost method to control the drone is to use a mobile phone with an APP. The operator takes his mobile phone screen as a human-machine interface, and at the same time controls and receives the images returned by the drone. The control mode can be divided into two symmetry modes according to how the operator views and knows the location of the drone: exocentric viewing and egocentric viewing mode. In the exocentric viewing mode, the operator directly visually confirms the position of the drone, while in the egocentric viewing mode, the drone is equipped with a camera and transmits the flight image to the operator in real-time. Since the drone cannot be far away from the operator in the exocentric viewing mode, the relative egocentric mode allows the operator to feel immersed in the environment to control the drone more accurately. Therefore, by adapting the egocentric viewing and using human motion recognition, an unmanned vehicle remote-control system is designed. The low-speed, small-scaled, nature-stability UAVs are ideally suited for the proposed system. The system was designed with a PID controller to improve the UAV's handling. The experimental results verified the feasibility of the UVA control system and it could stabilize the posture of the drone. © 2022 IEEE.

关键词： Drones

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Data Analytics on Eco-Conditional Factors Affecting Speech Recognition Rate of Modern Interaction Systems

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Journal of Mobile Multimedia 2022年第4期18卷 1153-1176页

作者： Kaladevi, A.C. Saravanakumar, R. Veena, K. Muthukumaran, V. Thillaiarasu, N. Kumar, S. Satheesh Department of Computer Science and Engineering Sona College of Technology Salem India Department of CSE Dayananda Sagar Academy of Technology and Management Bangalore 560082 India Department of Computer Science J.K.K.Nataraja College of Arts & Science Namakkal Dt. Komarapalayam India Department of Mathematics School of Applied Sciences REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Bengaluru India Department of Computer Science School of Applied Sciences REVA University Bengaluru India

Speech-based Interaction systems contribute to the growing class of contemporary interactive techniques (Human-computer Interactive system), which have emerged quickly in the last few years. Versatility, multi-channel synchronization, sensitivity, and timing are all notable characteristics of speech recognition. In addition, several variables influence the precision of voice interaction recognition. However, few researchers have done a significant study on the five eco-condition variables that tend to affect speech recognition rate (SRR): ambient noise, human noise, utterance speed, and frequency. The principal strategic goal of this research is to analyze the influence of the four variables mentioned earlier on SRR, and it includes many stages of experimentation on mixed noise speech data. The sparse representation-based analyzing technique is utilized to analyze the effects. Speech recognition is not noticeably affected by a person's usual speaking pace. As a result, high-frequency voice signals are more easily recognized (∼98.12%) than low-frequency speech signals in noisy environments. By performing the experiments, the test results may help design the distributive controlling and commanding systems. © 2022 River Publishers.

关键词： ambient noise eco-conditional factors frequency human noise Interaction system recognition rate utterance speed

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Generalized epidemic model incorporating non-Markovian infection processes and waning immunity

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Physical Review E 2023年第1期108卷 014405-014405页

作者： Qihui Yang Joan Saldaña Caterina Scoglio Department of Electrical and Computer Engineering Kansas State University Manhattan 66506 Kansas USA Department of Computer Science Applied Mathematics and Statistics Universitat de Girona Girona 17003 Catalonia Spain

The Markovian approach, which assumes exponentially distributed interinfection times, is dominant in epidemic modeling. However, this assumption is unrealistic as an individual's infectiousness depends on its viral load and varies over time. In this paper, we present a Susceptible-Infected-Recovered-Vaccinated-Susceptible epidemic model incorporating non-Markovian infection processes. The model can be easily adapted to accurately capture the generation time distributions of emerging infectious diseases, which is essential for accurate epidemic prediction. We observe noticeable variations in the transient behavior under different infectiousness profiles and the same basic reproduction number R0. The theoretical analyses show that only R0 and the mean immunity period of the vaccinated individuals have an impact on the critical vaccination rate needed to achieve herd immunity. A vaccination level at the critical vaccination rate can ensure a very low incidence among the population in the case of future epidemics, regardless of the infectiousness profiles.

关键词： Epidemic evolution Epidemic growth Population dynamics

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Development of Virtual Reality Platform through Human computer Interaction using Artificial Intelligence 3

Development of Virtual Reality Platform through Human Comput...

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3rd International Conference on applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Saranya, S. Channarayapriya, B. Harshavardhini, U. Nandhini, A.Sunitha Revathi, J. Venkatesan, R. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Deparment of Information Technology Chennai India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Mathematics Erode India Bannari Amman Institute of Technology Deparment of Information Technology Erode India

ISBN: (纸本)9798350375190

The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR system. This helps in providing a natural interaction experience to the users. Deep learning techniques help in obtaining the realistic nature of the physical world in the virtual platform. The personalized experiences are obtained using the integration of deep learning with VR platforms. The optimization algorithm helps in achieving user interaction in a real-time platform. The adaptive learning helps in increasing the overall immersion with a user-friendly interface. The VR development process is done using a genetic algorithm. This helps to evolve and optimize themselves within the period of time. This helps the VR platform to iteratively improve its performance and complete design through adopting successful combinations of various parameters. The proposed approach helps the VR system to evolve towards an optimal condition. This helps to satisfy the various needs and preferences of the users. Thus the proposed system helps in obtaining adaptive and intelligent virtual environments. This helps in overcoming various challenges in designing immersive VR experiences with personalized digital environments. © 2024 IEEE.

关键词： Virtual reality

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Trustworthy actionable perturbations 24

Trustworthy actionable perturbations

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Proceedings of the 41st International Conference on Machine Learning

作者： Jesse Friedbaum Sudarshan Adiga Ravi Tandon Program in Applied Mathematics University of Arizona Tucson AZ and Department of Electrical and Computer Engineering University of Arizona Tucson AZ Department of Electrical and Computer Engineering University of Arizona Tucson AZ

Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and "fool" the classifier. We propose a new framework for creating modified inputs that change the true underlying probabilities in a beneficial way which we call Trustworthy Actionable Perturbations (TAP). This includes a novel verification procedure to ensure that TAP change the true class probabilities instead of acting adversarially. Our framework also includes new cost, reward, and goal definitions that are better suited to effectuating change in the real world. We present PAC-learnability results for our verification procedure and theoretically analyze our new method for measuring reward. We also develop a methodology for creating TAP and compare our results to those achieved by previous counterfactual methods.

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events

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Procedia computer science 2023年 227卷 805-812页

作者： Alam Ahmad Hidayat Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data.

关键词： count data disaster surveillance floods mixed models Twitter

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Determining the financial performance of the firms in the Borsa Istanbul sustainability index:integrating multi criteria decision making methods with simulation

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Financial Innovation 2024年第1期10卷 3592-3635页

作者： Ahmet Kaya Dragan Pamucar Hasan Emin Gürler Mehmet Ozcalici Department of Banking-Finance Applied Sciences FacultyAkdeniz UniversityAntalyaTurkey Faculty of Organizational Sciences University of BelgradeBelgradeSerbia College of Engineering Yuan Ze UniversityTaoyuanTaiwan Department of Computer Science and Mathematics Lebanese American UniversityByblosLebanon Department of International Trade and Logistics Applied Sciences FacultyAkdeniz UniversityAntalyaTurkey Department of International Trade and Logistics Faculty of Economics and Administrative SciencesKilis 7 Aralık UniversityKilisTurkey

Regardless of the industry in which a company operates,evaluating corporate performance is one of the most critical and vital processes;the most essential and prominent performance evaluation is related to financial *** performance analysis is complex and critical for decision-makers in different financial performance factors;thus,a methodological framework is needed to solve such complex decision ***,this research aims to rank the companies included in the sustainability index(excluding banks)in Turkey by considering their financial *** criteria weights were determined using the full consistency method(FUCOM)by considering the evaluations of four *** firms were ranked using nine multi-criteria decision-making *** consensus among the nine rankings was ensured with the Copeland *** decision matrix includes financial ratios and the stock market performance of the firms;100,000 FUCOM weights were created with random evaluations to validate the *** results indicate that the most crucial criterion is the current ratio by considering expert *** simulation indicates that alternative 16(alternative 21)is superior(inferior)to the other alternatives,even though the weights are determined with random *** with expert evaluations is similar to the mean of the weight simulation *** results demonstrate that the proposed framework can be performed as a basis for financial performance ranking.

关键词： Financial performance BIST sustainability index Simulation MCDM techniques

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Latent Representation Learning for Multimodal Brain Activity Translation

Latent Representation Learning for Multimodal Brain Activity...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Arman Afrasiyabi Dhananjay Bhaskar Erica L. Busch Laurent Caplette Rahul Singh Guillaume Lajoie Nicholas B. Turk-Browne Smita Krishnaswamy Department of Computer Science Department of Genetics Yale University Department of Psychology Wu Tsai Institute Department of Mathematics and Statistics Mila - Quebec AI Institute Université de Montréal Department of Applied Mathematics

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

关键词： Wavelet transforms Training Translation Neuroscience Soft sensors Transformers Spatiotemporal phenomena Recording Spatial resolution Speech processing

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