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Using Weighted Matching to Solve 2-Approval/Veto Control and Bribery

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arXiv 2023年

Determining the complexity of election attack problems is a major research direction in the computational study of voting problems. The paper "Towards completing the puzzle: complexity of control by replacing, adding, and deleting candidates or voters" by Erdélyi et al. (JAAMAS 2021) provides a comprehensive study of the complexity of control problems. The sole open problem is constructive control by replacing voters for 2-Approval. We show that this case is in P, strengthening the recent RP (randomized polynomial-time) upper bound due to Fitzsimmons and Hemaspaandra (IJCAI 2022). We show this by transforming 2-Approval CCRV to weighted matching. We also use this approach to show that priced bribery for 2-Veto elections is in P. With this result, and the accompanying (unsurprising) result that priced bribery for 3-Veto elections is NP-complete, this settles the complexity for k-Approval and k-Veto standard control and bribery cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

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arXiv 2023年

作者： Lee, Daeun Son, Sejung Jeon, Hyolim Kim, Seungbae Han, Jinyoung Dept. of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Dept. of Applied Artificial Intelligence Dept. of Human-AI Interaction Sungkyunkwan University Seoul Korea Republic of Computer Science and Engineering College of Engineering University of South Florida TampaFL United States

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Multi-Module Deep Learning and IoT-Based Pest Detection System Using Sound Analytics in Large Agricultural Field

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Multi-Module Deep Learning and IoT-Based Pest Detection Syst...

2023 IEEE International Conference on Electrical, Electronics, Communication and computers, ELEXCOM 2023

作者： Ali, Md. Akkas Dhanaraj, Rajesh Kumar Sharma, Anupam Kumar Balusamy, Balamurugan Geetha, V. Grover, Veena School of Computing Science and Engineering Galgotias University U.P Greater Noida India Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj Bangladesh Pune India Shiv Nadar University India School of Applied Sciences Reva University Dept. of Computer Science Bangalore India Noida Institute of Engineering and Technology U.P Greater Noida India

ISBN: (纸本)9798350305111

Agriculture has progressed to an advanced stage worldwide and into an economical source of sophisticated agricultural practices. It is a significant milestone in the history of agriculture. The broad embrace of information and today's advanced technology has paved the way for this evolution. But pests cause substantial harm to farming, which has serious financial, ecological, and societal repercussions. That emphasizes the significance of computerized systems for early pest detection. Agricultural issues have recently emerged as a significant focus for machine learning-based research. This study offers an efficient Internet of Things (IoT)-enabled pest detection system that uses pest sound analytics in large agricultural fields. The system is built on a model called a Graph-based Pyramid Attention-multi module deep convolutional neural network (GPA-Net). The GPA-Net platform comprises a variety of essential subcomponents, a couple of which are the CSPNet, CTA, and SPA. The GPA-Net model has been trained using the sounds of the pests. The outcomes of the demonstrations revealed that the recommended GPA-Net model successfully identified the pest and outscored the state-of-the-art models YOLOv5, Faster RCNN, and ResNet-50 with 99.75% accuracy and an F1 score of 98.99%. © 2023 IEEE.

关键词： Internet of things

Stability and Limit Cycles of Fuzzy Inferences in a Recurrent Petri-like Neural Network

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Stability and Limit Cycles of Fuzzy Inferences in a Recurren...

International Joint Conference on Neural Networks (IJCNN)

作者： Lidia Ghosh Dipanjan Konar Amit Konar Atulya K. Nagar Dept. of Computer Science and Engineering Institute of Engineering and Management Kolkata Dept. of Electronics and Communication Engineering Institute of Radio Physics and Electronics Calcutta University Kolkata India ETCE Department Artificial Intelligence Laboratory Jadavpur University Kolkata India Dept. of Math and Computer Science Liverpool Hope University UK

This paper proposes a generic architecture of a recurrent fuzzy inferential neural network realized with Petri Nets. The proposed recurrent topology allows revision or updates of fuzzy singleton memberships of inferences, which may lead to limit cycles (sustained periodic oscillations) or stability of fuzzy inferences. Determining stability in such recurrent fuzzy neural network requires adaptation of fuzzy memberships for all propositions mapped at places of the Petri Net in parallel. The network is said to have attained stability, if after $k$ updates of memberships of the propositions, the steady-state values are reached for all the propositions of the network, where $k$ denotes the number of transitions in the Petri net. If fuzzy memberships of the propositions do not converge after $k$ updates, then the network yields sustained oscillations in memberships (called Limit Cycles). Detection of stability or limit cycles in such network requires users to wait for $k$ steps of fuzzy membership updates at all places of the network. To avoid the computational overhead for $k$ updating of memberships in the entire network, this paper makes an attempt to determine the condition of stability or limit cycles using Lyapunov stability theorem before the network is invoked for fuzzy membership updating and inference generation. The results of the analysis envisage that the condition of stability depends on the topological architecture and initial assignment of memberships at the places. The condition derived can be checked to test possible stability in the network. In case the condition for stability is not attainable, the network is expected to have limit cycles, which too can be detected without updating of memberships.

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Resource Allocation and Service Provisioning for Softwarized Network Slicing Using Game Theory: A Techno-Economic Approach

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Resource Allocation and Service Provisioning for Softwarized...

IEEE India Conference (INDICON)

作者： Deborsi Basu Samriddha Sanyal Goutam Das Uttam Ghosh Raja Datta G. S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Dept. of Electronics and Electrical Communication Engineering Indian Institute of Technology Kharagpur India Dept. of Computer Science and Data Science School of Applied Computational Sciences MMC TN USA

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Softwarized Network Slicing (SNS) is a forthcoming technological advancement that is going to transform conventional resource allocation techniques. Optimizing the flow of slice resources among the key network players, namely MVNOs (Mobile Virtual Network Operators), MIPs (Mobile Infrastructure Providers), and UEs (User Entities), is a complex and challenging task due to its complicated combinatorial nature. Moreover, maintaining the concurrent quality of service of end-users based on UE fairness is also difficult. Considering these factors, in this work, we have proposed a novel resource allocation strategy using a two-tier matching game between MIPs, MVNOs, and UEs, which not only minimizes the resource allocation cost but also maintains a decent UE satisfaction by providing desired UE SINR. An MILP (Mixed Integer Linear Programming) is formed for cost minimization, and the same is solved using a modified differed acceptance stable algorithm. We have compared our improvement with existing and latest state-of-the-art techniques.

关键词： Costs Network slicing Games Transforms Quality of service Interference Minimization Mixed integer linear programming Resource management Signal to noise ratio

On the Approximation of Singular Functions by Series of Non-integer Powers

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arXiv 2023年

作者： Zhao, Mohan Serkh, Kirill Dept. of Computer Science University of Toronto TorontoONM5S 2E4 Canada Dept. of Math. and Computer Science University of Toronto TorontoONM5S 2E4 Canada

In this paper, we describe an algorithm for approximating functions of the form f(x) = Rab xµσ(µ) dµ over [0, 1], where σ(µ) is some signed Radon measure, or, more generally, of the form f(x) = 〈σ(µ), xµ〉, where σ(µ) is some distribution supported on [a, b], with 0 c(log x)m = (−1)m〈δ(m)(µ − c), xµ〉, where a ≤ c ≤ b and m ≥ 0 is an integer. Given the desired accuracy ϵ and the values of a and b, our method determines a priori a collection of non-integer powers t1, t2, . . ., tN, so that the functions are approximated by series of the form f(x) ≈ PNj=1 cjxtj, and a set of collocation points x1, x2, . . ., xN, such that the expansion coefficients can be found by collocating the function at these points. We prove that our method has a small uniform approximation error which is proportional to ϵ multiplied by some small constants, and that the number of singular powers and collocation points grows as N = O(log 1ϵ). We demonstrate the performance of our algorithm with several numerical *** Codes 41A30, 41A58, 65D15 © 2023, CC BY.

关键词： Laplace transforms

A Continuation Method for Fitting a Bandlimited Curve to Points in the Plane

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arXiv 2023年

作者： Zhao, Mohan Serkh, Kirill Dept. of Computer Science University of Toronto TorontoONM5S 2E4 Canada Dept. of Math. and Computer Science University of Toronto TorontoONM5S 2E4 Canada

In this paper, we describe an algorithm for fitting an analytic and bandlimited closed or open curve to interpolate an arbitrary collection of points in R2. The main idea is to smooth the parametrization of the curve by iteratively filtering the Fourier or Chebyshev coefficients of both the derivative of the arc length function and the tangential angle of the curve, and applying smooth perturbations, after each filtering step, until the curve is represented by a reasonably small number of coefficients. The algorithm produces a curve passing through the set of points to an accuracy of machine precision, after a limited number of iterations. It costs O(N log N) operations at each iteration, provided that the number of discretization nodes is N. The resulting curves are smooth and visually appealing, and do not exhibit any ringing artifacts. The bandwidths of the constructed curves are much smaller than those of curves constructed by previous methods. We demonstrate the performance of our algorithm with several numerical *** Codes 65D10, 65D18, 68U05 © 2023, CC BY.

关键词： Interpolation

Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

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Exploiting Partial Observability and Optimized Simple State ...

20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-dept. exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 IEEE.

关键词： Autonomous agents

Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

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Investigating novel machine learning based intrusion detecti...

2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection