Governing algorithms and artificial intelligence (AI) involves multidisciplinary work across different fields to ensure human control over technology. This article aims to show how computer science and social sciences...
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Governing algorithms and artificial intelligence (AI) involves multidisciplinary work across different fields to ensure human control over technology. This article aims to show how computer science and social sciences must work together to rethink how we oversee algorithms and AI in a more democratic way.
We consider the problem of active sensing and sequential beam tracking at mmWave frequencies and above. We focus on the setting of aerial communications between a quasi-stationary receiver and mobile transmitter, for ...
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We consider the problem of active sensing and sequential beam tracking at mmWave frequencies and above. We focus on the setting of aerial communications between a quasi-stationary receiver and mobile transmitter, for example, a gateway array tracking a small agile drone, where we formulate the problem to be equivalent to actively sensing and tracking an optimal beamforming vector along the single dominant (often line-of-sight) path. In this setting, an ideal beam points in the direction of the angle of arrival (AoA) with sufficiently high resolution to ensure high beamforming gain. However, narrow beams are inherently sensitive to stochastic mobility. Without active sensing, narrow beam alignment can only be maintained in the case of highly predictive mobility with low prediction error. We pose the problem of active beam tracking and communication as a partially observed Markov decision problem (POMDP) with an expected average cost constraint. We establish the existence of a solution to the dynamic programming equation under reasonable technical assumptions. Drawing on the insight obtained from this solution, we propose an active joint sensing and communication algorithm for tracking the AoA through evolving a Bayesian posterior probability belief which is utilized for a sequential beamforming selection. Our algorithm relies on an integrated strategy of adaptive allocation of pilot versus data symbols as well as an active selection of beamforming vectors that trades off mutual information between the AoA and measurements (sensing) against spectral efficiency (communication). Through extensive numerical simulations, we analyze the performance of our proposed algorithm under various stochastic mobility models and demonstrate significant improvements over existing strategies. We also consider the impact of model mismatch on the performance of our algorithm which shows a good degree of robustness to model mismatch.
Temporal data are ubiquitous in real-world applications, and they can be generally divided into two categories: 1) synchronous temporal data which are basically equivalent to time series data;and 2) the asynchronous d...
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Temporal data are ubiquitous in real-world applications, and they can be generally divided into two categories: 1) synchronous temporal data which are basically equivalent to time series data;and 2) the asynchronous data which are often in the form of event data with a time stamp in continuous time-space. In fact, the event data are often converted to the time series by aggregating the event count in equal time intervals in many previous approaches. While it is often of one's greater interest to directly establish models based on the raw event data whose time stamps carry useful information, especially for those time-sensitive tasks, ranging from earthquake prediction, crime analysis, to infectious disease diffusion forecasting, etc. Developing the spatio-temporal point process and the related applications is the theme of this Special Issue, which treats an event as a point in the spatio-temporal space, with possibly extra attributes. The model captures the instantaneous happening rate of the events and their potential dependency. The derived use cases often refer to future events prediction, and causality estimation.
Dynamic optimization problems are pervasive in various fields, ranging from chemical process control to aerospace, autonomous driving, physics, robotics, and beyond. These problems involve optimizing a dynamic system ...
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Dynamic optimization problems are pervasive in various fields, ranging from chemical process control to aerospace, autonomous driving, physics, robotics, and beyond. These problems involve optimizing a dynamic system considering inputs, parameters, constraints, and a cost function. For dynamic optimization, two broad classes of strategies emerge: deterministic and heuristic methods. Deterministic optimization methods leverage the analytical properties of the problem, generating a sequence of points that converge to the optimal solution. These techniques are suitable when explicit models and constraints are available and easy to evaluate. On the other hand, heuristic approaches treat the problem as a black box, relying on iterative improvements to a fitness function. They are employed for complex problems with challenging system models or significant uncertainty.
It is an honor for me to write this overview article and share with the reader the topic of circuits and security. This topic has triggered my curiosity since I started as a Ph.D. student.
It is an honor for me to write this overview article and share with the reader the topic of circuits and security. This topic has triggered my curiosity since I started as a Ph.D. student.
This special section of IEEE/ACM Transactions on Computational Biology and Bioinformatics presents extended versions of some of the best papers accepted at the Eighth International Conference on algorithms for Computa...
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This special section of IEEE/ACM Transactions on Computational Biology and Bioinformatics presents extended versions of some of the best papers accepted at the Eighth International Conference on algorithms for Computational Biology, AlCoB 2021, held online due to the COVID-19 pandemic on November 9-11, 2021. The conference was organized by the Department of Computer Science at the University of Montana and the Institute for Research Development, Training and Advice - IRDTA, Brussels/London.
Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This te...
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Originating in the artificial intelligence literature, optimistic planning (OP) is an algorithm that generates near-optimal control inputs for generic nonlinear discrete-time systems whose input set is finite. This technique is, therefore, relevant for the near-optimal control of nonlinear switched systems for which the switching signal is the control, and no continuous input is present. However, OP exhibits several limitations, which prevent its desired application in a standard control engineering context, as it requires, for instance, that the stage cost takes values in [0.1], an unnatural prerequisite, and that the cost function is discounted. In this article, we modify OP to overcome these limitations, and we call the new algorithm OPmin. We then analyze OPmin under general stabilizability and detectability assumptions on the system and the stage cost. New near-optimality and performance guarantees for OPmin are derived, which have major advantages compared to those originally given for OP. We also prove that a system whose inputs are generated by OPmin in a receding-horizon fashion exhibits stability properties. As a result, OPmin provides a new tool for the near-optimal, stable control of nonlinear switched discrete-time systems for generic cost functions.
A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes...
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A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.
The Bayesian Optimization algorithm (BOA) is one of the most prominent Estimation of Distribution algorithms. It can detect the correlation between multiple variables and extract knowledge on regular patterns in solut...
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The Bayesian Optimization algorithm (BOA) is one of the most prominent Estimation of Distribution algorithms. It can detect the correlation between multiple variables and extract knowledge on regular patterns in solutions. Bayesian Networks (BNs) are used in BOA to represent the probability distributions of the best individuals. The BN's construction is challenging since there is a trade-off between acuity and computational cost to generate it. This trade-off is determined by combining a search algorithm (SA) and a scoring metric (SM). The SA is responsible for generating a promising BN and the SM assesses the quality of such networks. Some studies have already analyzed how this relationship affects the learning process of a BN. However, such investigation had not yet been performed to determine the bond linking the selection of SA and SM and the BOA's output quality. Acting on this research gap, a detailed comparative analysis involving two constructive heuristics and four scoring metrics is presented in this work. The classic version of BOA was applied to discrete and continuous optimization problems using binary and floating-point representations. The scenarios were compared through graphical analyses, statistical metrics, and difference detection tests. The results showed that the selection of SA and SM affects the quality of the BOA results since scoring metrics that penalize complex BN models perform better than metrics that do not consider the complexity of the networks. This study contributes to a discussion on this metaheuristic's practical use, assisting users with implementation decisions.
In order to solve the "minimum trap" of Artificial Potential Field and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning algorithm based on improved artif...
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In order to solve the "minimum trap" of Artificial Potential Field and the limitation of traditional path planning algorithm in dynamic obstacle environment, a path planning algorithm based on improved artificial potential field is proposed. Firstly, a virtual potential field detection circle model (VPFDCM) with adjustable radius is proposed to detect the "minimum trap" formed by the repulsion field of obstacles in advance. And the motion model of unmanned vehicle is established. Combined with the improved reinforcement learning algorithm based on Long Short-Term Memory(LSTM), the radius of virtual potential field detection circle is adjusted to achieve effective avoidance of dynamic obstacles. The reliable online collision free path planning of unmanned vehicle in semi closed dynamic obstacle environment is realized. Finally, the reliability and robustness of the algorithm are verified by MATLAB simulation. The simulation results show that the improved artificial potential field can effectively solve the problem of unmanned vehicle falling into the "minimum trap" and improve the reliability of unmanned vehicle movement. Compared with the traditional artificial potential field method, the improved artificial potential field method can achieve more than 90% success rate in obstacle avoidance.
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