Reviewerly/exHarmony huggingface dataset:
Conversion of Controllers to have Integer State Matrix for Encrypted Control: Non-Minimal Order Approach

To implement an encrypted dynamic controller based on homomorphic encryption that operates for an infinite time horizon, it is essential for every component of the controller's state matrix to be an integer. In this paper, we tackle the challenge of converting a pre-designed controller into a new one with an integer state matrix, while preserving its control performance. This enables encrypted dynamic systems to be realized without re-encryption and approximation of control parameters. To achieve this, we allow the order of the controller to be increased so that the resulting closed-loop system becomes a non-minimal realization of the original closed-loop, without losing internal stability. Two approaches are proposed to design such controller with an integer state matrix. The first approach is to design the new controller as an estimator of the original closed-loop system, and the conditions on the estimator gain are derived. Our second approach is to formulate a problem of finding certain polynomials, whose solution leads to the design of the new controller. In a special case when the numerator of the plant transfer function is a constant, we provide a constructive method to obtain such solution.
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications

Federated learning (FL) is a decentralized learning framework wherein a parameter server (PS) and a collection of clients collaboratively trains a model via minimizing a global objective. Communication bandwidth is a scarce resource; in each round, the PS aggregates the updates from a subset of clients only. In this paper, we focus on non-convex minimization that is vulnerable to non-uniform and time-varying communication failures between the PS and the clients. Specifically, in each round <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> , the link between the PS and client <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$i$</tex> is active with probability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p_{i}^{t}$</tex> , which is unknown to both the PS and the clients. This arises when the channel conditions are heterogeneous across clients and are changing over time. We show that when the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p_{i}^{t}$</tex> 's are not uniform, Federated Average (FedAvg) - the most widely adopted FL algorithm - fails to minimize the global objective. Observing this, we propose Federated Postponed Broadcast (FedPBC) which is a simple variant of FedAvg. It differs from FedAvg in that the PS postpones broadcasting the global model till the end of each round. We show that FedPBC converges to a stationary point of the original objective. The introduced staleness is mild and there is no noticeable slowdown. Both theoretical analysis and numerical results are provided. On the technical front, postponing the global model broadcasts enables implicit gossiping among the clients with active links at round <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> . Despite <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${p}_{i}^{t}$</tex> 's are time-varying, we are able to bound the perturbation of the global model dynamics via the techniques of controlling the gossip-type information mixing errors.
Towards Trustworthy AI: Sandboxing AI-Based Unverified Controllers for Safe and Secure Cyber-Physical Systems

In the past decade, artificial-intelligence-based (AI-based) techniques have been widely applied to design controllers over cyber-physical systems (CPSs) for complex control missions (e.g., motion planning in robotics). Nevertheless, AI-based controllers, particularly those developed based on deep neural networks, are typically very complex and are challenging to be formally verified. To cope with this issue, we propose a secure-by-construction architecture, namely Safe-Sec-visor architecture, to sandbox AI-based unverified controllers. By applying this architecture, the overall safety and security of CPSs can be ensured simultaneously, while formal verification over the AI-based controllers is not required. Here, we consider invariance and opacity properties as the desired safety and security properties, respectively. Accordingly, by leveraging a notion of (augmented) control barrier functions, we design a supervisor to check the control inputs provided by the AI-based controller and decide whether to accept them. At the same time, a safety-security advisor runs in parallel and provides fallback control inputs whenever the AI-based controller is rejected for safety and security reasons. To show the effectiveness of our approaches, we apply them to a case study on a quadrotor controlled by an AI-based controller. Here, the initial state of the quadrotor contains secret information which should not be revealed while the safety of the quadrotor should be ensured.
HySST: An Asymptotically Near-Optimal Motion Planning Algorithm for Hybrid Systems

This paper proposes a stable sparse rapidly-exploring random trees (SST) algorithm to solve the optimal motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HySST, selects a vertex with minimal cost among all the vertices within the neighborhood of a random sample, subsequently extending the search tree through flow or jump, which is also chosen randomly when both regimes are possible. In addition, HySST maintains a static set of witness points where all vertices within each witness's neighborhood are pruned, except for the ones with lowest cost. We show that HySST is asymptotically near-optimal, namely, the probability of failing to find a motion plan with cost close to the optimal approaches zero as the number of iterations of the algorithm increases to infinity. The proposed algorithm is applied to a collision-resilient tensegrity multicopter system so as to highlight its generality and computational features.
A System-Theoretic Privacy-Informed Framework in Multi-Agent Systems

The problem of privacy-preserving in multi-agent systems corresponds to prohibiting the disclosure of agents' initial states while ensuring desired performance such as distributed average consensus. In this paper, a system-theoretic dynamic privacy-informed framework is developed. The proposed privacy framework relies on an obfuscation phase where a dynamic mask is inserted on the agents' state trajectories and masked outputs are exchanged amongst agents, rendering the physical states of an agent indiscernible by the other agents or external curious attackers (eavesdropper adversaries). Application of the proposed dynamic privacy scheme to well-known problems in multi-agent systems including average consensus and social opinion dynamics are presented.
Obstacle Avoidance in Dynamic Environments via Tunnel-Following MPC with Adaptive Guiding Vector Fields

This paper proposes a motion control scheme for robots operating in a dynamic environment with concave obstacles. A Model Predictive Controller (MPC) is constructed to drive the robot towards a goal position while ensuring collision avoidance without direct use of obstacle information in the optimization problem. This is achieved by guaranteeing tracking performance of an appropriately designed receding horizon path. The path is computed using a guiding vector field defined in a subspace of the free workspace where each point in the subspace satisfies a criteria for minimum distance to all obstacles. The effectiveness of the control scheme is illustrated by means of simulation.
On the Convergence of Natural Policy Gradient and Mirror Descent-Like Policy Methods for Average-Reward MDPs

It is now well known that Natural Policy Gradient (NPG) globally converges for discounted-reward MDPs in the tabular setting, with perfect value function estimates. However, the result cannot be directly used to obtain a corresponding convergence result for average-reward MDPs by letting the discount factor tend to one. In this paper, we prove that NPG also converges for average-reward MDPs in which each policy leads to an irreducible Markov chain. Since NPG can also be interpreted as a mirror descent based policy method, we then discuss extensions to non-tabular settings for mirror descent-based methods.
Optimal Stopping Problems in Low-Dimensional Feature Spaces: Lossless Conditions and Approximations

Optimal control problems can be solved by dy-namic programming. However, this method suffers from the curse of dimensionality. To resolve this, simplified versions of the original problem are often constructed in lower-dimensional feature spaces, leading to approximate policies. Yet, the connections between the original and the approximate policy and costs are rarely formalized. This paper addresses this challenge for optimal stopping problems. We start by providing conditions for lossless feature representations. This means that from an optimal policy obtained in feature space, an optimal policy in the original space can be constructed. Then, we search for modified versions of the original problem that (i) admit a lossless feature representation of far lower dimension; and (ii) provide upper and lower bounds on the optimal cost of the original problem. We can then use policies obtained in feature space using these modified problems to provide approximate policies for the original problem that are guaranteed to perform better than or equal to this aforementioned cost upper bound. We apply our tools in a high-dimensional precision farming intervention problem, where our tools allow for a dramatic decrease in complexity with only a small increase in the cost.
Technological Solutions to Online Toxicity: Potential and Pitfalls

Social media platforms present a perplexing duality, acting at once as sites to build community and a sense of belonging, while also giving rise to misinformation, facilitating and intensifying disinformation campaigns and perpetuating existing patterns of discrimination from the physical world. The first-step platforms take in mitigating the harmful side of social media involves identifying and managing toxic content. Users produce an enormous volume of posts which must be evaluated very quickly. This is an application context that requires machine-learning (ML) tools, but as we detail in this article, ML approaches rely on human annotators, analysts, and moderators. Our review of existing methods and potential improvements indicates that neither humans nor ML can be removed from this process in the near future. However, we see room for improvement in the working conditions of these human workers.
Online Reinforcement Learning in Markov Decision Process Using Linear Programming

We consider online reinforcement learning in episodic Markov decision process (MDP) with unknown transition function and stochastic rewards drawn from some fixed but unknown distribution. The learner aims to learn the optimal policy and minimize their regret over a finite time horizon through interacting with the environment. We devise a simple and efficient model-based algorithm that achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tilde{O}(LX\sqrt{TA})$</tex> regret with high probability, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> is the episode length, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> is the number of episodes, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> are the cardinalities of the state space and the action space, respectively. The proposed algorithm, which is based on the concept of “optimism in the face of uncertainty”, maintains confidence sets of transition and reward functions and uses occupancy measures to connect the online MDP with linear programming. It achieves a tighter regret bound compared to the existing works that use a similar confidence set framework and improves computational effort compared to those that use a different framework but with a slightly tighter regret bound.
Abstraction-based Motion Coordination Control for Multi-Robot Systems

This paper studies the motion coordination control problem for multiple mobile robots under a common workspace and reach-avoid tasks. Using abstraction-based techniques, we combine the offline and online control methods to propose a distributed motion coordination control strategy. In the offline control strategy, we partition the workspace to derive the graph for the offline planning, and construct the symbolic abstraction for each robot to design the individual controller offline. In the online control strategy, we provide a detection mechanism to check the existence of the potential robot collision, and implement the constructed symbolic abstraction and graph-searching techniques to resolve the robot collision. The combination of the offline and online control strategies results in the overall motion coordination control strategy for all robots.
The Application of Computer Assisted Translation Technique in Spanish-Chinese Interpretation

At this stage, college Spanish teaching has gradually shifted from the traditional theoretical and practical teaching methods to the teaching methods based on sports technology. In view of this, in this paper, the application situation of computer assisted translation technique in Spanish-Chinese interpretation was studied and analyzed. First of all, the status of computer aided translation technology and its application in the interpretation as well as related researches and theories were briefly introduced; the application process and evaluation algorithms of interpretation quality were detailed; with interpreters in an institution as the research sample, the corpus and interpretation quality were compared and evaluated; finally, the conclusion that interpreters need to coordinate the relationship between computer aided translation results and contexts was obtained, thus ensuring the accuracy of interpretation.
Factorization of Multi-Agent Sampling-Based Motion Planning

Modern robotics often involves multiple embodied agents operating within a shared environment. Path planning in these cases is considerably more challenging than in single-agent scenarios. Although standard Sampling-based Algorithms (SBAs) can be used to search for solutions in the robots' joint space, this approach quickly becomes computationally intractable as the number of agents increases. To address this issue, we integrate the concept of factorization into sampling-based algorithms, which requires only minimal modifications to existing methods. During the search for a solution, we can decouple (i.e., factorize) different subsets of agents into independent lower-dimensional search spaces once we certify that their future solutions will be independent of each other using a factorization heuristic. Consequently, we progressively construct a lean hypergraph where certain (hyper-)edges split the agents to independent subgraphs. In the best case, this approach can reduce the growth in dimensionality of the search space from exponential to linear in the number of agents. On average, fewer samples are needed to find high-quality solutions while preserving the optimality, completeness, and anytime properties of SBAs. We present a general implementation of a factorized SBA, derive an analytical gain in terms of sample complexity for PRM*, and showcase empirical results for RRG.
Design Of Financing Strategy Model of Financial Management Based on Data Mining Technology

With the continued expansion of India's market economy, competition among firms is heating up. To effectively navigate this severe market rivalry, corporate managers are increasingly prioritizing internal management. In comparison to traditional financial analysis approaches, data mining technology provides for a more in-depth comprehension of the underlying information inside large amounts of financial data. It assists financial analysts, investors, and decision-makers in acquiring full insights into the company's financial status and making appropriate judgments on pertinent topics. Both financial and non-financial data produced by traditional accounting information systems frequently confront issues of excess data and insufficient information. When presented with an excess of structured or semi-structured data, adding data mining technologies to the system allows for a detailed and effective projection of the enterprise's future development trajectory.To succeed in this competitive market setting and achieve sustainable development, businesses must realize the critical role of financial management, finance, and data mining technologies. Fundraising management is an important component of financial management for the majority of businesses. On the one hand, finance management has a direct impact on the funding process for small and medium-sized businesses, ultimately defining the extent and pace of their financing initiatives. On the other hand, by prudently allocating and managing finances, businesses may assure the smooth functioning of diverse operations, providing a firm basis for debt payback financing. This article will go into the research and study of financial management and financing for businesses, exploiting the possibilities of data mining technologies.
Cost Budget Management of Financial Sharing Service Model Based on Decision Tree Classification Algorithm in The Era of Big Data

With the financial informatization, the total amount of financial data is increasing rapidly. Relying solely on people's data processing and decision-making cannot meet the requirements of enterprise finance, nor is it conducive to the development of enterprise finance. As a new management mode, Financial Sharing Service absorbs the advantages of traditional financial management mode and overcomes its disadvantages to the greatest extent. After years of development and improvement, and with the application of big data and decision tree classification algorithm, it is paid more and more attention by enterprises. The utilisation of the classification algorithm facilitates the extraction of pertinent information, enabling managers to make informed decisions and offering robust assistance for enhancing the efficiency of cost budget management in financial sharing service modes. The increasing adoption of big data, cloud accounting, and mobile Internet technology, along with the growing need for business cost control and budget management, has led to a surge in the establishment of financial sharing service centres by group enterprises. This study examines the cost budget management of a financial sharing service model using the decision tree classification method within the context of big data.. Combined with the application of contemporary big data, it analyzes the formulation, compilation and evaluation of cost budget target on the basis of financial sharing model for reference only.
Performance Comparison of Redis, Memcached, MySQL, and PostgreSQL: A Study on Key-Value and Relational Databases

This paper investigates and compares the performance of relational databases (MySQL and PostgreSQL) and key-value databases (Memcached and Redis) under various test loads. The study utilizes the Yahoo! Cloud Serving Benchmark to simulate diverse workloads and measure the behavior of these databases in different scenarios. The primary focus is on evaluating run time, throughput, and average latency metrics to understand how each database type handles varying thread levels and workload intensity. The outcomes of this research provide valuable insights into the scalability and efficiency aspects of relational databases. By conducting a comprehensive performance comparison, the study aims to assist database designers and developers in selecting the most suitable database option based on specific requirements. The findings contribute to informed decision-making regarding the choice between key-value and relational databases in various data storage scenarios.
Asynchronous Decentralized Q-Learning in Stochastic Games

Non-stationarity is a fundamental challenge in multi-agent reinforcement learning (MARL), where agents update their behaviour as they learn. In multi-agent settings, individual agents may have an incomplete view of the actions of others, which can complicate the learning process. Many theoretical advances in MARL avoid the challenge of non-stationarity by coordinating the policy updates of agents in various ways, including synchronizing times at which agents are allowed to revise their policies. In this paper, we study an asynchronous variant of the decentralized Q-learning algorithm, a recent MARL algorithm for stochastic games. We provide sufficient conditions under which the asynchronous algorithm drives play to equilibrium with high probability. In this generalization, players need not agree on the schedule of policy update times, and may change their policies at their own separately selected times. This work extends the applicability of the decentralized Q-learning algorithm to settings in which parameters are selected in an independent manner, and tames non-stationarity without imposing the coordination assumptions of prior work.
Statistical Analysis of Ecological Mathematical Model Based on Data Warehouse

Persistence of ecosystems, existence and stability of periodic and almost periodic solutions, and global attractiveness are important research contents in ecological mathematical theory. This article takes the ocean as an example to illustrate. The marine ecological model management system integrates marine technology, Internet technology and database technology. The purpose is to collect, organize and analyze mathematical models related to marine ecosystems, integrate them according to certain classification principles, and store them in the form of text. In the database, the query of the database according to the important parameters in the mathematical model or the classification of the mathematical model is provided on the Internet, and the queried mathematical model is displayed on the screen through the browser. This paper adopts the method of data warehouse. How to effectively use resources is an important aspect of whether to take the initiative in competition. Data warehouse can play the characteristics of information processing and has broad application prospects in the face of competition in the field of telecommunications.
Breast Cancer Detection Technique using Machine Learning Classifiers

Most common Cancer which is commonly found amongst women is Breast Cancer and if we talk about the cause of death then it rates the second highest. The most effective way to cure cancer is to track cancer in the early stages, so if detected early and treated properly it may get cured more effectively. To get the minimum error, the most important thing is to develop the technique which gives us the highest accuracy of prediction. In this research, we are using machine-learning algorithms that will be used to compare the accuracy here, we will present a prediction model using machine-learning models including LR, RF, DT, etc. are tested on the Breast Cancer Wisconsin dataset, and different datasets. By using the results of this research, the efficiency of various strategies can be defined, and the most precise technique may be applied to accurately forecast breast cancer. This research was conducted using a set of parameters, however, in the future suitable prediction techniques will be used.