Enforcement Algorithm Research Articles

Navigating data governance risks: Facial recognition in law enforcement under EU legislation

Facial recognition technologies (FRTs) are used by law enforcement agencies (LEAs) for various purposes, including public security, as part of their legally mandated duty to serve the public interest. While these technologies can aid LEAs in fulfilling their public security responsibilities, they pose significant risks to data protection rights. This article identifies four specific risks associated with the use of FRT by LEAs for public security within the frameworks of the General Data Protection Regulation and Artificial Intelligence Act. These risks particularly concern compliance with fundamental data protection principles, namely data minimisation, purpose limitation, data and system accuracy, and administrative challenges. These challenges arise due to legal, technical, and practical factors in developing algorithms for law enforcement. Addressing these risks and exploring practical mitigations, such as broadening the scope of data protection impact assessments, may enhance transparency and ensure that FRT is used for public security in a manner that serves the public interest.

Decentralized deadlock-free enforcement of message orderings in message-based systems

Message-based systems usually consist of distributed components that communicate using asynchronous message passing. In such systems, particular message orderings may violate some required properties. Given an automata-based specification of unwanted message sequences, we propose a decentralized deadlock-free runtime enforcement algorithm to prevent the formation of such sequences. In our approach, components are equipped with monitors executed concurrently. A component is only blocked before sending or receiving the last message of a sequence, until its associated monitor checks that such a message does not complete an unwanted sequence. According to the specification of unwanted sequences, some blocked components may suffer from a deadlock. Our deadlock-free algorithm guarantees that monitors detect and resolve such deadlocks by improving the existing deadlock detection algorithms. We evaluate the efficiency and scalability of our approach in terms of the communication overhead, the prevention latency, and the overhead of deadlock detection through simulation.

Polynomial-Time Verification and Enforcement of Delayed Strong Detectability for Discrete-Event Systems

Detectability is a fundamental property in partially observed dynamical systems. It describes whether one can use observed output sequences to determine the current and subsequent states. Delayed detectability generalizes detectability in the sense that when doing state estimation at a time instant, some outputs after the instant are also considered, making the estimation more accurate. In this article, we use a novel concurrent-composition method to give polynomial-time algorithms for verifying several delayed versions of strong detectability of discrete-event systems modeled by finite-state automata in the contexts of formal languages and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\omega$</tex-math></inline-formula> -languages without any assumption, which strengthen the polynomial-time verification algorithms in the literature based on two fundamental assumptions of liveness (aka deadlock-freeness) and divergence-freeness (the former implies an automaton will never halt and the latter implies the running of an automaton will always be eventually observed). In addition, based on our verification algorithms, we obtain polynomial-time algorithms for enforcing these notions of delayed strong detectability in an open-loop manner, which work in a different way compared with the existing exponential-time enforcement algorithms under the supervisory control framework in a closed-loop manner. Moreover, by using our methods, polynomial-time enforcement algorithms can be designed for many polynomially verifiable inference-based properties such as diagnosability and predictability.

Parametric Aeroelastic Reduced-Order Model with State-Consistence Enforcement

State-space reduced-order models (ROMs) constructed by traditional system identification methods suffer from the state-inconsistence issue and poor ROM interpolatability for varying flight conditions. This paper presents a novel system identification method and a state-consistence enforcement (SCE) algorithm to generate a state-space aeroelastic (AE)-ROM within a broad flight parameter space. A new regularization term is proposed to modify the traditional autoregressive exogenous formulation and specifically penalize state inconsistence between ROMs at varying flight conditions. The new formulation cast in the form of the generalized Tikhonov regularization problem can be computed very efficiently and produce fundamentally state-consistent ROMs. Rigorous numerical analysis that compares the state-consistent AE-ROM with the full-order model and the ROM without SCE is also conducted. The results convincingly prove that the proposed approach significantly improves the state consistence of ROMs at varying flight conditions. To the best of our knowledge, this research represents the first effort of developing a state-consistent data-driven AE-ROM and enabling aeroelasticity analysis within a broad flight parameter space.

On checking dissipativity of parameterized linear and time‐invariant circuits and systems

SummaryA novel dissipativity characterization is proposed for linear time‐invariant circuits and systems whose dynamic behavior depends on one external parameter. The proposed formulation extends to the parameterized case standard Hamiltonian‐based algebraic dissipativity characterizations and is structured as an underdetermined multiparameter eigenvalue problem. Resulting from a polynomial parameterization, the proposed characterization leads to an effective set of algorithms that are able to determine the regions of local dissipativity and local activity in the frequency‐parameter plane, which in turn can be exploited by dissipativity enforcement algorithms to produce uniformly passive models. The reference application of proposed technique is behavioral modeling of complex circuits or systems, whose dynamic behavior can be compressed into a low‐order system through dedicated model order‐reduction processes. Various examples ranging from integrated components to microstrip filters and networks are used to illustrate the proposed characterization.

General-Purpose Algorithm for Two-Material Minimum Feature Size Enforcement of Freeform Nanophotonic Devices

General-Purpose Algorithm for Two-Material Minimum Feature Size Enforcement of Freeform Nanophotonic Devices

Improving Accuracy After Stability Enforcement in the Loewner Matrix Framework

The Loewner matrix (LM) is a powerful macromodeling technique that uses sampled data in the frequency domain for representing dynamical systems in a descriptor form. As stability cannot be enforced during the construction process, a postprocessing step is required to ensure a stable macromodel, which reduces its accuracy. In this article, two techniques are incorporated and evaluated with the LM framework in this postprocessing phase. The first one is an efficient sign-pole-flipping technique for stability enforcement. The other one is a matrix updating technique applied after the stability enforcement of the model. The proposed approaches are evaluated using data in the scattering parameter form for printed circuit board via interconnects examples and an electromagnetic shielding structure and compared to the state-of-the-art stability enforcement algorithms. For completeness, we will also compare the results with respect to the macromodels created by the vector fitting algorithm. It was found that by the proposed flipping and matrix updating approaches, an error reduction by a factor between 2 and 20 was achieved with respect to the previously reported methods.

A rigorous and efficient explicit algorithm for irreversibility enforcement in phase-field finite element modeling of brittle crack propagation

In the present work, a computationally efficient and explicit algorithm for the rigorous enforcement of the irreversibility constraint in the phase-field modeling of brittle fracture is presented. The proposed approach is staggered and relies on the alternate minimization of the total energy functional. The phase-field evolution turns out to be governed by a complementarity boundary-value problem, where the complementarity stems from the irreversibility, while the boundary-value problem is originated by the presence of the gradient term in the phase-field functional. Several different techniques have been proposed in the literature to account for damage irreversibility in a computationally effective way. Following a similar approach proposed in the past for a gradient-plasticity problem, a particularly simple and effective solution strategy based on the Projected Successive Over-Relaxation (PSOR) method for constrained optimization, where an iterative explicit scheme is used for the solution of symmetric linear complementarity problems, is presented. Even though the proposed method is restricted to linear complementarity problems, it can be applied to the numerical simulation of a wide range of problems discussed in the literature. The performance of the suggested solution algorithm on a number of test cases is compared with that of a recently proposed penalty approach for the approximated enforcement of irreversibility.

Decentralized LTL Enforcement

We consider the runtime enforcement of Linear-time Temporal Logic formulas on decentralized systems with no central observation point nor authority. A so-called enforcer is attached to each system component and observes its local trace. Should the global trace violate the specification, the enforcers coordinate to correct their local traces. We formalize the decentralized runtime enforcement problem and define the expected properties of enforcers, namely soundness, transparency and optimality. We present two enforcement algorithms. In the first one, the enforcers explore all possible local modifications to find the best global correction. Although this guarantees an optimal correction, it forces the system to synchronize and is more costly, computation and communication wise. In the second one, each enforcer makes a local correction before communicating. The reduced cost of this version comes at the price of the optimality of the enforcer corrections.

Automated Enforcement of Irrigation Regulations and Social Pressure for Water Conservation

This study evaluates two interventions for residential water conservation. Comparing households across an enforcement algorithm’s cutoff using a regression discontinuity design, we find that automated irrigation violation warnings cause substantial water conservation but also shift some consumption from regulated to unregulated hours within the week. In contrast, we show using data from a randomized experiment with the same customers that normative Home Water Reports reduce water use by a much smaller amount, but that this social pressure is effective during all hours both before and after automating irrigation policy enforcement. Our findings highlight the merits of implementing multidimensional conservation programs.

An improved passivity enforcement algorithm for transmission line models using passive filters

This paper proposes a simple but effective method based on shunt passive filters to enforce passivity on a frequency dependent transmission line model for multi-conductor cables and overhead lines. The passivity enforcement algorithm is applied to a widely-used frequency dependent line model in EMT-type software, the Universal Line Model. The passivity violating regions of the transmission line model are identified using the frequency sweep method. The passive shunt series RLC filters are added to the nodes of the transmission lines to eliminate passivity violations. Examples of multi-conductor underground cable systems are presented to demonstrate the validity of the proposed approach.

Презумпція правомірності рішень платника податків у практиці адміністративних судів

Статтю присвячено детальному аналізу правової природи презумпції правомірності рішень платника податків як одного з вихідних положень податкового законодавства, змістовні аспекти якого розвивалися в межах судової практики. Так, автором було сконцентровано увагу на специфічних рисах презумпції правомірності рішень платників податків. Відповідні риси встановлюються на основі дослідження, серед іншого, практики Європейського суду з прав людини та Європейської комісії за демократію через право (Венеціанська комісія). Як дослідницьке завдання автором визначено специфіку взаємозв'язків судової доктрини презумпції правомірності рішень платника податків з іншими судовими доктринами, а саме: доктриною ділової мети, доктриною добросовісності платника податків тощо. Проведено розмежування судової доктрини презумпції правомірності рішень платника податків від доктрини добросовісності платника податків. Зазначено, що презумпція правомірності рішень покликана забезпечити правову визначеність у податковому правозастосуванні, саме тоді, коли закон передбачає неоднозначне трактування прав і обов'язків учасників податкових відносин. У статті проаналізовано актуальну судову практику, у рамках якої сформовано основні концептуальні вимоги даного презумптивного положення податкового законодавства. Встановлено, що суди розглядають презумпцію правомірності рішень платника податків як спеціальне правило, яке встановлює вимогу, відповідно до якої суб'єкти правозастосування повинні трактувати всі нормативно обумовлені сумніви на користь платника податків. Підкреслюється, що саме презумпція правомірності рішень перебуває в нерозривній єдності з вимогами принципу правової визначеності. Сам принцип правової визначеності є елементом принципу верховенства права. Саме дане вихідне положення забезпечує прогно-зованість правозастосовної практики. Принцип-пре-зумпція правомірності рішення платника податків є специфічним інструментом (техніко-юридичний прийом), який дозволяє забезпечити реалізацію інтересів платника податків у разі фактичної невизначеності алгоритмів правозастосування.

Extended constraint enforcement formulations for finite-DOF systems based on Gauss’s principle of least constraint

This paper aims to contribute to formulations of mathematical models for finite-DOF systems based on the Gauss’s principle of least constraint. A new theorem allows to extend the definition of the Gaussian deviation function, overcoming, without requiring any change of variables, algebraic limitations that arise when this principle is applied to systems for which the generalized mass matrices become singular under relaxation of constraints. As a result of this new theorem, the recursive constraint enforcement algorithm based on Udwadia–Kalaba equation, proposed within the scope of the modular modeling methodology for finite-DOF systems, is generalized. Finally, in a case study based on the classical problem of a knife-edge disc rolling in a plane, three constraint enforcement strategies that combine the application of the theorem herein proposed with conventional numerical integration methods have its results confronted, in two simulation scenarios, with those obtained from a benchmark model of this system.

Modeling and Analyzing Dynamic Fault-Tolerant Strategy for Deadline Constrained Task Scheduling in Cloud Computing

Cloud computing has been increasingly concerned in scientific computing area. More and more enterprises and research institutes have migrated their applications to the clouds. Due to the complexity of cloud computing system in structural and behavioral aspects, how to design the fault tolerant cloud computing system becomes a challenging problem. This paper investigates the modeling and analysis of fault tolerant strategy for deadline constrained task scheduling in cloud computing. First, a formal description language is defined to accurately model the different components of cloud application, and use it to characterize the operational mechanisms and fault behaviors. Second, we propose a fault tolerant strategy, which includes the scheduling mechanism, synchronization mechanism, and exception mechanism, to dynamically compute the execution mode and required virtual machine for tasks, thus ensuring the reliability and real-time requirement of cloud application. An enforcement algorithm is also designed to realize the proposed strategy. Third, the techniques of Petri nets are provided to analyze and validate the correctness of proposed method. Finally, several experiments are done to illustrate that the reliability of cloud application is improved and its deadline is met.

Predictive policing: utopia or dystopia? On attitudes towards the use of big data algorithms for law enforcement

The use of predictive AI tools to improve decision-making in relation to crime prevention and investigation is a reality. They are being implemented almost before we fully understand how they work, while we make relevant legal decisions that may determine the progress of the technology, and long before we can predict their full impact. This paper addresses the attitudes towards this technological revolution applied to criminal justice, focusing in particular on its use by police. The first section summarises and describes the techniques and technologies that make up predictive policing. Subsequently, the main part of the study analyses the attitudes with which this technology has been received. These range from the optimism of those who defend its immediate implementation as a way to improve police objectivity and efficiency, to the pessimism of those who see its use as strengthening a dystopia of state control and surveillance. Two apparent extremes that correspond to the transition from optimism to technological pessimism of the twentieth century. The article concludes with a defence of a realistic, critical and informed view of the use of these predictive algorithms. A vision that, on the one hand, accepts that there are no neutral technologies, yet does not fall into fatalism and technophobia; and, on the other hand, places the human being and the legitimate police function at the centre of the algorithmic equation while redefining its objectives based on the scientific evidence applied to each individual technology.

Enforcing Passivity of Parameterized LTI Macromodels via Hamiltonian-Driven Multivariate Adaptive Sampling

We present an algorithm for passivity verification and enforcement of multivariate macromodels whose state-space matrices depend in closed form on a set of external or design parameters. Uniform passivity throughout the parameter space is a fundamental requirement of parameterized macromodels of physically passive structures, that must be guaranteed during model generation. Otherwise, numerical instabilities may occur, due to the ability of nonpassive models to generate energy. In this paper, we propose the first available algorithm that, starting from a generic parameter-dependent state-space model, identifies the regions in the frequency-parameter space where the model behaves locally as a nonpassive system. The approach we pursue is based on an adaptive sampling scheme in the parameter space, which iteratively constructs and perturbs the eigenvalue spectrum of suitable skew-Hamiltonian/Hamiltonian pencils, with the objective of identifying the regions where some of these eigenvalues become purely imaginary, thus pinpointing local passivity violations. The proposed scheme is able to detect all relevant violations. An outer iterative perturbation method is then applied to the model coefficients in order to remove such violations and achieve uniform passivity. Although a formal proof of global convergence is not available, the effectiveness of the proposed implementation of the passivity verification and enforcement schemes is demonstrated on several examples.

Matbase auto Function Non-relational Constraints Enforcement Algorithms

Matbase auto Function Non-relational Constraints Enforcement Algorithms

Matbase auto Function Non-relational Constraints Enforcement Algorithms

MatBase is an intelligent prototype data and knowledge base management system based on the Relational (RDM), Entity-Relationship, and (Elementary) Mathematical ((E)MDM) Data Models, built upon Relational Database Management Systems (RDBMS). ((E)MDM) has 61 constraint types, out of which21 apply to autofunctions as well. All five relational (RDM) constraint types are passed by MatBase for enforcement to the corresponding RDBMS host. All non-relational ones are enforced by MatBase through automatically generated code. This paper presents and discusses both the strategy and the implementation of MatBase autofunction non-relational constraints enforcement algorithms. These algorithms are taught to our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the Department of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucharest Polytechnic University, as well as successfully used by two Romanian software companies.

Efficient Non-Dominated Multi-Objective Genetic Algorithm (NDMGA) and network security policy enforcement for Policy Space Analysis (PSA)

Computer network security is the first line of protection for guaranteeing information reliability. A computer network can be threatened when it does not have a well-designed or properly implemented network security policy. The major issue is that network operators cannot always support their network security strategy. Network operators often depend on security facilities to defend their Information Technology (IT) organizations. Different forms of security policies concerned with the network are distinct and circulated among various security middleboxes (MBs) organized into networks. Conversely, due to the inherent sophistication of security policies, it is not appropriate to use recent path-wise implementation algorithms. The present study solves the problem of policy implementation with MBs relatively faster compared to existing methods. In particular, this research models security policy implementation as a Weighted K Set Covering Problem (WKSCP) and employs a computational-geometry-based Policy Space Analysis (PSA) tool for a set of procedures as the security mechanism. Using the PSA tool leads to a Non-Dominated Multi-Objective Genetic Algorithm (NDMGA), which reveals the inherent complexities involved in a security policy and informs implementation of the policy enforcement algorithm. The resolution to this issue is to determine a collection of enforcement network nodes for an MB to execute on-datapath examination with globally distinct security policies. Therefore, the chosen policy enforcement nodes should be initially positioned on paths that are related to security strategies. Compared to the scope-wise strategy enforcement mechanism, where the procedures are executed over the objects of the network path, the scheme in this present work is implemented over the objects pertaining to the network nodes. A secure node may contain multiple paths, thus storing a large possible flow space that preserves the distribution and circumvents the ineffective usage of network bandwidth due to random traffic steering. Compared to current methodologies, the suggested NDMGA-based PSA tool yielded a 95% decrease in policy implemented nodes and a 93% decline in implemented rules.

Modified priority flood and global slope enforcement algorithm for topographic processing in physically based hydrologic modeling applications

We present a topographic processing tool, PriorityFlow, designed to improve the topographic input datasets which are required for physically based hydrologic models. PriorityFlow is available on GitHub, combines multiple processing approaches, and includes several key innovations over previous work: (1) a novel approach for incorporating a-priori stream network information with priority flood routing DEM processing, (2) improved slope calculations that incorporate flow directions, (3) options for incorporating slopes perpendicular to the primary flow direction, (4) more flexible options for defining subbasins and enforcing slopes along stream reaches. Here we document the approach and demonstrate the impact of topographic processing choices on hydrologic simulations. Results demonstrate that the modified priority flood algorithm can better reproduce observed stream networks without relying on traditional stream burning approaches. Additionally, we demonstrate the impact of slope processing options on runoff timing and magnitude. Specifically, we highlight the sensitivity of the hydrograph to slope processing choices along stream reaches.