Programming-based Approach Research Articles

Shared Spectrum Cooperative Networks: A Linear Programming-Based Approach

Shared Spectrum Cooperative Networks: A Linear Programming-Based Approach

AxOMaP : Designing FPGA-based A ppro x imate Arithmetic O perators using Ma thematical P rogramming

With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-constrained systems. As a result, emerging models of computation, such as approximate and stochastic computing, that leverage the inherent error-resilience of such algorithms are being actively explored for implementing ML inference on resource-constrained systems. Approximate computing (AxC) aims to provide disproportionate gains in the power, performance, and area (PPA) of an application by allowing some level of reduction in its behavioral accuracy (BEHAV). Using approximate operators (AxOs) for computer arithmetic forms one of the more prevalent methods of implementing AxC. AxOs provide the additional scope for finer granularity of optimization, compared to only precision scaling of computer arithmetic. To this end, the design of platform-specific and cost-efficient approximate operators forms an important research goal. Recently, multiple works have reported the use of AI/ML-based approaches for synthesizing novel FPGA-based AxOs. However, most of such works limit the use of AI/ML to designing ML-based surrogate functions that are used during iterative optimization processes. To this end, we propose a novel data analysis-driven mathematical programming-based approach to synthesizing approximate operators for FPGAs. Specifically, we formulate mixed integer quadratically constrained programs based on the results of correlation analysis of the characterization data and use the solutions to enable a more directed search approach for evolutionary optimization algorithms. Compared to traditional evolutionary algorithms-based optimization, we report up to 21% improvement in the hypervolume, for joint optimization of PPA and BEHAV, in the design of signed 8-bit multipliers. Further, we report up to 27% better hypervolume than other state-of-the-art approaches to DSE for FPGA-based application-specific AxOs.

Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

An Approximate Dynamic Programming Approach to Dynamic Stochastic Matching

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov decision processes (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming-based approach to approximate dynamic programming. This approach can provide both feasible control policies and bounds on the MDPs’ optimal policy value, which can be used to establish optimality gaps. However, the approximate linear programs (ALPs) resulting from this approach can often be difficult to solve. To address this computational challenge, we derive novel ALP reformulations that can be used for a broad class of dynamic stochastic matching problems that incorporate, among others, possible match failures and certain restrictions on feasible matchings. We show that these ALP reformulations can be solved efficiently and applied to a broad class of dynamic matching problems. In addition, our numerical results indicate that our ALP reformulations can produce tight bounds that allow us to establish near-optimal policy performance for a broad set of problem instances. Thus, ALP reformulations can present an attractive alternative for applications that involve dynamic stochastic matching. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0203 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0203 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Automated design of action advising trigger conditions for multiagent reinforcement learning: A genetic programming-based approach

Action advising is a popular and effective approach to accelerating independent multiagent reinforcement learning (MARL), especially for the learning systems that all the agents learn from scratch and the roles of them (advisors or advisees) cannot be predefined. The key component of action advising is the trigger condition, which answers the question of when to advise. Previous works mainly focus on the design of novel trigger conditions manually; however, since those conditions are often designed heuristically, the performance may be affected by the preference of the designers. To this end, this paper tries to solve the action advising problem automatically using genetic programming (GP), an evolutionary computation technique. A framework incorporating GP to action advising is provided, together with a novel population initialization method to enhance the performance. Empirical studies are provided to demonstrate the effectiveness of the proposed framework. More importantly, thanks to the high transparency of GP, comprehensive analysis is also conducted based on the results. Interesting and inspiring insights to the action advising problem are condensed from the discussions, which may provide guidance to future works.

An Effective Deployment of Controllers in Software-Defined Networks

Network performance has become a crucial consideration in ensuring a flawless user experience in the era of data-intensive applications and cloud computing. Traditional Networks are not suitable for solving such issues, so Software-defined networking (SDN) has evolved as a game-changing technology, promising network flexibility, scalability, and centralized control. However, some challenges remain open to the research, and one of the most popularly known challenges is controller problem placement (CPP). Latency is also a major consideration in the network. However, implementing low-latency communication in SDN systems remains a difficult issue, especially as networks become more complicated. In the realm of SDN, where centralized control meets distributed data planes, minimizing latency between switches and controllers emerges as a pivotal challenge. This article delves into the critical concept of optimizing controller placement within SDN networks to minimize latency, with a primary focus on considering the switch-controller distance. The proposed work uses a Linear programming-based approach to identify the optimal controller location in the network by considering the switch-controller distance and the latency is calculated. Further, a comparison of the proposed work with a heuristic-based approach is performed which shows that the proposed work performs well in terms of latency.

Finansal Piyasalarda Algoritmik Ticaret İçin Genetik Algoritma Temeli Yaklaşım

Software that enables realtime buy and sell transactions in financial markets according to predetermined conditions is called algorithmic trading. When developing algorithmic trading robots, indicators used in technical analysis are generally used. For the strategy selection of the robot, a process called Backtest is performed on the historical time series. The purpose of the Backtest process is the process of obtaining and interpreting values such as the number of successful/unsuccessful trades, the portfolio cash value after the commission to be paid to the intermediary institution, the profit factor and the sharpe ratio. The biggest disadvantage in this process is the selection of the appropriate stock, period, indicator and their parameters. Linear programming approaches are mostly used in the selection of these parameters that optimize the Backtest process optimally. However, according to the strategy to be used, the coding of these algorithms can have a linear, quadratic or polynomial complexity. This requires more long testing times for investors and algorithmic robot developers. Genetic algorithm-based approaches inspired by nature, on the other hand, converge to the optimal solution with much less iteration and require less processing power and time. In this study, a genetic programming-based approach is proposed for the selection of optimal conditions in algorithmic trading. In the experimental studies section, it has been seen that the use of traditional and genetic algorithm-based approaches in algorithmic trading operations has advantages when comparing complexity.

A heuristic approach for the integrated production–transportation problem with process flexibility

We study an integrated multi-item production and distribution problem considering a network of multiple plants and clients, who are geographically dispersed, with direct shipments from the plants to the clients. In addition to the decisions on production and distribution, a decision needs to be taken on the level of process flexibility in the network, i.e., which items can be produced in which plants. There is a clear trade-off between these decisions. On the one hand, a network with total flexibility where each plant can produce all types of items allows for lower transportation costs, but requires large investments in flexibility and frequent setups. On the other hand, a network with a limited amount of flexibility where each plant produces only a few items, will increase the transportation costs, but requires a lower investment in flexibility. We model this problem as an extension of the capacitated lot-sizing problem. We limit the investment in flexibility by a budget constraint and minimize the operational costs. Varying this budget allows us to analyze different levels of flexibility. In this paper, we propose mathematical models and a hybrid solution method that combines a mixed integer programming-based approach and a kernel search heuristic. Our computational results using data sets from the literature show that the proposed hybrid method produces on average better solutions with significantly lower computational times when compared with the results produced by a state-of-the-art optimization software. Additional computational results are presented by varying key parameters and analyzing their impact on the value of flexibility. These computational experiments indicate that some of the main managerial insights which were derived in the literature for the case without transportation costs are no longer valid when we consider transportation costs.

Ensuring secure interoperation of access control in a multidomain environment

Interoperation can combine multiple resources and domains, thus it has been widely used in many practical industrial applications, such as distributed database systems. However, the merger of local access control policies in such systems may lead to security violations with regard to access control. For instance, a person can potentially have access (indirectly) to another one's file or data in the interoperation to which s/he should be denied access in the individual system. Therefore, it is critical to deal with such issues in a multidomain environment. Nevertheless, a real-world interoperation contains a large number of entities and access. This imposes a challenge to find the maximum secure interoperation in terms of direct data sharing among individual systems. To overcome this difficulty, we propose an integer linear programming-based approach which can find the maximum secure interoperation in a computationally efficient way. Experimental results are given to demonstrate the efficacy of our approach.

A signomial programming-based approach for multi-echelon supply chain disruption risk assessment with robust dynamic Bayesian network

Disruption risk assessment is a primary and crucial step before taking measures to mitigate the negative impact of disruptions propagating along supply chains (SCs). Recently, robust dynamic Bayesian network (DBN) provides a valid tool for disruption risk estimation under the ripple effect in a data-scarce environment. However, existing literature has not considered such disruption risk assessment for multi-echelon SCs that are usually structurally complicated and thus vulnerable to disruptions with ripple effects. Motivated by this fact, we study the disruption risk assessment problem under the ripple effect for a multi-echelon SC with several suppliers and one manufacturer, in which only probability intervals of the suppliers’ states and those of the related disruption propagations are known. The aim is to acquire a robust risk estimation, measured by the worst-case total weighted probabilities for the manufacturer in the disrupted state over a time horizon. For the problem, a nonconvex nonlinear programming model is established to obtain the worst-case risk estimation. To efficiently solve the problem, a novel signomial programming (SP)-based approach is developed for finding near-optimal solutions. Numerical experiments on instances in the literature and our randomly generated instances are conducted to evaluate the efficiency of the proposed method. Besides, managerial insights are drawn.

Dynamic Positioning Capability Assessment Based on Optimal Thrust Allocation

Abstract The article presents an efficient method of optimal thrust allocation over the actuators in a dynamically positioned ship, according to the DNV-ST-0111 standard, Level 1. The optimisation task is approximated to a convex problem with linear constraints and mathematically formulated as quadratic programming. The case study is being used to illustrate the use of the proposed approach in assessing the DP capability of a rescue ship. The quadratic programming-based approach applied for dynamic positioning capability assessment allows for fast calculations to qualitatively compare different ship designs. In comparison with the DNV tool, it gives 100% successful validation for a ship with azimuth thrusters and a pessimistic solution for a ship equipped with propellers with rudders. Therefore, it can be safely applied at an early design stage.

Efficient approaches to optimize energy consumption in 3D wireless video sensor network under the coverage and connectivity constraints

Two advanced approaches (PA_1, and PA_2) based on a realistic 3D model of video sensor nodes (VSNs) deployed randomly over a 2D target area are proposed to minimize energy consumption in the network maintaining area coverage and connectivity. The reduction of the number of active VSNs decreases energy consumption but at the same time reduces the area coverage and connectivity. These conflicting issues are resolved and an optimal solution is obtained by using an integer linear programming-based approach (PA_1). But PA_1 is not tractable for large instances as the problem is NP-Hard. Hence, a heuristic approach (PA_2) based on an advanced genetic algorithm is also proposed in the present work for obtaining a near-optimal solution. Simulation studies are carried out to compare the performance of PA_1 and PA_2 with the other three state-of-the-art approaches (APP_5, APP_6, and ET_3). Among the three existing approaches, APP_6/(ET_3) is the best in energy consumption/(area coverage). It is observed that for the same simulation environment, both PA_1 and PA_2 guarantee higher network services, by reducing energy consumption by 40.85% and 33.34% respectively compared to the best existing approach APP_6; and as well as by increasing area coverage by 0.94% than the best existing approach ET_3 for the node density 150 on the target area of size 75x75 square meter. Between PA_1 and PA_2, PA_2 generates a suboptimal solution and PA_1 substantiates its superiority by reducing energy consumption by 11.26% than PA_2 without losing area coverage for the same simulation environment.

COALA: Concurrently Assigning Wire Segments to Layers for 2-D Global Routing

Two-dimensional (2-D) global routing followed by layer assignment is a common and popular strategy to obtain a good tradeoff between runtime and routing performance. Yet, the huge gap between 2-D routing patterns and the final 3-D routing paths often results in inevitable overflow after layer assignment. State-of-the-art (SOTA) studies on layer assignment usually adopt dynamic programming-based approaches to sequentially find an optimal solution for each net in terms of overflow or/and the number of vias. However, a fixed assignment ordering severely restricts the solution space, and the distributed overflows can hardly be resolved with any existing refinement approach. This article proposes a novel layer assignment framework that concurrently considers all the wire segments of nets and iteratively assigns them from the lowest available layer to the highest one. The concurrent scheme facilitates the maximal utilization of routing resource on each layer, contributing to an effective rerouting procedure that greatly reduces inevitable overflows. Based on the proposed framework, we further propose an obstacle-aware strategy that can mitigate obstacle-induced inevitable overflows in the original framework. Experimental results show that compared to an implemented sequential layer assignment approach based on SOTA techniques and refined by the well-known overflow/congestion reduction rip up and rerouting procedure, the proposed concurrent layer assignment framework (COALA) brings great improvements in the overflow reduction and runtime efficiency, which shows the significant advantage of the concurrent layer assignment scheme over sequential methods. The improvement is also verified in detailed routing, where the proposed COALA framework contributes to sparser routing results with fewer vias and design rule violations (DRVs).

Common Lyapunov Functions for Switched Linear Systems: Linear Programming-Based Approach

We study the stability of an equilibrium of arbitrarily switched, autonomous, continuous-time systems through the computation of a common Lyapunov function (CLF). The switching occurs between a finite number of individual subsystems, each of which is assumed to be linear. We present a linear programming (LP) based approach to compute a continuous and piecewise affine (CPA) CLF and compare this approach with different methods in the literature. In particular we compare it with the prevalent use of linear matrix inequalities (LMIs) and semidefinite optimization to parameterize a quadratic common Lyapunov function (QCLF) for the linear subsystems.

사용자 유사도 및 지역사회 운동 효과 기반 유사 요통 환자 추천에 관한 연구

In order to return to a healthy daily life of individuals who need rehabilitation exercise, it is necessary to provide continuous individualized rehabilitation exercise services after discharge at the hospital. However, in reality, it is difficult to provide services for the entire rehabilitation cycle of individuals because hospital and community rehabilitation data are not shared. In this paper, we consider the problem of providing a top-k similar patient list under the assumption that rehabilitation medical care and community exercise service are shared. The considered problem differs from the typical recommender system problems due to the lack of labels representing any two patients share the same or similar characteristics, which means that it is challenging to apply the existing recommender algorithms. We present a mathematical programming-based approach that enables us to identify the optimal feature set and similarity between patients. Then, a two-step algorithm was developed by utilizing the presented mathematical formulation, which results in a user-based collaborative filtering system. We report the experimental results for 12 experimental options using real-life clinical data of low back pain patients.

RIS-assisted communications in correlated Rayleigh channels: Comparison of training overhead reduction schemes

Channel training in reconfigurable intelligent surface (RIS)-assisted communications is usually conducted in an on-off manner, resulting in unaffordable training time overhead when the number of RIS elements is large. In this paper, for correlated Rayleigh channels, we compare three typical training overhead reduction schemes, namely RIS element selection (Scheme 1), element grouping (Scheme 2), and statistical CSI-based phase shifts design (Scheme 3). For Scheme 1 and Scheme 2, we propose two algorithms to select RIS elements (or form element groups) and determine the optimal number of activated elements (or formed groups), based on the channel correlation information only; for Scheme 3, we consider a semi-definite programming-based approach in the literature, and propose an alternative dominant eigenvector-based method for determining the RIS phase shifts vector. Via extensive simulations, we compare the achievable ergodic rates of these schemes versus the signal-to-noise ratio, the channel correlation level, and the element number-to-coherent time ratio, respectively, and discuss possible switching of the three schemes over these system parameters. At last, operation regions of the considered training overhead reduction schemes are shown in the plane characterized by the system parameters, which provides useful guidelines for practical scheme determination.

Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach

Diabetes mellitus is a global health problem, recognized as the seventh cause of death in the world. One of the most debilitating complications of diabetes mellitus is the diabetic foot (DF), resulting in an increased risk of hospitalization and significant morbidity and mortality. Amputation above or below the knee is a feared complication and the mortality in these patients is higher than for most forms of cancer. Identifying and interpreting relationships existing among the factors involved in DF diagnosis is still challenging. Although machine learning approaches have proven to achieve great accuracy in DF prediction, few advances have been performed in understanding how they make such predictions, resulting in mistrust of their use in real contexts. In this study, we present an approach based on Genetic Programming to build a simple global explainable classifier, named X-GPC, which, unlike existing tools such as LIME and SHAP, provides a global interpretation of the DFU diagnosis through a mathematical model. Also, an easy consultable 3d graph is provided, which could be used by the medical staff to figure out the patients’ situation and take decisions for patients’ healing. Experimental results obtained by using a real-world dataset have shown the ability of the proposal to diagnose DF with an accuracy of 100% outperforming other techniques of the state-of-the-art.

Toward Efficient Similarity Search under Edit Distance on Hybrid Architectures

Edit distance is the most widely used method to quantify similarity between two strings. We investigate the problem of similarity search under edit distance. Given a collection of sequences, the goal of similarity search under edit distance is to find sequences in the collection that are similar to a given query sequence where the similarity score is computed using edit distance. The canonical method of computing edit distance between two strings uses a dynamic programming-based approach that runs in quadratic time and space, which may not provide results in a reasonable amount of time for large sequences. It advocates for parallel algorithms to reduce the time taken by edit distance computation. To this end, we present scalable parallel algorithms to support efficient similarity search under edit distance. The efficiency and scalability of the proposed algorithms is demonstrated through an extensive set of experiments on real datasets. Moreover, to address the problem of uneven workload across different processing units, which is mainly caused due to the significant variance in the size of the sequences, different data distribution schemes are discussed and empirically analyzed. Experimental results have shown that the speedup achieved by the hybrid approach over inter-task and intra-task parallelism is 18 and 13, respectively.

Predictions of multiple food quality parameters using near-infrared spectroscopy with a novel multi-task genetic programming approach

In order to meet the increasing demand for food safety and quality, new methods for simultaneous and rapid determination of multiple food quality parameters (FQPs) are urgently needed in the food industry. Incorporating near-infrared (NIR) spectroscopy and spectral prediction model for rapid, repeatable, non-destructive, and low running costs quantitative analysis of FQPs is enjoying increasing popularity in the food industry. However, most existing spectrum-based prediction models are trained under a single-task learning framework, that is, a prediction model for each quality parameter and spectrum is constructed separately. This paradigm ignores possible connections among prediction tasks of different FPQs, which may result in the performance degradation of a single FPQ prediction model. This study proposes a novel multi-task genetic programming-based approach named EM4GPO for building multiple FQPs predictions simultaneously. In EM4GPO, the multi-dimensional trees are used to encode the raw NIR spectrum to shared features of multiple FQPs; for each FQP, a least square support vector regression (LS-SVR) modeling is performed on the shared features to obtain private features and prediction model; during the optimization process, a new algorithm is developed to optimize the previously obtained shared and private features, and LS-SVR prediction models through population evolution by combining the multidimensional multiclass genetic programming with multidimensional populations optimization method with nondominated sorting method. The proposed EM4GPO model is evaluated and compared with nine popular NIR prediction models using 10 NIR spectral datasets. The experimental results showed that EM4GPO outperformed other commonly used methods in all datasets which indicates that EM4GPO is competitive and effective in solving the problem of multiple FQPs predictions using the NIR spectrum.

Kesikli ve Sürekli Optimizasyon Kullanarak Yapay Sinir Ağları için Sistematik Girdi Seçimi Yöntemi

Selection of input variables of the empirical models has vital effect on the prediction performance, reduced overfitting and reduced computational load. Various trials and error and sequential methods in the literature to deal with input selection for artificial neural networks (ANNs). However, these methods are not considered as automatic and systematic. This study proposes a novel and efficient mixed integer nonlinear programming-based approach to handle optimal input selection and the ANN training simultaneously for classification problems. Such selection uses binary (0-1) variables to represent the presence of the input variables and trains traditional continuous network weights simultaneously. Two classification case studies are given to demonstrate the advantages by using widely used data sets and statistical measures. The first data set is related to the characterization of the type of a tumor related to breast cancer, the second data set is about predicting the type of a biotechnological product using different features, the last one is related to heart failure prediction. Results show that better test performance can be achieved with optimally selected inputs, resulting in reduced overfitting. The proposed approach delivers a significant advantage during the design and training of the ANNs and is also applicable to other empirical models.