Constraints Network Research Articles

Dual-consistency constraints network for noisy facial expression recognition

Although existing facial expression recognition (FER) methods have achieved great success, their performance degrades significantly under noisy labels caused by low-quality images, ambiguous expressions, and subjective and incorrect labeling. Recent studies have shown that deep neural networks (DNNs) can easily overfit noisy labels, which poses a great challenge to FER task in real-world scenarios. To address this issue, we propose a novel Dual-consistency Constraints Network (DC-Net) to automatically suppress noisy samples during training. Specifically, we first propose a Class Activation Mapping (CAM) Attention Consistency (CAC), which makes the model focus on partially important feature information. As a result, we obtain more robust local feature representations and reduce excessive attention to noisy labels. Then, a Class Feature Consistency (CFC) is designed to encourage the model to focus on the global semantic information of the image. Finally, with the collaboration of the CAC and the CFC, DC-Net can learn robust local and global feature information to prevent the model from learning biased information with noisy labels. We conducted extensive experiments on three field datasets, including RAF-DB, AffectNet, and FERPlus2013. Experimental results show that DC-Net significantly outperforms state-of-the-art noisy labeling methods at different noise rates and generalizes well to other tasks with a large number of classes, such as CIFAR100 and Tiny- ImageNet.

Predictive trajectory tracking control for the USV in networked environments with communication constraints

The trajectory tracking control problem of under-actuated unmanned surface vessels (USVs) suffering from communication constraints (network delay, packet loss, packet disorder) as well as external environment disturbance is investigated in this paper. To solve the aforementioned trajectory control problem, a nonlinear networked predictive control strategy based on the discrete sliding mode framework is proposed. Initially, using the forward Euler discretization method, a novel discrete virtual velocity control law is designed to transform trajectory tracking control into virtual velocity tracking control. Then, a sliding mode controller is suggested to suppress external disturbance of the USV. In addition, a networked sliding mode predictive control (NSMPC) scheme is proffered to guarantee active compensation of the communication constraints and stability of the trajectory tracking system simultaneously. Afterward, a theoretical analysis is given to show that the closed-loop system is uniformly bounded stable after the introduction of the digital network. Finally, extensive comparative simulation examples are provided to demonstrate the effectiveness of the proposed control strategy.

An enhanced spatio-temporal constraints network for anomaly detection in multivariate time series

Anomaly detection using multivariate time series plays a crucial role in system security. Conventional deep learning detection techniques mainly depend on temporal dependency and employ reconstruction or prediction-based methods. However, as feature variables grow more intricate, there is a risk of neglecting essential spatio-temporal structural information, potentially leading to insufficient model training in unsupervised settings. Hence, we propose an end-to-end anomaly detection model with multiple pre-training tasks designed for the spatio-temporal dimension to enhance our constraints. Specifically, in the temporal dimension, we employ an autoregressive task to train timestamp associations using data’s concealed autocorrelation and periodicity. In the spatio dimension, we acquire knowledge of a diverse feature-related heterogeneous graph. Subsequently, we design three different graph contrastive learning tasks to tap into the effective information arising from the inherent heterogeneity and hierarchy in spatio structures. Through joint spatio-temporal modeling, we can effectively capture inter and intra-feature associations from series and graph structural features, enhancing model robustness to cope with the complex chain reactions between features. Finally, we assess our model on three real-world datasets: SWaT, WADI(2017, 2019), our F1 scores demonstrate enhancements of 6.17%, 18.3% and 5.35% over the top-tier baseline performance. Our model is applicable for both temporal and graph, is self-supervised learning for sparse data which is suitable for data sparsity and complex scenarios that need to capture spatio-temporal characteristics at the same time, for example, traffic flow detection and anomaly detection of intelligent systems. Further visualization experiments and case studies will provide a better interpretation of our model.

Implementasi Peraturan Menteri Dalam Negeri Nomor 70 Tahun 2019 Tentang Sistem Informasi Pemerintahan Daerah pada Penerapan E-Planning dalam Perencanaan Pembangunan di Kabupaten Pakpak Bharat

This study aims to determine and analyze the implementation of the Minister of Home Affairs Regulation Number 70 of 2019 concerning the Regional Government Information System (SIPD) on the implementation of e-planning in development planning in the Pakpak Bharat Regency. The research approach is descriptive qualitative which data collection is done by interview, observation, and documentation study. The study conducted an analysis using Edward III's theory by analyzing the factors of communication, resources, disposition, and organizational structure. The results showed that the implementation of the Minister of Home Affairs Regulation Number 70 of 2019 concerning Regional Government Information Systems (SIPD) on the implementation of e-planning in development planning in the Pakpak Bharat Regency went quite smoothly. The attitude and response of the implementers in carrying out the mandate of the SIPD policy are good and quite committed, as evidenced by their efforts and efforts to meet the deadlines of the planning schedule. However, there are still some obstacles, such as a lack of understanding of the classification, codification,n and nomenclature of development planning and regional finance, infrastructure constraints (network or server), the readiness of unit price standards (SSH), and the absence of SOPs. Improvement of the quality of human resources for planners must be carried out so that they can keep up with changes in dynamic information systems and the resulting planning documents are of higher quality.

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things.

In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions.

Load aware energy‐balanced data gathering approach in CRSNs

Cognitive radio sensor network (CRSN) is a combination of two different prominent technologies named as wireless sensor network (WSN) and CR network (CRN). There are different related issues of two different technologies such as energy and hardware for WSN and dynamic spectrum for CRN. CRSN constitutes the research challenges in designing efficient communication protocol in resource constraints network. So, energy-balanced transmission is necessary to prolong the lifetime of WSNs. It ensures uniform average energy consumption among all the nodes. Data gathering approach in traditional sensor network suffers uncontrolled interference in the unlicenced spectrums as these spectrums are gradually getting crowded. CRSN network can provide a solution to spectrum scarcity in unlicenced channels. This study proposes a load aware energy balancing approach to maximise the network lifetime. It also proposes a dynamic channel sensing and accessing algorithm to optimise the energy consumption when the quality of unlicenced spectrum deteriorates. Once the event gets detected, the proposed routing protocol's objective is to transmit the data through energy-efficient path from source to destination. Its performance is evaluated through extensive simulations. Simulation results demonstrate a significant improvement over closely related works.

Locating rental stations and bikeways in a public bike system

ABSTRACTTo determine the spatial distribution of rental stations and bikeways in a public bike system, this paper proposes a facility location and network design model. The model is developed as a multi-objective programing problem that considers four objectives (minimizing cyclist risk, maximizing cyclist comfort, minimizing adverse impacts on traffic and maximizing service coverage) and multiple constraints (monetary budget, network connectivity, station spacing, bikeway types, station number and value ranges of decision variables). The ε-constraint method solves the programing problem for the public bike system in Daan District, Taipei City, Taiwan. The nine non-dominated alternatives generated are all markedly better than existing locations of rental stations and bikeways. Scenario analysis results indicate that increasing the construction budget for bikeways significantly improves cyclist safety and comfort whilst increasing the adverse impact on traffic. Planners can use this model to develop public bike systems that spatially integrate rental stations and bikeway networks.

COMPARATIVE ANALYSIS OF INTERNET OF THINGS LIGHTWEIGHT AUTHENTICATION PROTOCOL DESIGN SCHEMES

— Lightweight authentication is a branch of the modern authentication techniques, which intended to be used in constraints network such IoT and WSN which has devices with low or extremely low resources. There are several ways for designing a lightweight authentication protocol, researchers are competing to find the best ways that fit with the features of the Internet of Things. In this paper, we propose generalized approaches to design lightweight authentication protocols. Also, we highlight some principals and security requirements for the implementation of lightweight authentication. Finally, we provide a comparative study of using symmetric and asymmetric cryptography techniques for designing lightweight authentication protocols for IoT.

Cost‐based placement of vDPI functions in NFV infrastructures

SummaryNetwork functions virtualization (NFV) is transforming how networks are operated and how network services are delivered. The network is more flexible and adaptable, and it can scale with traffic demands. To manage video traffic in the network, or acquire protection from cyber‐attacks, Deep Packet Inspection (DPI) is increasingly deployed at specific locations in the network. The virtual DPI (vDPI) engines can be dynamically deployed as software on commodity servers within emerging network functions virtualization infrastructures. For a network operator, deploying a set of vDPIs over the network is a matter of finding the appropriate placement that meets the traffic management or cyber‐security targets (such as the number of inspected flows) and operational cost constraints (licence fees, network efficiency or power consumption). In this work, we formulate the vDPI placement problem as a cost minimization problem. The cost captures the different objectives that the operator is pursuing. A placement of vDPIs on the network nodes realizes a trade‐off between these possibly conflicting goals. We cast the problem as a multi‐commodity flow problem and solve it as an integer linear program. We then devise a centrality‐based greedy algorithm and assess its validity by comparing it with the integer linear program optimal solution on a real dataset (GEANT network with 22 nodes and real traffic matrix). We further analyse the scalability of the heuristic by applying it to larger random networks of up to 300 nodes. The results show the network structure and the costs influence time performance. Finally, they demonstrate that the heuristic approximates the optimal on small and medium problem instances well. Copyright © 2015 John Wiley & Sons, Ltd.

Elaboration of posteriori probability estimates for local inference in algebraic Bayesian networks in case of imprecise evidence

For an algebraic Bayesian network, there are several degrees of consistency. In the case of a scalar representation of the probability the global consistency of the algorithms posteriori global output is proven. In the case of interval probability estimates the problem of obtaining consistent results is complicated by the need to use approximate methods to obtain estimates of the a posteriori probability. Results of the algorithms of local inference on the posterior interval estimates of the probabilities for all types of incoming evidence are analyzed. Additional restrictions in the case of imprecise evidence are proposed. The consistency of the resulting from global posteriori algorithm using these constraints network is proven.

Multi-Objective Game Theoretic Schedulingof Bag-of-Tasks Workflows on Hybrid Clouds

Scheduling multiple large-scale parallel workflow applications on heterogeneous computing systems like hybrid clouds is a fundamental NP-complete problem that is critical to meeting various types of QoS (Quality of Service) requirements. This paper addresses the scheduling problem of large-scale applications inspired from real-world, characterized by a huge number of homogeneous and concurrent bags-of-tasks that are the main sources of bottlenecks but open great potential for optimization. The scheduling problem is formulated as a new sequential cooperative game and propose a communication and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, makespan, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.

Group planning with time constraints

Embedding planning systems in real-world domains has led to the necessity of Distributed Continual Planning (DCP) systems where planning activities are distributed across multiple agents and plan generation may occur concurrently with plan execution. A key challenge in DCP systems is how to coordinate activities for a group of planning agents. This problem is compounded when these agents are situated in a real-world dynamic domain where the agents often encounter differing, incomplete, and possibly inconsistent views of their environment. To date, DCP systems have only focused on cases where agents’ behavior is designed to optimize a global plan. In contrast, this paper presents a temporal reasoning mechanism for self-interested planning agents. To do so, we model agents’ behavior based on the Belief-Desire-Intention (BDI) theoretical model of cooperation, while modeling dynamic joint plans with group time constraints through creating hierarchical abstraction plans integrated with temporal constraints network. The contribution of this paper is threefold: (i) the BDI model specifies a behavior for self interested agents working in a group, permitting an individual agent to schedule its activities in an autonomous fashion, while taking into consideration temporal constraints of its group members; (ii) abstract plans allow the group to plan a joint action without explicitly describing all possible states in advance, making it possible to reduce the number of states which need to be considered in a BDI-based approach; and (iii) a temporal constraints network enables each agent to reason by itself about the best time for scheduling activities, making it possible to reduce coordination messages among a group. The mechanism ensures temporal consistency of a cooperative plan, enables the interleaving of planning and execution at both individual and group levels. We report on how the mechanism was implemented within a commercial training and simulation application, and present empirical evidence of its effectiveness in real-life scenarios and in reducing communication to coordinate group members’ activities.

The Analysis Technology of Parameters Change Impact Based on the Product Interface Coupling Network Model

In the current stage of product development process, it is lack of the technology of parameters association modeling, in order to achieve the product parameter configuration modeling, building the interface association constraints network model based on the process of top-down product decomposition,. The technology proposed in this paper studied reasonable constraints organization and expression method, it includes parameter extraction, data structure based on “Interface- Characteristic parameters- Coupling constraints and product coupling modeling analysis. The technology provides support for the detailed design of product.

Robust decision and optimisation for collaborative parameter design

The overall performance of a complex product generally depends on a number of specifications distributed in multi-teams from different disciplines, such as mechanics, cybernetics, dynamics and so on. The paper presents a robust decision and optimisation approach based on multidisciplinary modelling for complex product development. In this paper, the design knowledge is presented as constraints network from multidisciplinary domain. Then multidisciplinary robust decision and optimisation method is presented to solve the model. The method described in this paper was used to develop knowledge driven multidisciplinary collaborative design system, which is applied to railway vehicle development processing.

Collaborative robust optimization under uncertainty based on generalized dynamic constraints network

Uncertainties exist in every aspect of a collaborative multidisciplinary design process. These uncertainties will have a great influence on design negotiations between various disciplines and may force designers to make conservative decisions. In this paper, a novel collaborative robust optimization (CRO) method based on constraints network under uncertainty is presented. The generalized dynamic constraints network (GDCN) is developed to analysis and management of uncertainties, and to ensure the parameter consistency in the collaborative design process. Given the feasible consistent parameter region, The CRO is formulated as a multi-criteria optimization problem, which brings both the objective robustness and the feasibility robustness of the constraint into account simultaneously. The CRO based on GDCN could bring both the design parameters dynamic consistency management and robust optimization into account simultaneously, which assures a product’s reliability and quality robustness. The efficiency of proposed method is evaluated in the design of crank and connecting rod in one V6 engine.

Handling fuzzy temporal constraints in a planning environment

An interleaved integration of the planning and scheduling process is presented with the idea of including soft temporal constraints in a partial order planner that is being used as the core module of an intelligent decision support system for the design forest fire fighting plans. These soft temporal constraints have been defined through fuzzy sets. This representation allows us a flexible representation and handling of temporal information. The scheduler model consists of a fuzzy temporal constraints network whose main goal is the consistency checking of the network associated to each partial order plan. Moreover, we present a model of estimating this consistency, and show the monitoring and rescheduling capabilities of the system. The resulting approach is able to tackle problems with ill defined knowledge, to obtain plans that are approximately consistent and to adapt the execution of plans to unexpected delays.

Parameter coordination and optimization for collaborative design based on the constraints network

This paper presents an approach based on the constraints network to support collaborative design. Firstly, from the point of view of collaborative design, the model of the constraints network for coordination and optimization is studied. Secondly, a general consistency algorithm is designed to predict conflict and refine the intervals for parameter coordination, which verify the design stage early in the process and assist the designers in determining design variables to reduce the multidisciplinary iterations in collaborative design. Finally, a constraints-network-based parameter optimization model is established, and the multiobjective optimization approach is presented. The method is demonstrated by a bogie parameter design problem.

A variational geometric constraints network for a tolerance types specification

A geometric tolerance specification includes specifying the tolerance types and values for nominal geometry parts. In current computer-aided tolerancing practices, designers have to specify manually both tolerance types and values in a CAD system, usually starting out from single components. The paper presents a framework and computer implementation that provides automated methods for the specification of geometric tolerance types. Based on the ISO/TC 213 geometric product specification, a new concept of a variational geometric constraints network is presented. The generation and application of the network to specify geometric tolerance types is studied. An example is analysed to show the scheme to be effective, and the model for a tolerance specification, including the types and values specification, is presented.

Protection performance components in MPLS networks

In this paper, we present a new methodology for evaluating fault recovery performance of some existing mechanisms, which considers the establishment of quality of service network paths with protection. In order to evaluate the level of protection of a network, different components, such as protection parameters (packet loss and restoration time), or network parameters and constraints (link failure probability and network load), are analyzed. A formulation to calculate the influence of each component in the establishment of protected paths is discussed in multiprotocol label switching (MPLS) networks. Several experiments are presented to support this formulation. Moreover, an analysis of the relationship between these protection components and different traffic classes is also introduced and justified.

Generation and application of variational geometric constraints network based on ISO’s feature

The concept of variantional geometric constraints network is presented. Based on I SO's feature, three kinds ofvariational geometric constraints are defined. The concepts of mate tree (MT) and loop circuit (LC) are presented. Thegeneration method of well-constrained variational geometric constraints network (VGCN) is studied. The network can beapplied in generation of well-constrained tolerance types and tolerance chains. A simple example is analyzed to show thescheme to be effective.