Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

A Multiroute Signal Control Model considering Coordination Rate of Green Bandwidth

Oriented to characteristics of the inflow and outflow of routes in urban road network, we modified the classical fundamental green wave bandwidth model, in which separate turning green wave band is available for traffic flow from subarterials merging into an arterial, and this variable green wave band can be more flexible to service the commuting traffic. Moreover, with the analysis of the mapping characteristics of the phase coordination rate, the concept of the coordination rate of green wave bandwidth was proposed, with which as the objective function, a multiroute signal coordination control model was established, and this model is a mixed integer linear programming problem with the overall optimal coordination rate of inbound, outbound, and turning movement as the objective. Finally, a case study was given with road network in Suzhou Industrial Park, Jiangsu Province, China. From the simulation results, we can conclude that the coordinated distribution of the model proposed in this study is more stable; the fluctuation range is 0.09, which is less than that of optimization scheme in classical signal timing software Synchro, which is 0.33; and the total route delay can also be reduced by 15% compared to the current situation and 3.3% compared to Synchro optimization solution.

Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network

Distributed link-state routing protocols, including Open Shortest Path First (OSPF) and Intermediate System–Intermediate System (IS-IS), have successfully provided robust shortest path routing for IP networks. However, shortest path routing is inflexible and sometimes results in congestion on some critical links. By separating the control plane and the data plane, the centralized control of Software Defined Networking (SDN)-based approach possesses flexible routing capabilities. Fibbing is an approach that can achieve centralized control over a network running distributed routing protocols. In a Fibbing-controlled IP network, the controller cleverly generates fake protocol messages to manipulate routers to steer the flow of the desired paths. However, introducing fake nodes destroys the structure of the loop-free property of Loop-Free Alternate (LFA) that is used to achieve fast failure recovery in IP networks. This paper addresses this issue and presents a solution to provision routing paths so a Fibbing network can still apply LFA in the network. The proposed network jointly considers load-balanced and fast failure recovery. We formulate the problem as an integer linear programming problem. The numerical results reveal that the proposed method can provide 100% survivability against any single node or single link failure.

Balance Model of COVID-19 Epidemic Based on Percentage Growth Rate

The paper examines the possibility of using an alternative approach to predicting statistical indicators of a new COVID-19 virus type epidemic. A systematic review of models for predicting epidemics of new infections in foreign and Russian literature is presented. The accuracy of the SIR model for the spring 2020 wave of COVID-19 epidemic forecast in Russia is analyzed. As an alternative to modeling the epidemic spread using the SIR model, a new CIR discrete stochastic model is proposed based on the balance of the epidemic indicators at the current and past time points. The new model describes the dynamics of the total number of cases (C), the total number of recoveries and deaths (R), and the number of active cases (I). The system parameters are the percentage increase in the C(t) value and the characteristic of the dynamic balance of the epidemiological process, first introduced in this paper. The principle of the dynamic balance of epidemiological process assumes that any process has the property of similarity between the value of the total number of cases in the past and the value of the total number of recoveries and deaths at present. To calculate the values of the dynamic balance characteristic, an integer linear programming problem is used. In general, the dynamic characteristic of the epidemiological process is not constant. An epidemiological process the dynamic characteristic of which is not constant is called non-stationary. To construct mid-term forecasts of indicators of the epidemiological process at intervals of stationarity of the epidemiological process, a special algorithm has been developed. The question of using this algorithm on the intervals of stationarity and non-stationarity is being examined. Examples of the CIR model application for making forecasts of the considered indicators for the epidemic in Russia in May-June 2020 are given.

Spectrum Allocation and Device Association in Federated Learning-Enabled Industrial IoT via Hypergraph Matching

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

Spectrum Allocation and Device Association in Federated Learning-Enabled Industrial IoT via Hypergraph Matching

In this paper, a joint spectrum allocation and device association problem is investigated for a federated learning aided hierarchical Industrial Internet of Things (IIoT) system for smart factory. To achieve the optimization jointly, we design a weighted learning utility maximization problem, which is a 0-1 integer linear programming problem. To solve this problem, we convert it into a weighted 3D hypergraph model by capturing the 3D mapping relation for IIoT device, subchannel, and edge server. A local search algorithm is then presented to find a 3D hypergraph matching with maximum total weights as the suboptimal solution. Simulation results demonstrate the superior performance of the proposed algorithm compared with the greedy algorithm in the system learning utility.

Infinity Substitute in Finding Exact Minimum of Total Weighted Tardiness in Tight-Tardy Progressive 1-machine Scheduling by Idling-free Preemptions

A job schedule ensuring the exact minimum of total weighted tardiness can be found with the respective integer linear programming problem model, in which the infinity showing that the respective states are either forbidden or impossible is substituted with a sufficiently great positive integer. An open question is whether the substitute can be selected so that the computation time would be decreased. Thus, it is ascertained that, whichever job lengths and its priority weights are, substituting the infinity with just “a sufficiently great positive integer” is never recommended. To decrease the computation time on average, it is strongly recommended to select the infinity substitute as multiple maximum over finite decision variable weights in the exact model. It is sufficient to take 2 to 5 such maxima as the infinity substitute. However, the shortened computation time is not guaranteed for solving a single or few scheduling problems. It is only an expected benefit, which builds up as a few hundred scheduling problems are solved at least.