Embodiments of the present disclosure relate to methods, systems, and computer program products for observation data evaluation. In a method, a hierarchical relationship between a plurality of observation items is obtained based on a dataset including a plurality of observation samples. Here, an observation sample in the plurality of observation samples includes a group of measurements for the group of observation items, respectively. A plurality of evaluation models for evaluating an observation sample is generated based on the hierarchical relationship according to a predefined group of membership functions and a predefined group of fuzzy operators. An evaluation model is selected for a further evaluation from the plurality of evaluation models based on a plurality of confidence intervals for the plurality of evaluation models. With these embodiments, the evaluation model may be obtained in an easy and more effective way.

Provided is a method and system for predicting a height of a confined water rising zone. The method includes: obtaining sample data; dividing the sample data into a training sample and a test sample; calculating a degree of correlation between a height and a correlation factor value sequence; screening correlation factors according to the degree of correlation to obtain screened correlation factors; calculating weights of the screened correlation factors using an entropy weight method (EWM); obtaining standardized screened correlation factor value sequences according to correlation factor value sequences corresponding to the screened correlation factors; calculating a value of each indicator according to the standardized screened correlation factor value sequences and the weights; and obtaining a height prediction model of a confined water rising zone based on principal component analysis (PCA)-particle swarm optimization (PSO)-support vector regression (SVR), the value of each indicator, and the test sample.

Provided is a method and system for predicting a height of a confined water rising zone. The method includes: obtaining sample data; dividing the sample data into a training sample and a test sample; calculating a degree of correlation between a height and a correlation factor value sequence; screening correlation factors according to the degree of correlation to obtain screened correlation factors; calculating weights of the screened correlation factors using an entropy weight method (EWM); obtaining standardized screened correlation factor value sequences according to correlation factor value sequences corresponding to the screened correlation factors; calculating a value of each indicator according to the standardized screened correlation factor value sequences and the weights; and obtaining a height prediction model of a confined water rising zone based on principal component analysis (PCA)-particle swarm optimization (PSO)-support vector regression (SVR), the value of each indicator, and the test sample.

Described is a system for object detection that is robust to adversarial attacks. An initial hypothesis of an identity of an object in an input image is generated using a sparse convolutional neural network (CNN) and a distribution aware classifier. A foveated hypothesis verification process is performed for identifying a region of the input image that supports the initial hypothesis. Using a part-based classifier, an identity of a part of the object in the region of the input image is predicted. An attack probability for the predicted identity of the part, and the initial hypothesis is updated based on the predicted identity of the part and the attack probability. The foveated hypothesis verification process and updating of hypotheses is performed until a hypothesis reaches a certainty threshold. The object is labeled based on the hypothesis that reached the certainty threshold.

Provided is a method and system for predicting a height of a confined water rising zone. The method includes: obtaining sample data; dividing the sample data into a training sample and a test sample; calculating a degree of correlation between a height and a correlation factor value sequence; screening correlation factors according to the degree of correlation to obtain screened correlation factors; calculating weights of the screened correlation factors using an entropy weight method (EWM); obtaining standardized screened correlation factor value sequences according to correlation factor value sequences corresponding to the screened correlation factors; calculating a value of each indicator according to the standardized screened correlation factor value sequences and the weights; and obtaining a height prediction model of a confined water rising zone based on principal component analysis (PCA)-particle swarm optimization (PSO)-support vector regression (SVR), the value of each indicator, and the test sample.

Provided is a method and system for predicting a height of a confined water rising zone. The method includes: obtaining sample data; dividing the sample data into a training sample and a test sample; calculating a degree of correlation between a height and a correlation factor value sequence; screening correlation factors according to the degree of correlation to obtain screened correlation factors; calculating weights of the screened correlation factors using an entropy weight method (EWM); obtaining standardized screened correlation factor value sequences according to correlation factor value sequences corresponding to the screened correlation factors; calculating a value of each indicator according to the standardized screened correlation factor value sequences and the weights; and obtaining a height prediction model of a confined water rising zone based on principal component analysis (PCA)-particle swarm optimization (PSO)-support vector regression (SVR), the value of each indicator, and the test sample.

Methods, apparatus, systems, and articles of manufacture for networked device testing are disclosed. An example method includes determining an input to be applied to a programmable network device, mutating the input to determine an input variant, applying the input variant to the programmable network device, and in response to determining that the input variant causes the programmable network device to enter an expected state, add the input variant to a test set.

Methods, apparatus, systems, and articles of manufacture for networked device testing are disclosed. An example method includes determining an input to be applied to a programmable network device, mutating the input to determine an input variant, applying the input variant to the programmable network device, and in response to determining that the input variant causes the programmable network device to enter an expected state, add the input variant to a test set.

A method for optimizing push-based data broadcasting in Vehicle Named Data Networks (VNDNs) is disclosed. The method introduces a novel approach to cluster vehicles and select Cluster Heads (CHs) using fuzzy logic. Each CH is responsible for broadcasting data packets within its cluster. The process involves determining the location of each vehicle, clustering them into groups, disseminating cluster information, and employing fuzzy logic to select CHs.

This method significantly mitigates data storm issues in VNDNs, ensuring smoother data transmission between vehicles, particularly during emergencies, enhancing both efficiency and reliability in data dissemination within VNDN environments.

A method for optimizing push-based data broadcasting in Vehicle Named Data Networks (VNDNs) is disclosed. The method introduces a novel approach to cluster vehicles and select Cluster Heads (CHs) using fuzzy logic. Each CH is responsible for broadcasting data packets within its cluster. The process involves determining the location of each vehicle, clustering them into groups, disseminating cluster information, and employing fuzzy logic to select CHs.

This method significantly mitigates data storm issues in VNDNs, ensuring smoother data transmission between vehicles, particularly during emergencies, enhancing both efficiency and reliability in data dissemination within VNDN environments.