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The exploration of unknown environments is an important task for an autonomous robot. When exploring an unknown environment, robots face a common trade-off between visiting already mapped areas or exploring new areas. This can be done by using a planning stage in conjunction with the SLAM algorithm. This is normally called integrated exploration. In this paper, we propose a novel integrated exploration...
Passive operating system fingerprinting reveals valuable information to the defenders of heterogeneous private networks; at the same time, attackers can use fingerprinting to reconnoiter networks, so defenders need obfuscation techniques to foil them. We present an effective approach for passive fingerprinting that uses data features from TLS as well as the TCP/IP and HTTP protocols in a multi-session...
Annotating human poses in realistic scenes is very time consuming, yet necessary for training human pose estimators. We propose to address this problem in an active learning framework, which alternates between requesting the most useful annotations among a large set of unlabelled images, and re-training the pose estimator. To this end, (1) we propose an uncertainty estimator specific for body joint...
The paper presents a variation of the systems' synthesis problem setting, where it is proposed to use the values of the entropy potentials of the output parameters as an optimization criterion. The use of such criteria makes it possible to improve the quality of the process dynamics assessment, what creates the prerequisites for the control efficiency improvement. With the reference to this specific...
The paper observes methods of qualimetry of dynamic processes based on the “entropy” approach. Also the paper presents variants of criteria for the processes quality assessment, adapted to different situations, as well as justifies the expediency of their application. The proposed models are based on the use of typical statistical characteristics of the analyzed parameters, are compact and convenient...
This paper, considering the complexity of university internal governance effect and particularity of the university itself, aims to build a indicator system to evaluate university internal governance effect, using the Grey Multilayer Comprehensive Assessment Method to evaluate this indicator system, whilst applying the Analytical Hierarchical Process (AHP) and Entropy Evaluation Method to define the...
The identification of online robot is important for managing the internet order, such as forbidding spam, fictitious information, and inductive words published by online robot to mislead the public. We studied the behaviors of the WeChat users to identify online robot, including turning pages, scanning the web, and forwarding posts. In fact, the amount and time interval of information forwarding were...
In this paper, we leverage two large-scale real-world datasets to provide the first results on the limits of predictability of cellular data traffic demands generated by individual users over time and space. Using information theory tools, we measure the maximum predictability that any algorithm has potential to achieve. We first focus on the predictability of mobile traffic consumption patterns in...
In heterogeneous wireless networks, an important task for mobile terminals is network selection. A main challenge of network selection is to represent the uncertainty. Concerning this challenge, we investigate intuitionistic fuzzy multi-attribute decision making (IFMADM) and discuss its application in network selection. Objectively determining attribute weights and aggregating intuitionistic fuzzy...
Ocean current is one of the important components in the Ocean state monitoring, which can be measured by ocean acoustic technology. This paper studies the inference of current filed. Based on least squared (LS) method, we can estimate the current filed with travel time. However, this method is suffered from perturbation from noise, and also requires prior information which is hard to obtain. In this...
We consider a setting where a system has to interact, and hence create distinct outputs (observables), but subject to such operational constraints wants to minimize the leakage that such observables reveal about its secret input. It has been previously demonstrated that under some (highly symmetrical) constraints on the observables, it is possible to design systems that are universally optimal in...
Several works have shown that relationships between data points (i.e., context) in structured data can be exploited to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these inter-relationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling effort. Towards this goal, we propose an active...
Fuzzy rough technique is a mathematical tool to deal with fuzzy and rough knowledge, which could reduce the redundant objects and attributes by keeping the information invariant. In the existing researches on fuzzy rough sets, all the attributes are assumed to have the same weights for the decision. Actually different attributes may play different roles on the decision. As a result, we introduce weights...
Feature selection is to select certain quantity of important features from large number of original features. In this paper, a new feature selection algorithm based on features unit (FU) is presented. The algorithm uses entropy of information to determine whether a feature should integrate with other features on the basis of its relevance to the class. As for the features which fit to integrate with...
In the eutrophication evaluation, the uncertainty of evaluation level is the main problem. This process also refers to many factors which have different effect on result. Multi-dimensional normal cloud is a system model which can describe the uncertainty and fuzzy feature. This model involves the factor weight. In our paper, the weight is determined by AHP, CRITIC and information entropy which are...
Situation information and sensor information are differentiated and a method for computing the situation information expected value (SIEV) is presented for use in Information Based Sensor Management (IBSM). Nine case pairs are evaluated in which the sensor capabilities vary among poor, average, and good sensors, and the goal lattice values vary among attack, defend, and stealth modes showing that...
Uncertainty measures in evidence theory can supply a new criterion to rate the quality of information carried by belie structures. It can also be used to measure the quantity of knowledge conveyed by belief structures. Following the work of Klir and Yuan, several uncertainty measures for belief structures have been developed. Among them, aggregate uncertainty AU, the total uncertainty TU and the ambiguity...
How to quantify the uncertainty information consisted in the body of evidence (BOE) in the framework of Dempster-Shafer evidence theory is still an open issue. A few uncertainty measures have been proposed in Dempster-Shafer evidence theory framework, but these studies mainly focused on the mass function itself and the scale of the frame of discernment (FOD) is totally ignored. Since the existing...
Unmanned Aircraft Systems, UAS, have seen unprecedented levels of growth during the last decade. Projections and expectations for future UAS utilization span a very wide and diverse spectrum of civilian and public domain applications, in addition to the obvious military applications, from emergency response, to environmental monitoring, early fire detection and forest protection, to name but a few...
In this paper, a new entropy based uncertainty measure is introduced for evaluating the significance of subsets of attributes in incomplete decision tables. Some properties of rough conditional entropy are derived. And three attribute reduction algorithms are provided, including an algorithm using exhaustive search, an algorithm using heuristic search and an algorithm using probabilistic search for...
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