Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system (ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.
Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.
Double-authentication-preventing signature (DAPS) is a novel signature notion proposed at ESORICS 2014. The double-authentication-preventing property means that any pair of signatures on two different messages with the same subject will result in an immediate collapse of the signature system. A few potential applications of DAPS have been discussed by its inventors, such as providing a kind of self-enforcement to discourage certificate authority (CA) from misbehaving in public key infrastructure and offering CA some cryptographic arguments to resist legal coercion. In this study, we focus on some fundamental issues on DAPS. We propose a new definition, which is slightly weakened but still reasonable and strong enough to capture the DAPS concept. We develop the new notion of invertible chameleon hash functions with key exposure. Then we propose a generic DAPS scheme, which is provably secure if the underlying invertible chameleon hash function with key exposure is secure. We instantiate this general construction to obtain the DAPS schemes respectively based on the well-known assumptions of integer factorization, Rivest-Shamir-Adleman (RSA), and computational Diffie-Hellman (CDH). They are more efficient than previous DAPS schemes. Furthermore, unlike previous constructions, the trusted setup condition is not needed by our DAPS schemes based on RSA and CDH.
Academic literature retrieval concerns about the selection of papers that are most likely to match a user’s information needs. Most of the retrieval systems are limited to list-output models, in which the retrieval results are isolated from each other. In this paper, we aim to uncover the relationships between the retrieval results and propose a method to build structural retrieval results for academic literature, which we call a paper evolution graph (PEG). The PEG describes the evolution of diverse aspects of input queries through several evolution chains of papers. By using the author, citation, and content information, PEGs can uncover various underlying relationships among the papers and present the evolution of articles from multiple viewpoints. Our system supports three types of input queries: keyword query, single-paper query, and two-paper query. The construction of a PEG consists mainly of three steps. First, the papers are soft-clustered into communities via metagraph factorization, during which the topic distribution of each paper is obtained. Second, topically cohesive evolution chains are extracted from the communities that are relevant to the query. Each chain focuses on one aspect of the query. Finally, the extracted chains are combined to generate a PEG, which fully covers all the topics of the query. Experimental results on a real-world dataset demonstrate that the proposed method can construct meaningful PEGs.
Today, digital cameras are widely used in taking photos. However, some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study, we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature, which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.
With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness, a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called “RepoLike,” to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.
Although the perimeter security model works well enough when all internal hosts are credible, it is becoming increasingly difficult to enforce as companies adopt mobile and cloud technologies, i.e., the rise of bring your own device (BYOD). It is observed that advanced targeted cyber-attacks usually follow a cyber kill chain; for instance, advanced targeted attacks often rely on network scanning techniques to gather information about potential targets. In response to this attack method, we propose a novel approach, i.e., an “isolating and dynamic” cyber defense, which cuts these potential chains to reduce the cumulative availability of the gathered information. First, we build a zero-trust network environment through network isolation, and then multiple network properties are maneuvered so that the host characteristics and locations needed to identify vulnerabilities cannot be located. Second, we propose a software-defined proactive cyber defense solution (SPD) for enterprise networks and design a general framework to strategically maneuver the IP address, network port, domain name, and path, while limiting the performance impact on the benign network user. Third, we implement our SPD proof-of-concept system over a software-defined network controller (OpenDaylight). Finally, we build an experimental platform to verify the system’s ability to prevent scanning, eavesdropping, and denial-of-service attacks. The results suggest that our system can significantly reduce the availability of network reconnaissance scan information, block network eavesdropping, and sharply increase the cost of cyber-attacks.
Classification of intertidal area in synthetic aperture radar (SAR) images is an important yet challenging issue when considering the complicatedly and dramatically changing features of tidal fluctuation. The difficulty of intertidal area classification is compounded because a high proportion of this area is frequently flooded by water, making statistical modeling methods with spatial contextual information often ineffective. Because polarimetric entropy and anisotropy play significant roles in characterizing intertidal areas, in this paper we propose a novel unsupervised contextual classification algorithm. The key point of the method is to combine the generalized extreme value (GEV) statistical model of the polarization features and the Markov random field (MRF) for contextual smoothing. A goodness-of-fit test is added to determine the significance of the components of the statistical model. The final classification results are obtained by effectively combining the results of polarimetric entropy and anisotropy. Experimental results of the polarimetric data obtained by the Chinese Gaofen-3 SAR satellite demonstrate the feasibility and superiority of the proposed classification algorithm.
Educational innovation is a field that has been greatly enriched by using technology in its processes, resulting in a learning model where information comes from numerous sources and collaboration takes place among multiple students. One attractive challenge within educational innovation is the design of collaborative learning activities from the social computing point of view, where collaboration is not limited to student-to-student relationships, but includes student-to-machine interactions. At the same time, there is a great lack of tools that give support to the whole learning process and are not restricted to specific aspects of the educational task. In this paper, we present and evaluate context-aware framework for collaborative learning applications (CAFCLA) as a solution to these problems. CAFCLA is a flexible framework that covers the entire process of developing collaborative learning activities, taking advantage of contextual information and social interactions. Its application in the experimental case study of a collaborative WebQuest within a museum has shown that, among other benefits, the use of social computing improves the learning process, fosters collaboration, enhances relationships, and increases engagement.
In this paper, we present an approach that can handle Z-numbers in the context of multi-criteria decision-making problems. The concept of Z-number as an ordered pair Z=(A, B) of fuzzy numbers A and B is used, where A is a linguistic value of a variable of interest and B is a linguistic value of the probability measure of A. As human beings, we communicate with each other by means of natural language using sentences like “the journey from home to university most likely takes about half an hour.” The Z-numbers are converted to fuzzy numbers. Then the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are applied to two case studies and compared with the standard approach using crisp values. The results obtained show the feasibility of the approach.
Loss of control (LOC) is considered one of the leading causes of fatal aircraft accidents worldwide. Reducing LOC is critical to improve flight safety. Although it is still vaguely defined, LOC is generally associated with a flight state that is outside the safety envelope, with nonlinear influences of aircraft dynamics and incorrect handling by the flight crew. We have studied how nonlinear factors and pilot operations contribute to LOC. In this study, the stall point and bifurcation point are confirmed using the bifurcation analysis, and the results show that the aircraft will stall when excessive elevator movement is commanded. Moreover, even though there may be an equilibrium state in one of the elevator deflections, the flight state may still be outside the flight safety envelope. When the flight state is near the edge of the flight safety envelope, the strategy to regulate the elevator deflection is super-sensitive, and a slight change in the elevator deflection may contribute to a flight state outside the safety envelope. To solve this issue, the differential manifold theory is introduced to determine the safety envelope. Examples are provided using NASA’s generic transport model.
A radio-over-fiber (RoF) distributed antenna system (DAS) architecture is proposed, where millimeter wave (mm-wave) signals are transmitted to four different radio access units (RAUs) arranged in a ring topology. The proposed architecture transmits duplex data of 128 Mb/s to each RAU in both downlink (DL) and uplink (UL) directions. The radio frequency (RF) signals are transmitted by polarization multiplexing a multi-wavelength source. Millimeter-wave signals at a frequency of 25 GHz are generated at each RAU using remote heterodyne detection. The proposed architecture provides increased coverage while maintaining good bit error rate (BER) results.