Fuzzy decision support system for fertilizer

Fuzzy geographic information systems is a newly emerging field of computational intelligence. It combines fuzzy logic with spatial context. Most of the natural phenomena are fuzzy in nature. They show a degree of uncertainty or vagueness in their extent and attribute, which cannot be expressed by a crisp value. Agriculture is one of the fields of the spatial domain that needs to be described in fuzzy terms. Fertilizer is a key input for the agriculture sector. In this article, the spatial surfaces of fertilizers are developed for the wheat crop using a fuzzy decision support system. The algorithm of our system takes soil nutrients and cropping time as input, applies fuzzy logic on the input values, defuzzifies the fuzzy output to crisp value, and generates a fertilizer surface. The resultant output surface of fertilizer describes the amount of fertilizer needed to cultivate a specific crop in a specified area. The complexity of our algorithm is \(O(mnr)\) , where \(m\) is the height of the raster, \(n\) is the width of the raster, and \(r\) is the number of expert rules.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

Multimedia framework to support eHealth applications

Limited bandwidth resources lead to a number of challenges especially for eHealth applications, which are communicated over IP and wireless networks. These multimedia services include high-resolution videos and have very large file sizes that require a high level of compression to overcome this limitation. Therefore, there is an acute demand for the research community to provide an efficient multimedia framework to encode medical videos with high quality specifically under the conditions of an error-prone environment. Both an affordable delivery framework and effective coding techniques are extremely desirable for the delivery of high-quality eHealth video applications for transmission over heterogeneous networks and devices. In this paper, we propose and demonstrate a multimedia framework to support eHealth applications, which has an improved coding scheme that uses an SVC-scalable extension of MPEC-4 AVC/H.264. Simulation results show that the proposed scheme achieves a significant improvement in terms of the PSNR-Y gain and reduces the picture quality degradation caused by artifacts and distortions, compared to the existing scheme.     

Exploiting Ontology for Software License Agreements

Software end-users need to sign licenses to seal an agreement with the product providers. Habitually, users agree with the license (i.e. terms and conditions) without fully understanding the agreement. To address this issue, an ontological model is developed to formulate the user requirements and license agreements formally. This paper, introduces ontological model that includes the abstract license ontology of common features found in di?erent license agreements. The abstract license ontology is then extended to a few real world license agreements. The resulting model can be used for di?erent purposes such as querying the appropriate licenses for a speciˉc requirement or checking the license terms and conditions with user requirements.     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.

     

Deep learning for graphics recognition: document understanding and beyond

International Journal on Document Analysis and Recognition (IJDAR) -     

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

Behavioral Attestation for Web Services using access policies

Service Oriented Architecture with underlying technologies like web services and web service orchestration opens new vistas for integration among business processes operating in heterogeneous environments. However, such dynamic collaborations require a highly secure environment at each respective business partner site. Existing web services standards address the issue of security only on the service provider platform. The partner platforms to which sensitive information is released have till now been neglected. Remote Attestation is a relatively new field of research which enables an authorized party to verify that a trusted environment actually exists on a partner platform. To incorporate this novel concept in to the web services realm, a new mechanism called WS-Attestation has been proposed. This mechanism provides a structural paradigm upon which more fine-grained solutions can be built. In this paper, we present a novel framework, Behavioral Attestation for Web Services, in which XACML is built on top of WS-Attestation in order to enable more flexible remote attestation at the web services level. We propose a new type of XACML policy called XACML behavior policy, which defines the expected behavior of a partner platform. Existing web service standards are used to incorporate remote attestation at the web services level and a prototype is presented, which implements XACML behavior policy using low-level attestation techniques.