Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models

Soft classification techniques avoid the loss of information characteristic to hard classification techniques when handling mixed pixels. Sub‐pixel mapping is a method incorporating benefits of both hard and soft classification techniques. In this paper an algorithm is developed based on sub‐pixel/pixel attractions. The design of the algorithm is accomplished using artificial imagery but testing is done on artificial as well as real synthetic imagery. The algorithm is evaluated both visually and quantitatively using established classification accuracy indices. The resulting images show increased accuracy when compared to hardened soft classifications.     

CrimeSPOT: A language and runtime for developing active wireless sensor network applications

Advances in wireless sensing and actuation technology allow embedding significant amounts of application logic inside wireless sensor networks. Such active WSN applications are more autonomous, but are significantly more complex to implement. Event-based middleware lends itself to implementing these applications. It offers developers fine-grained control over how an individual node interacts with the other nodes of the network. However, this control comes at the cost of event handlers which lack composability and violate software engineering principles such as separation of concerns. In this paper, we present CrimeSPOT as a domain-specific language for programming WSN applications on top of event-driven middleware. Its node-centric features enable programming a node’s interactions through declarative rules rather than event handlers. Its network-centric features support reusing code within and among WSN applications. Unique to CrimeSPOT is its support for associating application-specific semantics with events that carry sensor readings. These preclude transposing existing approaches that address the shortcomings of event-based middleware to the domain of wireless sensor networks. We provide a comprehensive overview of the language and the implementation of its accompanying runtime. The latter comprises several extensions to the Rete forward chaining algorithm. We evaluate the expressiveness of the language and the overhead of its runtime using small, but representative active WSN applications.     

Guarding against online threats: why entrepreneurs take protective measures

Information technology is becoming increasingly important for entrepreneurs. Protecting their technical infrastructure and stored data is, therefore, also becoming more vital. Nevertheless, research into the safety and security of entrepreneurs in general, and online threats targeted at entrepreneurs in particular, is still limited. This study investigates to what extent self-employed entrepreneurs protect themselves against online threats, and what motivates them to do so. Based on secondary analyses on data collected from 1622 Dutch entrepreneurs, we observe that the majority implement technical and personal coping measures. By adopting protection motivation theory as a theoretical basis for our study, we found that coping appraisal explains the adoption of protective measures. Entrepreneurs are likely to implement protective measures when they believe a measure is effective, when they are capable of using internet technology, when their attitude towards online protection is positive and when they believe they are responsible for their own online security. Although the secondary analysis provides some limitations, this study offers new insights into the usage of protective measures and the motivation for taking them. These insights can help to improve information security campaigns.     

Toric forms of elliptic curves and their arithmetic

We scan a large class of one-parameter families of elliptic curves for efficient arithmetic. The construction of the class is inspired by toric geometry, which provides a natural framework for the study of various forms of elliptic curves. The class both encompasses many prominent known forms and includes thousands of new forms. A powerful algorithm is described that automatically computes the most compact group operation formulas for any parameterized family of elliptic curves. The generality of this algorithm is further illustrated by computing uniform addition formulas and formulas for generalized Montgomery arithmetic.     

Filter-resistant code injection on ARM

Code injection attacks are one of the most powerful and important classes of attacks on software. In these attacks, the attacker sends malicious input to a software application, where it is stored in memory. The malicious input is chosen in such a way that its representation in memory is also a valid representation of a machine code program that performs actions chosen by the attacker. The attacker then triggers a bug in the application to divert the control flow to this injected machine code. A typical action of the injected code is to launch a command interpreter shell, and hence the malicious input is often called shellcode. Attacks are usually performed against network facing applications, and such applications often perform validations or encodings on input. Hence, a typical hurdle for attackers, is that the shellcode has to pass one or more filtering methods before it is stored in the vulnerable application??s memory space. Clearly, for a code injection attack to succeed, the malicious input must survive such validations and transformations. Alphanumeric input (consisting only of letters and digits) is typically very robust for this purpose: it passes most filters and is untouched by most transformations. This paper studies the power of alphanumeric shellcode on the ARM architecture. It shows that the subset of ARM machine code programs that (when interpreted as data) consist only of alphanumerical characters is a Turing complete subset. This is a non-trivial result, as the number of instructions that consist only of alphanumeric characters is very limited. To craft useful exploit code (and to achieve Turing completeness), several tricks are needed, including the use of self-modifying code.     

Microblog language identification: overcoming the limitations of short, unedited and idiomatic text

Multilingual posts can potentially affect the outcomes of content analysis on microblog platforms. To this end, language identification can provide a monolingual set of content for analysis. We find the unedited and idiomatic language of microblogs to be challenging for state-of-the-art language identification methods. To account for this, we identify five microblog characteristics that can help in language identification: the language profile of the blogger (blogger), the content of an attached hyperlink (link), the language profile of other users mentioned (mention) in the post, the language profile of a tag (tag), and the language of the original post (conversation), if the post we examine is a reply. Further, we present methods that combine these priors in a post-dependent and post-independent way. We present test results on 1,000 posts from five languages (Dutch, English, French, German, and Spanish), which show that our priors improve accuracy by 5 % over a domain specific baseline, and show that post-dependent combination of the priors achieves the best performance. When suitable training data does not exist, our methods still outperform a domain unspecific baseline. We conclude with an examination of the language distribution of a million tweets, along with temporal analysis, the usage of twitter features across languages, and a correlation study between classifications made and geo-location and language metadata fields.