Developing an empirical algorithm for protecting text-based CAPTCHAs against segmentation attacks

In this paper, we aim to provide an effective and efficient method to generate text-based Captchas which are resilient against segmentation attack. Different to the popular industry practice of using very simple color schemes, we advocate to use multiple colors in our Captchas. We adopt the idea of brush and canvas when coloring our Captchas. Furthermore, we choose to use simple accumulating functions to achieve diffusion on painted colors and DES encryption to achieve a good level of confusion on the brush pattern. To facilitate ordinary users and developers, we propose an empirical algorithm with support of Taguchi method to guarantee the quality of the chosen color schemes. Our proposed methodology has at least three advantages — 1) the settings of color schemes can be fully customized by the user or developer; 2) the quality of selected colors have desirable statistical features that are ensured by Taguchi method; 3) the algorithm can be fully automated into computer programs. Moreover, our included examples and experiments prove the practicality and validity of our algorithm.

Recognising user identity in twitter social networks via text mining

Social networks have become a convenient and effective means of communication in recent years. Many people use social networks to communicate, lead, and manage activities, and express their opinions in supporting or opposing different causes. This has brought forward the issue of verifying the owners of social accounts, in order to eliminate the effect of any fake accounts on the people. This study aims to authenticate the genuine accounts versus fake account using writeprint, which is the writing style biometric. We first extract a set of features using text mining techniques. Then, training of a supervised machine learning algorithm to build the knowledge base is conducted. The recognition procedure starts by extracting the relevant features and then measuring the similarity of the feature vector with respect to all feature vectors in the knowledge base. Then, the most similar vector is identified as the verified account.

Multi-label classification with clustering for image and text categorization

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.

Metacognitive reading strategies and the text type

This study purported to see the effect of text type on metacognitive reading strategy use. To this end, the Metacognitive Awareness of Reading Strategies Inventory (MARSI) test was used to assess the use of metacognitive strategies and examine the effect of text type (expository and narrative) on these strategies. It also sought to determine to what degree gender, proficiency and motivation could affect the use of this strategy. Results showed that text type is effective in the learners’ choice of the strategies. Students reading expository text significantly used more metacognitive reading strategies than those reading narrative texts. It was also seen that more proficient students used the strategies more than less proficient ones. Moreover, there was a linear relationship between motivation and metacognitive reading strategies. However, gender had no significant effect on the use of metacognitive strategies.

Contract law : text and cases

A blend of academic commentary supported by key cases providing fundamental knowledge of contract law.

Randai Hari Bela Negara

This was a traditional theatre form with an original script in Indonesian (as opposed to the local language of the region) that retold the events leading to the formation of the emergency government of Indonesian following Dutch aggression after Indonesia declared independence. The performance features an original script based on in-depth historical research, originally choreographed dance based on traditional styles, a play script for the actors, and original songs. The preparation of this script required extensive historical research, including interviews, as well as the conversion of traditional art genres into Indonesian for presentation in the modern context. This was the first time a randai, a traditional theatre form, has ben done in Indonesian and represents a significant innovation in the performing and literary arts. The performance involved dance, music, singing, written text, and acting, all of which were composed, directed, and overseen by the author.