Abstract Cryptography and complexity theory have gained a lot of importance because of zero-knowledge proofs. The motive behind zero-knowledge proofs are to provide an obfuscation to the verifier, so that the verifier will not understand the information sent by the prover. Zero-knowledge proofs are normally used to verify a prover’s theorem to a verifier, in such a way that the verifier... Cryptography and complexity theory have gained a lot of importance because of zero-knowledge proofs. The motive behind zero-knowledge proofs are to provide an obfuscation to the verifier, so that the verifier will not understand the information sent by the prover. Zero-knowledge proofs are normally used to verify a prover’s theorem to a verifier, in such a way that the verifier will not be able to discover any supplementary evidence other than the proof given to him. An enigmatic conception was formalized, that lead to the formation zero-knowledge proof systems. In this paper, we have reviewed different zero-knowledge argument / proof techniques. We have also reviewed the proof system implications in the presence of malicious prover and malicious verifier. We have removed the outliers of the experiment by using Mahalanobis distance. Examples related to zero-knowledge argument systems are also given. Read More Read Less

Abstract The purpose of this study is to find an efficient way to discriminate between Attention-deficit/ hyperactivity disorder (ADHD) and Obstructive sleep apnea (OSA). The study collected 120 children (aged 6-12 years) data between 2011 and 2015, who were divided into three groups, ADHD, OSA and a combination of ADHD and OSA. Each group based on the doctor’s determination, using the... The purpose of this study is to find an efficient way to discriminate between Attention-deficit/ hyperactivity disorder (ADHD) and Obstructive sleep apnea (OSA). The study collected 120 children (aged 6-12 years) data between 2011 and 2015, who were divided into three groups, ADHD, OSA and a combination of ADHD and OSA. Each group based on the doctor’s determination, using the DSM-IV diagnostic standards. The data included four questionnaires as follow: CBCL, DBRS, OSA-18 and CSHQ. Therefore, in order to speed up the whole process of clinical diagnosis classification, we train and test three machine learning models to find the best way to help clinical doctor to diagnosis. The study results indicate that in all of subscale items, there were 21 item show significantly difference among three subgroups, especially in the DBRS. Our results also show that Neural Network model has better computational efficiency than CHART and CHAID for subgroups classification. Read More Read Less

Abstract The online social network is becoming more and more advance now a day. Most of the online users are intent to share their information, especially photo through OSN application. Currently, there is a lot of research on Digital Image that focusing on source identification and forgery detection. This paper is focusing on analyzing the source camera identification in Online Social... The online social network is becoming more and more advance now a day. Most of the online users are intent to share their information, especially photo through OSN application. Currently, there is a lot of research on Digital Image that focusing on source identification and forgery detection. This paper is focusing on analyzing the source camera identification in Online Social Network. The techniques consist of sensor imperfection that carries an abundance of information. It is reliable for identification purposes since the digital camera has its own uniquely sensor. Our proposed framework consists of techniques used for extracting the sensor noise from the digital images and then the feature extraction method is applied to extract the image feature. In this framework, Gaussian filter is used to obtain the noisy images. This noisy image is then used for the feature extraction of several texture features. Based on this idea, the extracted features taken from images are then applied to a classifier for identifying source camera. All datasets are analyzed Multilayer Perceptron. The experimental result shows that the best performance of the identification process by combining 4 texture features was achieved with an average accuracy 80%. Read More Read Less