Caesar cipher has been cracked by computer by using relative frequency. And though further advanced techniques are developed, they have extreme computational complexity so in order to reduce computational complexity and yet to increase security, advancement in Caesar cipher is done by changing the entire order of alphabets and then the cipher text is again encrypted & finally following delta formation technique, all together which forms an unconditionally secure cipher. For the clients who is worried about computationally complexity rather than bandwidth of transmission
Cross-modal recipe retrieval has gained prominence due to its ability to retrieve a text representation given an image representation and vice versa. Clustering these recipe representations based on similarity is essential to retrieve relevant information about unknown food images. Existing studies cluster similar recipe representations in the latent space based on class names. Due to inter-class similarity and intraclass variation, associating a recipe with a class name does not provide sufficient knowledge about recipes to determine similarity. However, recipe title, ingredients, and cooking actions provide detailed knowledge about recipes and are a better determinant of similar recipes. In this study, we utilized this additional knowledge of recipes, such as ingredients and recipe title, to identify similar recipes, emphasizing attention especially on rare ingredients. To incorporate this knowledge, we propose a knowledge-infused multimodal cooking representation learning network, Ki-Cook, built on the procedural attribute of the cooking process. To the best of our knowledge, this is the first study to adopt a comprehensive recipe similarity determinant to identify and cluster similar recipe representations. The proposed network also incorporates ingredient images to learn multimodal cooking representation. Since the motivation for clustering similar recipes is to retrieve relevant information for an unknown food image, we evaluated the ingredient retrieval task. We performed an empirical analysis to establish that our proposed model improves the Coverage of Ground Truth by 12% and the Intersection Over Union by 10% compared to the baseline models. On average, the representations learned by our model contain an additional 15.33% of rare ingredients compared to the baseline models. Owing to this difference, our qualitative evaluation shows a 39% improvement in clustering similar recipes in the latent space compared to the baseline models, with an inter-annotator agreement of the Fleiss kappa score of 0.35.
The amount of plaque in coronary arteries in any particular point is identified by the IntraVascular UltraSound (IVUS) images. The classification of IVUS images is very important to diagnose various coronary artery diseases. In this study, the classification of IVUS images based on Non-negative Matrix Factorization (NMF) technique and Maximum Likelihood Classifier (MLC) is presented. Initially, the IVUS images are given to frost filter to remove speckle noise as the imaging technique uses ultrasound waves. Then, NMF technique is employed to extract the features and stored in database. Then MLC is used for classification of IVUS images for both normal and abnormal categories. The IVUS Image Classification (IIC) system obtains 98% classification accuracy by using NMF features and MLC classification.
Internet is growing very rapidly; so is its security issues. There are a wide variety of attacks possible in networked machines. DOS attack, buffer overflow attack, cross site attack, DNS exploit attack are a few to name. Without security measures and controls in place, network and data might be subjected to attacks. The commonly deployed security devices are firewall, IDS, IPS, anti-virus etc. Potential number of threats is still pervading which are formulated as attacks by combining many unnoticed primitive events. The best solution is to install a Complex Event Processing (CEP) system which can analyze multiple devices to infer attack patterns. Log information of network devices is the best choice for analysis. In a large network, there will be millions of events logged. Correlated analysis of this huge volume of log is the main challenge in Complex Event Processing (CEP) system. We describe a method to reduce the input to the Complex Event Processing (CEP) system, using Support Vector Machine (SVM) classifier. Our experiment shows that the input size can be considerably reduce using the classifier. Hence improves the working of Complex Event Processing (CEP) system.
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