Digital Audio Watermarking using EMD for Voice Message Encryption with Added Security

Several accurate watermarking methods for image watermarking have being suggested and implemented to secure various forms of digital data, images and videos however, very few algorithms are proposed for audio watermarking. This is also because human audio system has dynamic range which is wider in comparison with human vision system. In this paper, a new audio watermarking algorithm for voice message encryption based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each frame is then decomposed adaptively, by EMD, into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). The watermark, which is the secret message that is to be sent, along with the synchronization codes are embedded into the extrema of the last IMF, a low frequency mode stable under different attacks and preserving the perceptual quality of the host signal. Based on exhaustive simulations, we show the robustness of the hidden watermark for audio compression, false decryption, re-quantization, resampling. The comparison analysis shows that our method has better performance than other steganography schemes recently reported

Aplikasi Metode Feature Reduction Dalam Pengenalan Wajah Manusia

Pengenalan wajah (face recognition) memiliki banyak aplikasi seperti pada pemeriksaan keamanan, pencarian kembali gambar, dan sebagainya. Dengan metode aplikasi pengenalan wajah yang ada sekarang kebanyakan sistem pengenalan hanya menggunakan gambar wajah dengan  satu macam ekspresi saja. Untuk itu dibutuhkan suatu aplikasi pengenalan wajah yang mampu mengenali wajah dengan berbagai ekspresi, seperti senyum, tertawa, marah, dan ekspresi lainnya. Aplikasi ini bukan hanya mampu mengenali ekspresi wajah tapi juga dapat mencari identitas personal seseorang dengan tingkat keakuratan yang lebih baik. Untuk mempercepat proses pencocokkan wajah, maka ciri wajah tersebut dapat disederhanakan lagi dengan membuang data yang kurang relevan. Metode pengenalan wajah ini dikenal dengan nama metode Feature Reduction. Tools yang digunakan untuk melakukan analisis dan desain adalah Unified Modeling Language. Hasil dari penelitian ini adalah aplikasi untuk mengenali wajah manusia dengan menggunakan metode Feature Reduction

EmotiBlog: towards a finer-grained sentiment analysis and its application to opinion mining

Comunicación presentada en las IV Jornadas TIMM, Torres (Jaén), 7-8 abril 2011.EmotiBlog is a corpus designed for Sentiment Analysis research. Preliminary studies demonstrated its relevance as a Machine Learning resource for detecting subjective information. In this paper we explore additional features by a detailed analysis. In addition, we compare EmotiBlog with other well-known Sentiment Analysis resource such as the JRC corpus. Finally, as a result of our research, we developed an Opinion Mining application, which takes into account user opinions when rating the results of a search engine specialized in mobile phones

An Empirical Study of Sentiment Analysis for Chinese Microblogging

This paper used three machine learning algorithms, three kinds of feature selection methods and three feature weight methods to study the sentiment classification for Chinese microblogging. The experimental results indicate that the performance of SVM is best in three machine learning algorithms; IG is the better feature selection method compared to the other methods, and TF-IDF is best fit for the sentiment classification in Chinese microblogging. Combining the three factors the conclusion can be drawn that the performance of combination of SVM, IG and TF-IDF is best

Improving Sentiment Analysis in Arabic Using Word Representation

The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text. In this paper, we describe how we construct Word2Vec models from a large Arabic corpus obtained from ten newspapers in different Arab countries. By applying different machine learning algorithms and convolutional neural networks with different text feature selections, we report improved accuracy of sentiment classification (91%-95%) on our publicly available Arabic language health sentiment dataset [1]Comment: Authors accepted version of submission for ASAR 201

Research on multi-modal sentiment feature learning of social media content

社交媒体已成为现代社会舆论交流和信息传递的主要平台。针对社交媒体的情感分析对于舆论监控、商业产品导向和股市预测等都具有重大应用价值。但社交媒体内容的多模态性（文本、图片等）让传统的单模态情感分析方法面临许多局限，多模态情感分析技术对跨媒体内容的理解与分析具有重大的理论价值。 多模态情感分析区别于单模态方法的关键问题在于，如何综合利用形态各异的多模态情感信息，来获取整体的情感倾向性，同时考虑单个模态本身在情感表达上的性质。针对该问题，利用社交媒体上的多模态内容在情感表达上所具有的关联性、抽象层级性的特点，提出了一套面向社交媒体的多模态情感特征学习与融合方法，实现多模态情感分析，主要内容和创新点...Social media has become a main platform of public communication and information transmission. Therefore, social media sentiment analysis has great application values in many fields, such as public opinion monitoring, production marking, stock forecasting and so on. But the multi-modal characteristic of social media content (e.g. texts and images) significantly challenges traditional text-based sen...学位：工学硕士院系专业：信息科学与技术学院_模式识别与智能系统学号：3152013115327