There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

1. Place animal in induction chamber and anesthetize the mouse and ensure sedation. 2. Once the animal is sedated, move it to a nose cone for hair removal using cream. Only apply cream to the area of the chest that will be utilized for imaging. Once the hair is removed, wipe area with wet gauze to ensure all hair is removed. 3. Move the animal to the imaging platform and tape its paws to the ECG lead plates and insert rectal probe. Body temperature should be maintained at 36-37°C. During imaging, reduce anesthesia to maintain proper heart rate. If the animal shows signs of being awake, use a higher concentration of anesthetic.

人唾液天疱疮抗原的纯化

Computer communications

Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews

Sentiment analysis (SA) is a continuing field of research that lies at the intersection of many fields such as data mining, natural language processing and machine learning It is concerned with the automatic extraction of opinions conveyed in a certain text Due to its vast applications, many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention This survey presents a comprehensive overview of the works done so far on Arabic SA (ASA) The survey groups published papers based on the SA-related problems they address and tries to identify the gaps in the current literature laying foundation for future studies in this field

A comprehensive survey of arabic sentiment analysis

Social networks contain large amounts of posts of different data types (text, images, sounds and videos). Textual posts express authors' opinions (with or against) or feeling (love, hate, optimism, pessimism, or anger). Such opinions are important for commercial and governmental organization since they help checking public opinion about a product, policy or an object in general. In this paper we present the application of two different approaches to classify Arabic Facebook posts. The first one depends on syntactic features, using common patterns used in different Arabic dialects to express opinions. These patterns achieved high accuracy in determining the polarity of a sentiment even when tested against new corpus. This approach acts on informal Arabic text, which has not been addressed before. Different setups were tried and the highest coverage and accuracy achieved were 49.5% and 83.4 % respectively. The second approach is an ordinary probabilistic model, Naive-Bayes classifier, that assumes the independence of features in determining the class the highest coverage achieved in this approach was 60.5% in the first setup and 91.2% when Naive search was used as a binary classifier to classify the posts as objective or subjective.

Classifying sentiment in arabic social networks: Naïve search versus Naïve bayes

Type-aware virtual machine management for energy efficient cloud data centers

Automatic emotion recognition from speech signals without linguistic cues has been an important emerging research area. Integrating emotions in human---computer interaction is of great importance to effectively simulate real life scenarios. Research has been focusing on recognizing emotions from acted speech while little work was done on natural real life utterances. English, French, German and Chinese corpora were used for that purpose while no natural Arabic corpus was found to date. In this paper, emotion recognition in Arabic spoken data is studied for the first time. A realistic speech corpus from Arabic TV shows is collected. The videos are labeled by their perceived emotions; namely happy, angry or surprised. Prosodic features are extracted and thirty-five classification methods are applied. Results are analyzed in this paper and conclusions and future recommendations are identified.

Emotion recognition in Arabic speech

Connected and Autonomous Electric Vehicles: Quality of Experience survey and taxonomy

Sentiment Analysis is the process of identifying sentiment from text written in a natural language with respect to the entity it is referring to. Sentiment lexicons are used to perform this task. Several lexicons are available to perform this task in English using WordNet. In this paper, we present a new method to create a sentiment lexicon for the Arabic language using semi-supervised learning on the WordNet and matching them with an Arabic database.

Rached Zantout

Papers

Classifying sentiment in arabic social networks: Naïve search versus Naïve bayes

Type-aware virtual machine management for energy efficient cloud data centers

Emotion recognition in Arabic speech

Connected and Autonomous Electric Vehicles: Quality of Experience survey and taxonomy

Sentiment analysis: Arabic sentiment lexicons