This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field $f$ and a set of "signal" persistence pairs to maintain, our approaches produces an output field $g$ that is close to $f$ and which optimizes (i) the cancellation of "non-signal" pairs, while (ii) preserving the "signal" pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. We also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

Abstract Over recent years, the world has experienced explosive growth in the volume of textual data, which makes a manual analysis impossible. Machine learning techniques provided an effective solution to this problem. Due to its capacity to organize the huge and varied amounts of data, it offered valuable insights and it has become an emerging investigative field for the research community. Classification techniques are used to classify data into different classes according to desired criteria. By their simplicity, they give rise to a variety of applications: automated text categorization, opinion mining, and so forth. These processes go through three stages: text representation, features extraction, and the classification process; they still face many difficulties due both to the complex nature of text databases and to the high dimensionality of texts representations. This article presents a new classification approach that learns to classify texts from the most reliable features more accurately. The added advantage of the proposed approach is that it automatically classifies a text without necessarily processing all its features. The experimental results showed that this new classification by thresholds outperforms the state‐of‐the‐art methods. As a result, the obtained f‐measure on automatic text categorization was 95.06% while it is lower on opinion mining.

This paper propose a new method of facial expression recognition from image sequences taking into consideration only the first and the last frames, that represent respectively the neutral state and the emotion state. Our proposed method is based on calculating six distances from eleven points of the face. These points are detected and tracked using supervised decent method, and refer to the four internal parts of the face (eyebrows, eyes, nose, and mouth). After that, two data mining techniques are used to classify facial expression into emotion, taking as input dynamic feature vector deducted from the six distances previously calculated in the first and the last frames. Our method achieved a best recognition rate of 98% in the CK+ database, and 80.6% in the OULU-CASIA VIS database.