dataesr/scientific-paragraphs-categorization huggingface dataset:
YEARS) PATHoLogICAL TUMoR SIzE ($20 mm) PATHoLogICAL TUMoR gRADE (3) SIgNATURE (PREDICTED PooR PRogNoSIS) p HAzARD RATIo [95% CI] 0. 006 5. 21 [1. 62-16. 7] 0. 001 4. 03 [1. 75-9. 3] 0. 002 3. 61 [1. 61-8. 1] 0. 002 3. 65 [1. 63-8. 17] 17. ER STATUS HER2 STATUS (NEgATIvE) (PoSITIvE) HAzARD RATIo p HAzARD RATIo p HAzARD RATIo p [95% CI] [95% CI] [95% CI] 1. 07 [0. 47-2. 4] 0. 88 0. 96 [0. 49-1. 9] 0. 91 1. 58 [0. 77-3. 24] 0. 22 1. 66 [0. 77-3. 57] 0. 20 0. 73 [0. 35-1. 54] 0. 41 2. 24 [1. 08-4. 64] 0. 03 1. 64 [0. 76-3. 57] 0. 21 0. 74 [0. 35-1. 58] 0. 44 1. 79 [0. 87-3. 68] 0. 12 1. 61 [0. 75-3. 46] 0. 23 0. 76 [0. 36-1. 59] 0. 46 1. 82 [0. 88-3. 75] 0. 10 1. 92 [0. 93-3. 99] 0. 08 1. 73 [0. 86-3. 49] 0. 12 2. 47 [0. 86-7. 08] 0. 09 HAzARD RATIo p HAzARD RATIo p [95% CI] [95% CI] van de vijver's dataset (n = 151) amsterdam 70-gene 1.
28 [0. 66-2. 48] 0. 47 3. 27 [1. 54-6. 97] 0. 002 CoxBoost 6-gene 1. 39 [0. 72-2. 69] 0. 33 3. 59 [1. 71-7. 5] ,0. 001 Lasso 18-gene 1. 62 [0. 83-3. 16] 0. 16 3. 31 [1. 59-6. 87] 0. 001 elastic net 29-gene 1. 51 [0. 78-2. 93] 0. 23 3. 19 [1. 55-6. 58] 0. 002 Desmedt's dataset (n = 197) amsterdam 70-gene 1. 43 [0. 64-3. 19] 0. 39 1 [0. 51-1. 96] 0. 99.
Dataset for benchmark constraints In order to test the accuracy and reliability of our algorithm with constraints, we employ two different datasets. The first one is the dataset which is used by RNA-SSD  [10]  (dataset A). This dataset contains 8 natural structures of length between 65 and 583 nucleotides. The biological description of these structures are presented in Table  1. The second dataset is chosen from  [24]  which contains 12 structures of length 178 and 176 nucleotides (dataset B), consisting of artificial miRNA structures that are published in  [25]  (see Table  2 ). Since no sequence constraints are available for the structures in dataset A, three different sets of constraints are randomly generated for this dataset. In the first set of  constraints, only 10% of positions in each structure are randomly selected and a random nucleotide (with uniform distribution) is assigned to each selected position and considered as sequence constraints (dataset A -C10).
In the second and third sets of constraints, respectively 20% and 30% of positions are randomly selected, fixed, and considered as constraints (datasets A -C20 and A -C30, respectively). For the structures in dataset B, their corresponding sequence constraints are available, in which the natural RNA sequences of the structures are employed to fix some positions. The third dataset (dataset C) contains 408 sequences of length between 36 and 1509 nucleotides that are selected from Rfam 11. To this end, the first sequence in each block of 940 sequences in Rfam 11 is selected. The results of our algorithm over the above mentioned datasets are compared with the results of four other approaches, namely INFO-RNA, MODENA, NUPACK, and RNAiFold. Different measures are employed in our comparisons to determine the accuracy and reliability of the competitor approaches.
All the results (except for RNAiFold) are obtained by a computer with Intel Core2Duo (2. 26 GHz) CPU, having 2GB of memory, and running Linux Ubuntu (11. 04) as operating system. The Vienna RNA Package (version 1. 8. 5) along with the Turner free energy parameters  [26]  are employed in all approaches. NUPACK is the only approach that uses the ensemble defect as a fitness function, where the threshold value is considered 0. 01. As for MODENA, the default parameters (50 for both population size and iterations) are utilized. The results of RNAiFold are either obtained from its web server (for datasets A and B) or generated by a powerful cluster (for dataset C). Since the hardware specification to run the RNAiFold is different, the time is not provided for this method in our comparisons. For each structure and its related constraint sequence in datasets A and B, all the mentioned approaches are executed 50 times, where the time limit of 1800 seconds is considered for each execution.
The success count (SC) indicates how often each approach successfully designs an RNA sequence (among 50 executions) for each given structure and constraint. The expected time (E T ) indicates how much time is required for successfully designing an RNA sequence for each structure and it is calculated as follows: E T = Total Execution Time SC. ( 1 ) All the mentioned approaches are executed one time for the structures and their corresponding sequences in dataset C. The time limit of 3600 seconds are considered for each execution. Here, the SC indicates how many sequences are successfully designed for 408 structures in this dataset. The expected summation of energy distance (E ED ) indicates how much energy value of successfully designed sequences of any approach is different from the energy value of natural sequence and it is calculated as follows: E ED = | E sd -E n | SC , ( 2 ) where E sd indicates the energy value of successfully designed sequence and E n indicates the energy value of it corresponding natural sequence.
The accuracy and speed comparisons of our algorithm with respect to the other approaches are presented in Tables 3, 4 and 5, respectively for the constructed datasets of random constraints A -C10, A -C20, and A -C30. The ∞ sign in these tables indicates that the corresponding approach could not design an appropriate sequence for the given structure and constraint in the period of the execution time limit. The best results are also indicated in bold face in these tables. Since MODENA returns all correctly generated sequences as results (among 50 sequences in its final population), the E T of MODENA is calculated by considering one execution time as its total execution time, in order to be fair in our comparison. As it is mentioned in Tables 3, 4 and 5, our algorithm A7 0 ∞ 0 ∞ 0 ∞ 0 0 ∞ A8 0 ∞ 0 ∞ 45 1133.
91 0 0 ∞ The success count (SC) and expected time (E T ) comparison between the existing approaches for dataset AC20, including 20% of sequence constraints. The ∞ represents no result and the bold faces indicate the best results. The same comparisons between the existing approaches for dataset B are presented in Table  6. The ERD-EC column in this table relates to the results of ERD when the energy constraint is also specified. The energy interval for the generated sequences for dataset B is considered as the natural energy value ±10. As it is understood, the computational time is increased when the energy interval is specified. Again, the superiority of our approach is concluded form this table. Table  7  indicates that the average energy values of the generated sequences by ERD over the given structures are closer to the natural energies, compared with the other approaches.
Also, when the energy constraint is applied, more reliable sequences are generated by ERD. This helps us to select an RNA sequence whose secondary structure has free energy value closer to that of natural counterparts. Comparing with the other methods, INFO-RNA is superior in generating sequences that have lower minimum free energy structure. 0 0 ∞ A5 0 ∞ 0 ∞ 0 ∞ 0 5 2472. 85 A6 0 ∞ 0 ∞ 0 ∞ 0 0 ∞ A7 0 ∞ 0 ∞ 0 ∞ 0 0 ∞ A8 32 Two important questions are, 1) Which approach can generate divers sequences for the given target structure? and 2) Which approach can generate sequences similar to natural one? To answer these questions, the similarity between the generated sequences as well as the similarity of them to the corresponding natural sequence must be calculated. To do this, the needle software from the EMBOSS is employed  [27]  to calculate the similarities.
EMBOSS (the European Molecular Biology Open Software Suite) is a free open source software analysis package  specially developed for the needs of the molecular biologist. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is an appropriate platform to allow other scientists to develop and release software. needle uses the Needleman-Wunsch alignment algorithm to find the optimum alignment (including gaps) of two sequences along their entire length  [28, 29]. The algorithm uses a dynamic programming method to ensure the alignment is optimum, by exploring all possible alignments and choosing the best one. A scoring matrix is provided for every possible residue or nucleotide match.
For each approach, the expected similarity between the generated sequences for different dataset are presented in Table  8. Since the success counts are almost different for all approach, the expected similarity for the generated sequences by each approach (E SA ) is calculated as follows: The expected similarity (E SA ) between the generated sequences by each approach on different datasets. The lower similarities are indicated in bold face. E SA = 1 |D| I∈D SC(I) i=1 SC(I) j=i+1 similarity(S i , S j ) SC(I)(SC(I)-1) 2 , ( 3 ) where D is the employed dataset, I indicates a structure of D, SC(I) represents the success count for the structure I, S i (S j ) indicates the ith (jth) successfully designed sequence for the structure I, and similarity(S i , S j ) calculates the similarity between S i and S j.
As it is understood from Table  8 , the generated sequences by ERD are less similar (more divers) comparing with the other competitors. Also, the expected similarity of the generated sequences by each approach to the corresponding natural sequence (E SN ) is calculated as follows: E SN = SC i=1 similarity(S A i , S N i ) SC , ( 4 ) where S A i is the ith successfully designed sequence for each approach and S N i is the corresponding natural sequence in dataset C. The calculated values of E SN for each approach are shown in Table  9. As it is obvious, ERD is superior in time, accuracy, and similarity to the natural sequences comparing with the other approach. The  ERD-EC in this table is related to the results of ERD when the energy constraint (±10% of the natural energy value) is specified.
The success count (SC) and expected time (E T ) comparison between the existing approaches for dataset A-C30, including 30% of sequence constraints. The ∞ represents no result and the bold faces indicate the best results. performs much better than MODENA in all cases, and it is better than INFO-RNA and RNAiFold in most of the cases. Compared with NUPACK, our algorithm is superior specially in terms of computational time. 52. 13 43 1124. 62 25 1059. 20 33 50 124. 56.
THE CHAOTIC-FWT IMAGE ENCRYPTION ALGORITHM Our proposed encryption scheme of gray-scale image, which has been implemented in MATLAB, is presented in detail in this section. We use an external secret key, two chaotic logistic maps and the FWT to achieve the goal of the encryption. Two basis of image encryption processes are used: the permutation and the substitution. For image encryption methods based on chaos, those processes are applied directly on the pixel values of images. In  [28] , those processes are used to encrypt the image and then the encryption image is scrambled by the discrete Walsh transform. Thus, image encryption through chaos and the discrete Walsh transform are performed separately. In this work, we realize image encryption performing chaos and the discrete Walsh transform simultaneously.
This indicates that the chaotic FWT has hidden information randomly and information leakage in encryption process is negligible. We also conclude the effectiveness of the algorithm because of the highest value of entropy information. Table  1 : Correlation coefficients an entropy information of some medical images from  [31]. To prove the effectiveness of the algorithm on any type of image, we apply it on various other non medical images. We have used the USC-SIPI image database which is a collection of digitized image available and maintained by the University of Southern California  [32]. We have chosen miscellaneous volume to measure the correlation coefficient of several USC-SIPI image databases. Table  2  shows the results of those images. The same as for medical images, correlations Co of images in table 2 are near to zero and the entropy information is near to eight.
INTRODUCTION To improve the SIM 3D software developed in our laboratory, have created a more general tool to calculate potential distributions and concentrations of free carriers, by a numerical solution of equations of transport for III -V semiconductors. Worth τ 0 , considerably higher than the dielectric relaxation time τrd (τ rd <<τ 0 ), the results can be analyzed using simple injection and assuming a common unit holders in excess life expectancy which is constant for electrons and holes through the ν region this type of semiconductor will be called a semiconductor to life according to the terminology of Van Roosbroeck. In the contrary case or τ 0 is much lower than the dielectric relaxation time τ rd (τ rd >>τ 0 )  [1] , the effects of space charge is very important by any and lifetimes of excess carriers vary greatly from one point to another along the structure.
In this paper, we consider the task of text categorization as a graph classification problem. By representing textual documents as graph-of-words instead of historical n-gram bag-of-words, we extract more discriminative features that correspond to long-distance n-grams through frequent subgraph mining. Moreover, by capitalizing on the concept of k-core, we reduce the graph representation to its densest part -its main core -speeding up the feature extraction step for little to no cost in prediction performances. Experiments on four standard text classification datasets show statistically significant higher accuracy and macro-averaged F1-score compared to baseline approaches.
In our work, we explored a graph representation of text, namely graph-of-words, to challenge the traditional bag-of-words representation and help better classify textual documents into categories. We first trained a classifier using frequent subgraphs as features for increased effectiveness. We then reduced each graph-of-words to its main core before mining the features for increased efficiency. Finally, we also used this technique to reduce the total number of n-gram features considered in the baselines for little to no loss in prediction performances. The rest of the paper is organized as follows. Section 2 provides a review of the related work. Section 3 defines the preliminary concepts upon which our work is built. Section 4 introduces the proposed approaches. Section 5 describes the experimental settings and presents the results we obtained on four standard datasets.
Subgraphs as features The main idea is to mine frequent subgraphs and use them as features for classification, be it with Adaboost  (Kudo et al. , 2004)  or a linear SVM  (Deshpande et al. , 2005). Indeed, most datasets that were used in the associated experiments correspond to chemical compounds where repeating substructure patterns are good indicators of belonging to one particular class. Some popular graph pattern mining algorithms are gSpan  (Yan and Han, 2002) , FFSM  (Huan et al. , 2003)  and Gaston  (Nijssen and Kok, 2004). The number of frequent subgraphs can be enormous, especially for large graph collections, and handling such a feature set can be very expensive. To overcome this issue, recent works have proposed to retain or even only mine the discriminative subgraphs, i. e. features that contribute to the classification decision, in particular gBoost  (Saigo et al.
, 2009) , CORK  (Thoma et al. , 2009)  and GAIA  (Jin et al. , 2010). However, when experimenting, gBoost did not converge on our larger datasets while GAIA and CORK consider subgraphs of node size at least 2, which exclude unigrams, resulting in poorer performances. Moreover, all these approaches have been developed for binary classification, which meant mining features as many times as the number of classes instead of just once (one-vs-all learning strategy). In this paper, we tackle the scalability issue differently through an unsupervised feature selection approach to reduce the size of the graphs and a fortiori the number of frequent subgraphs. et al. (2003)  proposed the first kernels between graphs (as opposed to previous kernels on graphs, i. e. between nodes) based on either random walks or cycles to tackle the problem of classification between graphs.
Similar works The work of  Markov et al. (2007)  is perhaps the closest to ours since they also perform subgraph feature mining on graph-of-words representations but with non-standard datasets and baselines. The works of  Jiang et al. (2010)  and  Arora et al. (2010)  are also related but their representations are different and closer to parse and dependency trees used as base features for text categorization by  Kudo and Matsumoto (2004)  and  Matsumoto et al. (2005). Moreover, they do not discuss the choice of the support value, which controls the total number of features and can potentially lead to millions of subgraphs on standard datasets.
Unsupervised feature mining using gSpan We considered the task of text categorization as a graph classification problem by representing textual documents as graph-of-words and then extracting subgraph features to train a graph classifier. Each document is a separate graph-of-words and the collection of documents thus corresponds to a set of graphs. Therefore, for larger datasets, the total number of graphs increases but not the average graph size (the average number of unique terms in a text), assuming homogeneous datasets. Because the total number of unique node labels corresponds to the number of unique terms in the collection in our case, graph kernels are not suitable for us as verified in practice using the MATLAB code made available by  Shervashidze (2009). We therefore only explored the methods that consider subgraphs as features.
Multiclass scenario In standard binary graph classification (e. g. , predicting chemical compounds' carcinogenicity as either positive or negative  (Helma et al. , 2001) ), feature mining is performed on the whole graph collection as we expect the mined features to be able to discriminate between the two classes (thus producing a good classifier). However, for the task of text categorization, there are usually more than two classes (e. g. , 118 categories of news articles for the Reuters-21578 dataset) and with a skewed class distribution (e. g. , a lot more news related to "acquisition" than to "grain"). Therefore, a single support value might lead to some classes generating a tremendous number of features (e. g. , hundreds of thousands of frequent subgraphs) and some others only a few (e.
g. , a few hundreds subgraphs) resulting in a skewed and non-discriminative feature set. To include discriminative features for these minority classes, we would need an extremely low support resulting in an exponential number of features because of the majority classes. For these reasons, we decided to mine frequent subgraphs per class using the same relative support (%) and then aggregating each feature set into a global one at the cost of a supervised process (but which still avoids crossvalidated parameter tuning). This was not needed for the tasks of spam detection and opinion mining since the corresponding datasets consist of only two balanced classes.
Datasets We used four standard text datasets: two for multiclass document categorization (WebKB and R8), one for spam detection (LingSpam) and one for opinion mining (Amazon) so as to cover all the main subtasks of text categorization: • WebKB: 4 most frequent categories among labeled webpages from various CS departments -split into 2,803 for training and 1,396 for test  (Cardoso-Cachopo, 2007, p. 39-41). • R8: 8 most frequent categories of Reuters-21578, a set of labeled news articles from the 1987 Reuters newswire -split into 5,485 for training and 2,189 for test  (Debole and Sebastiani, 2005). • LingSpam: 2,893 emails classified as spam or legitimate messages -split into 10 sets for 10-fold cross validation  (Androutsopoulos et al. , 2000). • Amazon: 8,000 product reviews over four different sub-collections (books, DVDs, electronics and kitchen appliances) classified as positive or negative -split into 1,600 for training and 400 for test each  (Blitzer et al.
Implementation We developed our approaches mostly in Python using the igraph library  (Csardi and Nepusz, 2006)  for the graph representation and main core extraction. For unsupervised subgraph feature mining, we used the C++ implementation of gSpan from gBoost  (Saigo et al. , 2009). Finally for classification and standard n-gram text categorization we used scikit  (Pedregosa et al. , 2011) , a standard Python machine learning library.
Results Table  2  shows the results on the four considered datasets. The first three rows correspond to the baselines: unsupervised n-gram feature extraction and then supervised learning using kNN, NB (Multinomial but Bernoulli yields similar results) and linear SVM. The last three rows correspond to our approaches. In our first approach, denoted as "gSpan + SVM", we mine frequent subgraphs (gSpan) as features and then train a linear SVM. These features correspond to long-distance n-grams. This leads to the best results in text categorization on almost all datasets (all if we compare to baseline methods), in particular on multiclass document categorization (R8 and WebKB). In our second approach, denoted as "MC + gSpan + SVM", we repeat the same procedure except that we mine frequent subgraphs (gSpan) from the main core (MC) of each graph-of-words and then train an SVM on the resulting features.
Main cores can vary from 1-core to 12-core depending on the graph structure, 5-core and 6-core being the most frequent (more than 60%). This yields results similar to the SVM baseline for a faster mining and training compared to gSpan + SVM. Table  1  (upper table) shows the reduction in the dimension of the feature space and we see Table  2 : Test accuracy and macro-average F1-score on four standard datasets. Bold font marks the best performance in a column. * indicates statistical significance at p < 0. 05 using micro sign test with regards to the SVM baseline of the same column. MC corresponds to unsupervised feature selection using the main core of each graph-of-words to extract n-gram and subgraph features. gSpan mining support values are 1. 6% (WebKB), 7% (R8), 4% (LingSpam) and 0. 5% (Amazon).
that on average less than 60% of the subgraphs are kept for little to no cost in prediction effectiveness. In our final approach, denoted as "MC + SVM", we performed unsupervised feature selection by keeping the terms appearing in the main core (MC) of each document's graph-of-words representation and then extracted standard n-gram features. Table 1 (lower table) shows the reduction in the dimension of the feature space and we see that on average less than half the n-grams remain. Figure  2  shows the distribution of non-zero features before and after the feature selection on the R8 dataset. Similar changes in distribution can be observed on the other datasets, from a right-tail Gaussian to a power law distribution as expected from the main core retention. Table  2  shows that the main core retention has little to no cost in accuracy and F1score but can reduce drastically the feature space and the number of non-zero values per document.
Conclusion In this paper, we tackled the task of text categorization by representing documents as graphof-words and then considering the problem as a graph classification one. We were able to extract more discriminative features that correspond to long-distance n-grams through frequent subgraph mining. Experiments on four standard datasets show statistically significant higher accuracy and macro-averaged F1-score compared to baselines. To the best of our knowledge, graph classification has never been tested at that scale -thousands of graphs and tens of thousands of unique node labels -and also in the multiclass scenario. For these reasons, we could not capitalize on all standard methods. In particular, we believe new kernels that support a very high number of unique node labels could yield even better performances.