Error correction on 6466 bit encoded links

Le texte intégral de ce document de travail n'est pas disponible en ligne. Une copie papier est disponible à l'Annexe de la bibliothéque. Effectuez une recherche par titre dans le catalogue pour réserver le document. // The full text of this working paper is not available online. A print copy is available in the Library Annex. Search by title in the catalogue to request the paper

Differentiated back-off for Ethernet

Le texte intégral de ce document de travail n'est pas disponible en ligne. Une copie papier est disponible à l'Annexe de la bibliothéque. Effectuez une recherche par titre dans le catalogue pour réserver le document. // The full text of this working paper is not available online. A print copy is available in the Library Annex. Search by title in the catalogue to request the paper

Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques

We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services

Peer-to-Peer IP Traffic Classification Using Decision Tree and IP Layer Attributes

We present a new approach using data-mining technique and, in particular, decision tree to classify peer-to-peer (P2P) traffic in IP networks. We captured the Internet traffic at a main gateway router, performed preprocessing on the data, selected the most significant attributes, and prepared a training-data set to which the decision-tree algorithm was applied. We built several models using a combination of various attribute sets for different ratios of P2P to non-P2P traffic in the training data. We observed that the accuracy of the model increases significantly when we include the attributes “Src IP addr” and “Dst IP addr” in building the model. By detecting communities of peers, we achieved classification accuracy of higher than 98%. Consequently, we recommend that: (a) the classification must be done within the authority of the Internet service providers (ISP) in order to detect communities of peers, and (b) the decision tree needs to be frequently trained to ensure the fairness and correctness of the classification algorithm. Our approach is based only on information in the IP layer, eliminating the privacy issues associated with deep-packet inspection

Data mining EEG signals in depression for their diagnostic value

Quantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models

Additional file 1: Table S1. of Data mining EEG signals in depression for their diagnostic value

Feature Selection—Individually Analyzed Mastoid Referenced EEG Bands. Table S2. Feature Selection—Individually Analyzed Cz Referenced EEG Bands. Table S3. Feature Selection—Combined Analyzed Bands Using the Low (8–10.5) Hz) Alpha EEG Band. Table S4. Feature Selection—Combined Analyzed Bands Using the High (10.5–13 Hz) Alpha EEG Band. Table S5. Feature Selection—Combined Analyzed Bands Using the Total (8.5–13 Hz) Alpha EEG Band. (DOCX 44 kb