Tempering–preservation treatment inactivated lipase in wheat bran and retained phenolic compounds

Wheat bran is rich in functional ingredients, but the high level of lipase limits its applications. Tempering–preservation treatment (at 70–90 °C with moisture of 20%–40% for 1–4 h) was exploited for stabilising wheat bran and its effect on polyphenols was investigated. The results showed that more lipase was inactivated at higher tempering moisture, temperature and longer time. The optimum condition for inactivation of wheat bran lipase was 30% moisture and 90 °C for 4 h. The inactivation rate reached 93.8% with a residual enzyme activity of 0.264 U g⁻¹. Under the optimum condition, the sum of free phenolic acids rose from 25.4 to 55.8 µg g⁻¹. As for bound phenolic acids, there was a slight increase of hydroxybenzoic acid derivatives but a slight decrease of hydroxycinnamic acid derivatives. The total contents of phenolic acids before and after stabilisation were not significantly different. This study showed the possibility of using tempering–preservation as an efficient method for inactivation of wheat bran lipase while maintaining its phenolic compounds, which could be used in the production of whole wheat flour.     

A flaw in the electronic commerce protocol SET

The Secure Electronic Transaction (SET) protocol has been developed by the major credit card companies in association with some of the top software corporations to secure e-commerce transactions. This paper recalls the basics of the SET protocol and presents a new flaw: a dishonest client may purchase goods from an honest merchant (with the help of another merchant) for which he does not pay. Fortunately, by checking his balance sheet, the merchant may trace with the help of his bank the client and his accomplice. We also propose a modification to fix the flaw.     

Permeability reduction by asphaltenes and resins deposition in porous media

The deposition of crude oil polar fractions such as asphaltenes and resins in oil reservoir rocks reduce considerably the rock permeability and the oil production. In the present work, a crude oil and various core samples were extracted from Rhourd–Nouss (RN) reservoir rock. Afterwards, core flow experiments were carried out in the laboratory to investigate permeability reduction that causes formation damage. The core permeability damage was evaluated by flooding Soltrol, through the sample and measuring the solvent permeabilities, K_i and K_f, respectively, before and after injection of a given pore volume number of the crude oil.The data indicate that upon flooding the crude oil through the porous medium, considerable permeability reduction, expressed as the ratio (K_i − K_f)/K_i, and ranging from 72.4% to 98.3% were observed. The permeability reduction is found to result from irreversible retention of asphaltenes and resins in the porous core sample. However, no correlations could be established between the depth of the well, the core porosity, the core mineral compositions determined by X-ray analysis, and the permeability damage factors. In addition, effluents flowing away from RN wells were collected and analysed at various periods, after carrying out aromatic solvents squeezes. The amount of saturates, aromatics, resins, and asphaltenes (SARA analysis), of the crude oil, the deposited crude oil fraction, and the effluent’s residues were measured and compared. The asphaltenes weight percent was found to increase from 1.56% for the crude oil to 11.42% for the deposited oil fraction, and was in the range 1.37–2.36% for the effluent’s residues. Such results indicate that the deposited oil fraction and the effluent’s residues consist mainly of asphaltenes and resins.     

A New Method for Sentiment Analysis Using Contextual Auto-Encoders

Sentiment analysis, a hot research topic, presents new challenges for understanding users’ opinions and judgments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In this paper, we introduce a novel machine learning framework using auto-encoders network to predict the sentiment polarity label at the word level and the sentence level. Inspired by the dimensionality reduction and the feature extraction capabilities of the auto-encoders, we propose a new model for distributed word vector representation “PMI-SA” using as input pointwise-mutual-information “PMI” word vectors. The resulted continuous word vectors are combined to represent a sentence. An unsupervised sentence embedding method, called Contextual Recursive Auto-Encoders “CoRAE”, is also developed for learning sentence representation. Indeed, CoRAE follows the basic idea of the recursive auto-encoders to deeply compose the vectors of words constituting the sentence, but without relying on any syntactic parse tree. The CoRAE model consists in combining recursively each word with its context words (neighbors’ words: previous and next) by considering the word order. A support vector machine classifier with fine-tuning technique is also used to show that our deep compositional representation model CoRAE improves significantly the accuracy of sentiment analysis task. Experimental results demonstrate that CoRAE remarkably outperforms several competitive baseline methods on two databases, namely, Sanders twitter corpus and Facebook comments corpus. The CoRAE model achieves an efficiency of 83.28% with the Facebook dataset and 97.57% with the Sanders dataset.     

A new semantic relatedness measurement using WordNet features

Computing semantic similarity/relatedness between concepts and words is an important issue of many research fields. Information theoretic approaches exploit the notion of Information Content (IC) that provides for a concept a better understanding of its semantics. In this paper, we present a complete IC metrics survey with a critical study. Then, we propose a new intrinsic IC computing method using taxonomical features extracted from an ontology for a particular concept. This approach quantifies the subgraph formed by the concept subsumers using the depth and the descendents count as taxonomical parameters. In a second part, we integrate this IC metric in a new parameterized multistrategy approach for measuring word semantic relatedness. This measure exploits the WordNet features such as the noun “is a” taxonomy, the nominalization relation allowing the use of verb “is a” taxonomy and the shared words (overlaps) in glosses. Our work has been evaluated and compared with related works using a wide set of benchmarks conceived for word semantic similarity/relatedness tasks. Obtained results show that our IC method and the new relatedness measure correlated better with human judgments than related works.     

A Probabilistic Scheduler for the Analysis of Cryptographic Protocols

When modelling cryto-protocols by means of process calculi which express both nondeterministic and probabilistic behavior, it is customary to view the scheduler as an intruder. It has been established that the traditional scheduler needs to be carefully calibrated in order to more accurately reflect the intruder's capabilities for controlling communication channels. We propose such a class of schedulers through a semantic variant called PPC_νσ, of the Probabilistic Poly-time Calculus (PPC) of Mitchell et al. [J.C. Mitchell, A. Ramanathan, A. Scedrov, and V. Teague. A probabilistic polynomial-time process calculus for the analysis of cryptographic protocols. Theoretical Computer Science, 353:118–164, 2006] and we illustrate the pertinence of our approach by an extensive study of the Dining Cryptographers (DCP) [David Chaum. The dining cryptographers problem: Unconditional sender and recipient untraceability. J. Cryptology, 1(1):65–75, 1988] protocol. Along these lines, we define a new characterization of Mitchell et al.'s observational equivalence [J.C. Mitchell, A. Ramanathan, A. Scedrov, and V. Teague. A probabilistic polynomial-time process calculus for the analysis of cryptographic protocols. Theoretical Computer Science, 353:118–164, 2006] more suited for taking into account any observable trace instead of just a single action as required in the analysis of the DCP.     

Taxonomy-based information content and wordnet-wiktionary-wikipedia glosses for semantic relatedness

Computing the semantic similarity/relatedness between terms is an important research area for several disciplines, including artificial intelligence, cognitive science, linguistics, psychology, biomedicine and information retrieval. These measures exploit knowledge bases to express the semantics of concepts. Some approaches, such as the information theoretical approaches, rely on knowledge structure, while others, such as the gloss-based approaches, use knowledge content. Firstly, based on structure, we propose a new intrinsic Information Content (IC) computing method which is based on the quantification of the subgraph formed by the ancestors of the target concept. Taxonomic measures including the IC-based ones consume the topological parameters that must be extracted from taxonomies considered as Directed Acyclic Graphs (DAGs). Accordingly, we propose a routine of graph algorithms that are able to provide some basic parameters, such as depth, ancestors, descendents, Lowest Common Subsumer (LCS). The IC-computing method is assessed using several knowledge structures which are: the noun and verb WordNet “is a” taxonomies, Wikipedia Category Graph (WCG), and MeSH taxonomy. We also propose an aggregation schema that exploits the WordNet “is a” taxonomy and WCG in a complementary way through the IC-based measures to improve coverage capacity. Secondly, taking content into consideration, we propose a gloss-based semantic similarity measure that operates based on the noun weighting mechanism using our IC-computing method, as well as on the WordNet, Wiktionary and Wikipedia resources. Further evaluation is performed on various items, including nouns, verbs, multiword expressions and biomedical datasets, using well-recognized benchmarks. The results indicate an improvement in terms of similarity and relatedness assessment accuracy.     

Anonymous and secure electronic transaction protocol

We present a new protocol for electronic transactions which is not only secure but also anonymous, the latter characteristic being obtained by associating an encryption device with a chip card. Security is ensured by the use of encryption, electronic signature and authentication. In order to check the validity of the security properties enforced by the protocol, a model and a specification are provided. The protocol modeling language is a process algebra with value passing extended by an observation mechanism allowing the specification of security levels, by cryptographic primitives, and by a function call feature on private channels allowing the modeling of interactions with the crypto-system. The anonymity is expressed by an information flow property. The verification method, based on cosimulation, is proved consistent and complete and analysis confirms that this approach ensures not only anonymity (thanks to the fact that the client never discloses to the merchant any information permitting his identification), but also the quasi-impossibility of any fraudulent transaction.