Further exploration of the Dendritic Cell Algorithm: antigen multiplier and time windows

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performance in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DCA, including antigen multiplier and moving time windows, are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with contant-sized detectors is not applicable to the data set. And the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.

Análise de distúrbios de tensão em sistemas de distribuição de energia elétrica baseada em sistemas imunológicos artificiais

Pós-graduação em Engenharia Elétrica - FEIS

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Monitoramento e identificação de falhas em estruturas aeronáuticas e mecânicas utilizando técnicas de computação inteligente

Pós-graduação em Engenharia Mecânica - FEIS

Diagnóstico de distúrbios de tensão em sistemas de distribuição de energia elétrica usando um algoritmo imuno-neural

Pós-graduação em Engenharia Elétrica - FEIS

Aplicação de sistemas imunológicos artificiais para biometria facial: Reconhecimento de identidade baseado nas características de padrões binários

Pós-graduação em Engenharia Elétrica - FEIS

Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

Positive and negative mutant selection in the human histone hairpin-binding protein using the yeast three-hybrid system.

We have used the yeast three-hybrid system in a positive selection for mutants of the human histone hairpin-binding protein (HBP) capable of interacting with non-canonical hairpins and in a negative selection for loss-of-binding mutants. Interestingly, all mutations from the positive selection are located in the N- and C-terminal regions flanking a minimal RNA-binding domain (RBD) previously defined between amino acids 126 and 198. Further, in vitro binding studies demonstrate that the RBD, which shows no obvious similarity to other RNA-binding motifs, has a relaxed sequence specificity compared to full-length HBP, allowing it to bind to mutant hairpin RNAs not normally found in histone genes. These findings indicate that the sequences flanking the RBD are important for restricting binding to the highly conserved histone hairpin structure. Among the loss-of-binding mutations, about half are nonsense mutations distributed throughout the N-terminal part and the RBD whereas the other half are missense mutations restricted to the RBD. Whereas the nonsense mutations permit a more precise definition of the C-terminal border of the RBD, the missense mutations identify critical residues for RNA binding within the RBD.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.

Low Complexity Intra Mode Selection for Efficient Distributed Video Coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Wealth constraints, skill prices or networks: what determines emigrant selection?

The productive characteristics of migrating individuals, emigrant selection, affect welfare. The empirical estimation of the degree of selection suffers from a lack of complete and nationally representative data. This paper uses a new and better dataset to address both issues: the ENET (Mexican Labor Survey), which identifies emigrants right before they leave and allows a direct comparison to non-migrants. This dataset presents a relevant dichotomy: it shows on average negative selection for Mexican emigrants to the United States for the period 2000-2004 together with positive selection in Mexican emigration out of rural Mexico to the United States in the same period. Three theories that could explain this dichotomy are tested. Whereas higher skill prices in Mexico than in the US are enough to explain negative selection in urban Mexico, its combination with network effects and wealth constraints is required to account for positive selection in rural Mexico.

New evidence on emigrant selection

This paper examines the extent to which Mexican emigrants to the United States are negatively selected, that is, have lower skills than individuals who remain in Mexico. Previous studies have been limited by the lack of nationally representative longitudinal data. This one uses a newly available household survey, which identifies emigrants before they leave and allows a direct comparison to non-migrants. I find that, on average, US bound Mexican emigrants from 2000 to 2004 earn a lower wage and have less schooling years than individuals who remain in Mexico, evidence of negative selection. This supports the original hypothesis of Borjas (AER, 1987) and argues against recent findings, notably those of Chiquiar and Hanson (JPE, 2005). The discrepancy with the latter is primarily due to an under-count of unskilled migrants in US sources and secondarily to the omission of unobservables in their methodology.

A critical role for the T cell receptor alpha-chain connecting peptide domain in positive selection of CD1-independent NKT cells.

Natural killer T (NKT) cells are a subset of mature alpha beta TCR(+) cells that co-express NK lineage markers. Whereas most NKT cells express a canonical Valpha14/Vbeta8.2 TCR and are selected by CD1d, a minority of NKT cells express a diverse TCR repertoire and develop independently of CD1d. Little is known about the selection requirements of CD1d-independent NKT cells. We show here that NKT cells develop in RAG-deficient mice expressing an MHC class II-restricted transgenic TCR (Valpha2/Vbeta8.1) but only under conditions that lead to negative selection of conventional T cells. Moreover development of NKT cells in these mice is absolutely dependent upon an intact TCR alpha-chain connecting peptide domain, which is required for positive selection of conventional T cells via recruitment of the ERK signaling pathway. Collectively our data demonstrate that NKT cells can develop as a result of high avidity TCR/MHC class II interactions and suggest that common signaling pathways are involved in the positive selection of CD1d-independent NKT cells and conventional T cells.

Promoter IV of the class II transactivator gene is essential for positive selection of CD4+ T cells.

Major histocompatibility complex class II (MHCII) expression is regulated by the transcriptional coactivator CIITA. Positive selection of CD4(+) T cells is abrogated in mice lacking one of the promoters (pIV) of the Mhc2ta gene. This is entirely due to the absence of MHCII expression in thymic epithelia, as demonstrated by bone marrow transfer experiments between wild-type and pIV(-/-) mice. Medullary thymic epithelial cells (mTECs) are also MHCII(-) in pIV(-/-) mice. Bone marrow-derived, professional antigen-presenting cells (APCs) retain normal MHCII expression in pIV(-/-) mice, including those believed to mediate negative selection in the thymic medulla. Endogenous retroviruses thus retain their ability to sustain negative selection of the residual CD4(+) thymocytes in pIV(-/-) mice. Interestingly, the passive acquisition of MHCII molecules by thymocytes is abrogated in pIV(-/-) mice. This identifies thymic epithelial cells as the source of this passive transfer. In peripheral lymphoid organs, the CD4(+) T-cell population of pIV(-/-) mice is quantitatively and qualitatively comparable to that of MHCII-deficient mice. It comprises a high proportion of CD1-restricted natural killer T cells, which results in a bias of the V beta repertoire of the residual CD4(+) T-cell population. We have also addressed the identity of the signal that sustains pIV expression in cortical epithelia. We found that the Jak/STAT pathways activated by the common gamma chain (CD132) or common beta chain (CDw131) cytokine receptors are not required for MHCII expression in thymic cortical epithelia.