Selection of intrusion detection system threshold bounds for effective sensor fusion

The motivation behind the fusion of Intrusion Detection Systems was the realization that with the increasing traffic and increasing complexity of attacks, none of the present day stand-alone Intrusion Detection Systems can meet the high demand for a very high detection rate and an extremely low false positive rate. Multi-sensor fusion can be used to meet these requirements by a refinement of the combined response of different Intrusion Detection Systems. In this paper, we show the design technique of sensor fusion to best utilize the useful response from multiple sensors by an appropriate adjustment of the fusion threshold. The threshold is generally chosen according to the past experiences or by an expert system. In this paper, we show that the choice of the threshold bounds according to the Chebyshev inequality principle performs better. This approach also helps to solve the problem of scalability and has the advantage of failsafe capability. This paper theoretically models the fusion of Intrusion Detection Systems for the purpose of proving the improvement in performance, supplemented with the empirical evaluation. The combination of complementary sensors is shown to detect more attacks than the individual components. Since the individual sensors chosen detect sufficiently different attacks, their result can be merged for improved performance. The combination is done in different ways like (i) taking all the alarms from each system and avoiding duplications, (ii) taking alarms from each system by fixing threshold bounds, and (iii) rule-based fusion with a priori knowledge of the individual sensor performance. A number of evaluation metrics are used, and the results indicate that there is an overall enhancement in the performance of the combined detector using sensor fusion incorporating the threshold bounds and significantly better performance using simple rule-based fusion.

The emerging world of small ORFs

Small open reading frames (sORFs) are an often overlooked feature of plant genomes. Initially found in plant viral RNAs and considered an interesting curiosity, an increasing number of these sORFs have been shown to encode functional peptides or play a regulatory role. The recent discovery that many of these sORFs initiate with start codons other than AUG, together with the identification of functional small peptides encoded in supposedly noncoding primary miRNA transcripts (pri-miRs), has drastically increased the number of potentially functional sORFs within the genome. Here we review how advances in technology, notably ribosome profiling (RP) assays, are complementing bioinformatics and proteogenomic methods to provide powerful ways to identify these elusive features of plant genomes, and highlight the regulatory roles sORFs can play.

Functional role of the Mso1p-Sec1p complex in membrane fusion regulation

Sec1/Munc18 (SM) protein family members are evolutionary conserved proteins. They perform an essential, albeit poorly understood function in SNARE complex formation in membrane fusion. In addition to the SNARE complex components, only a few SM protein binding proteins are known. Typically, their binding modes to SM proteins and their contribution to the membrane fusion regulation is poorly characterised. We identified Mso1p as a novel Sec1p interacting partner. It was shown that Mso1p and Sec1p interact at sites of polarised secretion and that this localisation is dependent on the Rab GTPase Sec4p and its GEF Sec2p. Using targeted mutagenesis and N- and C-terminal deletants, it was discovered that the interaction between an N-terminal peptide of Mso1p and the putative Syntaxin N-peptide binding area in Sec1p domain 1 is important for membrane fusion regulation. The yeast Syntaxin homologues Sso1p and Sso2p lack the N-terminal peptide. Our results show that in addition to binding to the putative N-peptide binding area in Sec1p, Mso1p can interact with Sso1p and Sso2p. This result suggests that Mso1p can mimic the N-peptide binding to facilitate membrane fusion. In addition to Mso1p, a novel role in membrane fusion regulation was revealed for the Sec1p C-terminal tail, which is missing in its mammalian homologues. Deletion of the Sec1p-tail results in temperature sensitive growth and reduced sporulation. Using in vivo and in vitro experiments, it was shown that the Sec1p-tail mediates SNARE complex binding and assembly. These results propose a regulatory role for the Sec1p-tail in SNARE complex formation. Furthermore, two novel interaction partners for Mso1p, the Rab GTPase Sec4p and plasma membrane phospholipids, were identified. The Sec4p link was identified using Bimolecular Fluorescence Complementation assays with Mso1p and the non-SNARE binding Sec1p(1-657). The assay revealed that Mso1p can target Sec1p(1-657) to sites of secretion. This effect is mediated via the Mso1p C-terminus, which previously has been genetically linked to Sec4p. These results and in vitro binding experiments suggest that Mso1p acts in cooperation with the GTP-bound form of Sec4p on vesicle-like structures prior to membrane fusion. Mso1p shares homology with the PIP2 binding domain of the mammalian Munc18 binding Mint proteins. It was shown both in vivo and in vitro that Mso1p is a phospholipid inserting protein and that this insertion is mediated by the conserved Mso1p amino terminus. In vivo, the Mso1p phospholipid binding is needed for sporulation and Mso1p-Sec1p localisation at the sites of secretion at the plasma membrane. The results reveal a novel layer of membrane fusion regulation in exocytosis and propose a coordinating role for Mso1p in connection with membrane lipids, Sec1p, Sec4p and SNARE complexes in this process.

Data fusion and matching by maximizing statistical dependencies

The core aim of machine learning is to make a computer program learn from the experience. Learning from data is usually defined as a task of learning regularities or patterns in data in order to extract useful information, or to learn the underlying concept. An important sub-field of machine learning is called multi-view learning where the task is to learn from multiple data sets or views describing the same underlying concept. A typical example of such scenario would be to study a biological concept using several biological measurements like gene expression, protein expression and metabolic profiles, or to classify web pages based on their content and the contents of their hyperlinks. In this thesis, novel problem formulations and methods for multi-view learning are presented. The contributions include a linear data fusion approach during exploratory data analysis, a new measure to evaluate different kinds of representations for textual data, and an extension of multi-view learning for novel scenarios where the correspondence of samples in the different views or data sets is not known in advance. In order to infer the one-to-one correspondence of samples between two views, a novel concept of multi-view matching is proposed. The matching algorithm is completely data-driven and is demonstrated in several applications such as matching of metabolites between humans and mice, and matching of sentences between documents in two languages.

Music in the recovering brain

Listening to music involves a widely distributed bilateral network of brain regions that controls many auditory perceptual, cognitive, emotional, and motor functions. Exposure to music can also temporarily improve mood, reduce stress, and enhance cognitive performance as well as promote neural plasticity. However, very little is currently known about the relationship between music perception and auditory and cognitive processes or about the potential therapeutic effects of listening to music after neural damage. This thesis explores the interplay of auditory, cognitive, and emotional factors related to music processing after a middle cerebral artery (MCA) stroke. In the acute recovery phase, 60 MCA stroke patients were randomly assigned to a music listening group, an audio book listening group, or a control group. All patients underwent neuropsychological assessments, magnetoencephalography (MEG) measurements, and magnetic resonance imaging (MRI) scans repeatedly during a six-month post-stroke period. The results revealed that amusia, a deficit of music perception, is a common and persistent deficit after a stroke, especially if the stroke affects the frontal and temporal brain areas in the right hemisphere. Amusia is clearly associated with deficits in both auditory encoding, as indicated by the magnetic mismatch negativity (MMNm) response, and domain-general cognitive processes, such as attention, working memory, and executive functions. Furthermore, both music and audio book listening increased the MMNm, whereas only music listening improved the recovery of verbal memory and focused attention as well as prevented a depressed and confused mood during the first post-stroke months. These findings indicate a close link between musical, auditory, and cognitive processes in the brain. Importantly, they also encourage the use of listening to music as a rehabilitative leisure activity after a stroke and suggest that the auditory environment can induce long-term plastic changes in the recovering brain.

The Ecologic Foundations of Stylistics in Music and in Language

This contribution suggests that it is possible to describe the transformations of musical style in an analogous way to the transformations of style in language, and also that it can be explained how the ‘musics in contact’ behave in an analogous way to the ‘languages in contact’. According to this idea, the ‘evolution’ of styles in music and in language can be identified and studied as dynamic exchanges in ecological niches. It is suggested, also, that the idiolectic-ecolectic, and acrolectic-basilectic relationships in music and language are functions of cycles in several ‘layers’ and ‘rhythms’. The presence of stylistic varieties and influences in music and in language may imply that they are part of major sign systems within a more complex ecological relationship.

The Influence of World Factors on Finnish Stock Market Volatility

This paper investigates to what extent the volatility of Finnish stock portfolios is transmitted through the "world volatility". We operationalize the volatility processes of Finnish leverage, industry, and size portfolio returns by asymmetric GARCH specifications according to Glosten et al. (1993). We use daily return data for January, 2, 1987 to December 30, 1998. We find that the world shock significantly enters the domestic models, and that the impact has increased over time. This applies also for the variance ratios, and the correlations to the world. The larger the firm, the larger is the world impact. The conditional variance is higher during recessions. The asymmetry parameter is surprisingly non-significant, and the leverage hypothesis cannot be verified. The return generating process of the domestic portfolio returns does usually not include the world information set, thus indicating that the returns are generated by a segmented conditional asset pricing model.

A speech-music discriminator using HILN model based features

We propose a simple speech music discriminator that uses features based on HILN(Harmonics, Individual Lines and Noise) model. We have been able to test the strength of the feature set on a standard database of 66 files and get an accuracy of around 97%. We also have tested on sung queries and polyphonic music and have got very good results. The current algorithm is being used to discriminate between sung queries and played (using an instrument like flute) queries for a Query by Humming(QBH) system currently under development in the lab.

Phylogenetics and biogeography of a spectacular Old World radiation of butterflies: the subtribe Mycalesina (Lepidoptera: Nymphalidae: Satyrini)

Background: Butterflies of the subtribe Mycalesina (Nymphalidae: Satyrinae) are important model organisms in ecology and evolution. This group has radiated spectacularly in the Old World tropics and presents an exciting opportunity to better understand processes of invertebrate rapid radiations. However, the generic-level taxonomy of the subtribe has been in a constant state of flux, and relationships among genera are unknown. There are six currently recognized genera in the group. Mycalesis, Lohora and Nirvanopsis are found in the Oriental region, the first of which is the most speciose genus among mycalesines, and extends into the Australasian region. Hallelesis and Bicyclus are found in mainland Africa, while Heteropsis is primarily Madagascan, with a few species in Africa. We infer the phylogeny of the group with data from three genes (total of 3139 bp) and use these data to reconstruct events in the biogeographic history of the group.,Results: The results indicate that the group Mycalesina radiated rapidly around the Oligocene-Miocene boundary. Basal relationships are unresolved, but we recover six well-supported clades. Some species of Mycalesis are nested within a primarily Madagascan clade of Heteropsis,while Nirvanopsis is nested within Lohora. The phylogeny suggests that the group had its origin either in Asia or Africa, and diversified through dispersals between the two regions, during the late Oligocene and early Miocene. The current dataset tentatively suggests that the Madagascan fauna comprises two independent radiations. The Australasian radiation shares a common ancestor derived from Asia. We discuss factors that are likely to have played a key role in the diversification of the group. Conclusions: We propose a significantly revised classification scheme for Mycalesina. We conclude that the group originated and radiated from an ancestor that was found either in Asia or Africa, with dispersals between the two regions and to Australasia. Our phylogeny paves the way for further comparative studies on this group that will help us understand the processes underlying diversification in rapid radiations of invertebrates.