Learning from textual data streams for detecting email spam

This master thesis introduces a method for the detecting email spam through the translation problem in incremental learning of the time series. Common spam detection systems mainly use methods of supervised learning (naive Bayesian classifier, decision trees), while in the master’s thesis presents the classification by using the methods of data stream mining. For learning sets, we also choose the attributes that do not contain personal data and which are not required to obtain the consent of the sender or the recipient (attributes consist the envelope part of e-mail). With the help of algorithms for learning from data streams (VFDT, cVFDT) we used the electronic sequence of messages as text data stream. The results were compared with the traditional spam detection methods and they show that traditional spam detection methods have higher accuracy compared to algorithms for learning from data stream and therefore are not suitable for detecting email spam

Learning from textual data streams for detecting email spam

This master thesis introduces a method for the detecting email spam through the translation problem in incremental learning of the time series. Common spam detection systems mainly use methods of supervised learning (naive Bayesian classifier, decision trees), while in the master’s thesis presents the classification by using the methods of data stream mining. For learning sets, we also choose the attributes that do not contain personal data and which are not required to obtain the consent of the sender or the recipient (attributes consist the envelope part of e-mail). With the help of algorithms for learning from data streams (VFDT, cVFDT) we used the electronic sequence of messages as text data stream. The results were compared with the traditional spam detection methods and they show that traditional spam detection methods have higher accuracy compared to algorithms for learning from data stream and therefore are not suitable for detecting email spam

Effect of NLP proteins on yeasts

Proteine iz družine NLP (Nep1-like proteins) najdemo v raznolikih, taksonomsko nesorodnih mikroorganizmih (glivah, oomicetah, bakterijah). Takšni mikroorganizmi so zelo razširjeni in lahko okužijo različne pridelke, predvsem dvokaličnice, kot so krompir, paradižnik, soja, tobak in hmelj. Nekateri proteini NLP so citotoksični za dvokaličnice saj povzročijo nekrozo tkiva in aktivirajo imunski odziv rastline. Poleg citotoksičnih proteinov pa obstajajo tudi proteini NLP, ki niso citotoksični. Proteini NLP so po strukturi podobni aktinoporinom, toksinom, ki tvorijo pore v celično membranoin se prav tako kot aktinoporini vežejo na sfingolipide. Za proučevanje interakcije med proteini NLP in sfingolipidi smo izbrali kvasovko Saccharomyces cerevisiae, ki je enostaven in dobro poznan modelni organizem ter hkrati vsebuje sfingolipide, ki so v precejšnji meri podobni rastlinskim sfingolipidom. Ugotovili smo, da se citotoksični protein NLPPya veže na kvasne protoplaste močneje in je tudi bolj toksičen za kvasovke od proteina HaNLP3, ki ni cititoksičen za rastline. Za proučevanje vezave proteinov NLP na kvasne sfingolipide smo uporabili dva mutirana seva kvasovke, ki imata okvarjeno manozilacijo sfingolipidov. Ugotovili smo, da se največ proteina veže na sev BY4742 csg2Δ, ki nima manoziliranih sfingolipidov, ima pa povečano količino inozitol-fosforil ceramidov.Proteins from the NLP family (Nep1-like proteins) are found in a variety of taxonomically unrelated microorganisms (fungi, oomycetes, bacteria). Such microorganisms are widespread and can infect different crop plants, especially dicotyledons such as potatoe, tomatoe, soybean, tobacco and hop. Some NLP proteins are cytotoxic to dicotyledonous plants, and cause tissue necrosis and induce immune response of the plant. In addition to cytotoxic proteins, there are also NLP proteins that are not cytotoxic. NLP proteins are structurally similar to actinoporins, toxins that form pores into the cell membrane. NLP proteins bind to sphingolipids, just like actinoporins. The yeast Saccharomyces cerevisiae, which is a simple and well-known model organism and contains sphingolipids that are similar to plant sphingolipids, has been selected for the study of the interaction between NLP proteins and sphingolipids. We found that the cytotoxic protein NLPPya binds to the yeast protoplasts more strongly and is also more toxic to yeasts than the protein HaNLP3, which is not cytotoxic to plants. To examine the binding of NLP proteins to yeast sphingolipids we used two mutants with impaired sphingolipid mannosylation, and compared them to the wild type cells. We found that most proteins bind to the BY4742 csg2Δ strain which does not have manosylated sphingolipids, but has an increased amount of ceramides and inositol-phosphoryl ceramides

Majda Merše: slovenistična bibliografija 1979‒2019

Bibliografija Majde Merše za čas do leta 1995 je bila objavljena v knjigah Biografije in bibliografije raziskovalcev Znanstvenoraziskovalnega centra SAZU (2: 1976–1985, Ljubljana: SAZU, 1988, 26; 3: 1986–1995, Ljubljana: Založba ZRC, ZRC SAZU, 1998, 44–45). Tukajšnja, izpopolnjena bibliografija zajema avtoričino slovenistično, predvsem jezikoslovno delo in objave o njej. Objavljeni povzetki niso upoštevani, če je delo, na katero se nanašajo, izšlo, predavanje kot govorni nastop pa je upoštevano le, če ni bilo objavljeno ali predstavljeno v objavljenem povzetku. Elektronske objave prvotno natisnjenih del tu niso navedene, dosegljive pa so v vedno znova osveženi bibliografiji na spletnem naslovu https://bib.cobiss.net/bibliographies/si/webBiblio/bib201_20191118_150939_06500.html (16. 12. 2019). – Za osnovo so bili uporabljeni podatki iz Cobissa

Vlado Nartnik: bibliografija 1967–2011

Bibliografija Vlada (Vladimirja) Nartnika za čas do leta 1995 je bila objavljena v knjigi Biografije in bibliografije raziskovalcev Znanstvenoraziskovalnega centra SAZU 3: 1986–1995 (Ljubljana: Založba ZRC, ZRC SAZU, 1998, 48–53). Tukajšnja bibliografija zajema njegovo objavljeno delo in objave o njem. Objavljeni povzetki niso upoštevani, če je delo, na katero se nanašajo, izšlo, predavanje kot govorni nastop pa je upoštevano le, če ni bilo objavljeno ali predstavljeno v objavljenem povzetku. Elektronske objave prvotno natisnjenih del tu niso navedene, lahko pa so dosegljive v vedno znova osveženi bibliografiji na spletnem naslovu http://splet02.izum.si/cobiss/bibliography?code=06331. – Za osnovo so bili uporabljeni podatki iz Cobissa, ki jih je pripravila naša sodelavka Mojca Uran iz Biblioteke SAZU v Ljubljani