Remote sensing and GIS-based analysis of cave development in the Suoimuoi Catchment (Son La - NW Vietnam)

Integration of remotely sensed imagery with ground surveys is a promising method in cave development studies. In this research a methodology was set up in which a variety of remote sensing and GIS techniques support cave analysis in the tropical karst area of the Suoimuoi catchment, NW Vietnam. In order to extract the maximum information from different remotely sensed data, the hue invariant IHS transformation was applied to integrate Landsat multispectral channels with the high resolution Landsat 7 ETM panchromatic channel. The resulting fused image was used, after enhancement, to visually and digitally extract lineaments. Aerial photos evaluated the extracted lineaments. Based on lineament density indices a fracture zone favorable for cave development is defined. The distance between caves and faults was investigated as well as the correspondence between the cave occurrence and the fracture zone

A Deep Learning Approach to Network Intrusion Detection

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments

Algebraic varieties with automorphism groups of maximal rank

We confirm, to some extent, the belief that a projective variety X has the largest number (relative to the dimension of X) of independent commuting automorphisms of positive entropy only when X is birational to a complex torus or a quotient of a torus. We also include an addendum to an early paper though it is not used in the present paper.Comment: Mathematische Annalen (to appear

In Vitro Corrosion Properties of Mg Matrix In Situ Composites Fabricated by Spark Plasma Sintering

Mg matrix in situ composites were fabricated from Mg and ZnO powder by a spark plasma sintering method. The composition and microstructure of the sintered samples were characterized. Corrosion properties of fabricated composites were evaluated by immersion and by electrochemical tests using Hanks’ solution. The results showed that the formation of in situ products improved significantly the corrosion resistance of the fabricated composites compared with pure Mg; Mg-10 wt % ZnO composites especially exhibited the lowest corrosion rate. In addition, an energy-dispersive X-ray (EDX) analysis showed that calcium phosphate formed as a corrosion product on the surface of Mg-10 wt % ZnO composites, while Mg(OH)_2 appeared as a corrosion product on the surface of Mg-20 wt % ZnO composite. The findings suggested Mg-10 wt % ZnO composite as a potential candidate for temporary implant application

Good things come in small packages? : Ekenäsregionens kommunsammanslagningar ur ett valdemokratiskt perspektiv

Endast sammandrag. Inbundna avhandlingar kan sökas i Helka-databasen (http://www.helsinki.fi/helka). Elektroniska kopior av avhandlingar finns antingen öppet på nätet eller endast tillgängliga i bibliotekets avhandlingsterminaler.Only abstract. Paper copies of master’s theses are listed in the Helka database (http://www.helsinki.fi/helka). Electronic copies of master’s theses are either available as open access or only on thesis terminals in the Helsinki University Library.Vain tiivistelmä. Sidottujen gradujen saatavuuden voit tarkistaa Helka-tietokannasta (http://www.helsinki.fi/helka). Digitaaliset gradut voivat olla luettavissa avoimesti verkossa tai rajoitetusti kirjaston opinnäytekioskeilla.I arbetet presenteras de kommunsammanslagningar som skedde i Ekenäsregionen åren 1977 och 1993. Syftet är att ta reda på om kommuninvånarnas förutsättningar att delta i den politiska processen förändrats i och med sammanslagningarna. I arbetet används 7 demokratiindikatorer för att kartlägga kommunernas demokratiska "klimat" och de eventuella förändringar som skedde i samband med sammanslagningarna. Indikatorerna är bl.a. förändringar i valdeltagande, i antalet förtroendeuppdrag, i social representativitet och i geografiska avstånd. Det empiriska materialet baserar sig på kommunernas val- och mötesprotokoll, verksamhetsberättelser samt annat skriftligt material om de två reformerna och som finns i Ekenäs stads arkiv. Förutom dessa har även använts intervjumaterial från Radio Vega Västnyland i Ekenäs. Baserat på det data som funnits har de två sammanslagningarna haft fler negativa än positiva effekter på invånarnas förutsättningar att vara en del av det representativa systemet i kommunen. Sambandet mellan kommunstorlek och fungerande lokal demokrati beror dock mycket på vilken form av aktivitet som granskas. Till exempel visar det empiriska materialet att ökad kommunstorlek och större invånarantal i allmänhet erbjuder bättre förutsättningar för olika politiska alternativ att uppstå samtidigt som den sociala representativiteten försämras. Genom att sätta fingret på dylika effekter kan man förhoppningsvis ta fram lösningar som motverkar de negativa effekterna

Deep Learning Combined with De - noising Data for Network Intrusion Detection

Anomaly-based Network Intrusion Detection Systems (NIDSs) are a common security defense for modern networks. The success of their operation depends upon vast quantities of training data. However, one major limitation is the inability of NIDS to be reliably trained using imbalanced datasets. Network observations are naturally imbalanced, yet without substantial data pre-processing, NIDS accuracy can be significantly reduced. With the diversity and dynamicity of modern network traffic, there are concerns that the current reliance upon un-natural balanced datasets cannot remain feasible in modern networks. This paper details our de-noising method, which when combined with deep learning techniques can address these concerns and offer accuracy improvements of between 1.5% and 4.5%. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs