Sketching sonic interactions by imitation-driven sound synthesis

Sketching is at the core of every design activity. In visual design, pencil and paper are the preferred tools to produce sketches for their simplicity and immediacy. Analogue tools for sonic sketching do not exist yet, although voice and gesture are embodied abilities commonly exploited to communicate sound concepts. The EU project SkAT-VG aims to support vocal sketching with computeraided technologies that can be easily accessed, understood and controlled through vocal and gestural imitations. This imitation-driven sound synthesis approach is meant to overcome the ephemerality and timbral limitations of human voice and gesture, allowing to produce more refined sonic sketches and to think about sound in a more designerly way. This paper presents two main outcomes of the project: The Sound Design Toolkit, a palette of basic sound synthesis models grounded on ecological perception and physical description of sound-producing phenomena, and SkAT-Studio, a visual framework based on sound design workflows organized in stages of input, analysis, mapping, synthesis, and output. The integration of these two software packages provides an environment in which sound designers can go from concepts, through exploration and mocking-up, to prototyping in sonic interaction design, taking advantage of all the possibilities of- fered by vocal and gestural imitations in every step of the process

Asymmetric Feature Maps with Application to Sketch Based Retrieval

We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive a short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion, exceeding significantly the state of the art on standard benchmarks.Comment: CVPR 201

A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

Palaeoenvironmental and diagenetic reconstruction of a closed-lacustrine carbonate system - the challenging marginal setting of the Miocene Ries Crater Lake (Germany)

Chemostratigraphic studies on lacustrine sedimentary sequences provide essential insights on past cyclic climatic events, on their repetition and prediction through time. Diagenetic overprint of primary features often hinders the use of such studies for palaeoenvironmental reconstruction. Here the potential of integrated geochemical and petrographic methods is evaluated to record freshwater to saline oscillations within the ancient marginal lacustrine carbonates of the Miocene Ries Crater Lake (Germany). This area is critical because it represents the transition from shoreline to proximal domains of a hydrologically closed system, affected by recurrent emergent events, representing the boundaries of successive sedimentary cycles. Chemostratigraphy targets shifts related to subaerial exposure and/or climatic fluctuations. Methods combine facies changes with δ13C–δ18O chemostratigraphy from matrix carbonates across five closely spaced, temporally equivalent stratigraphic sections. Isotope composition of ostracod shells, gastropods and cements is provided for comparison. Cathodoluminescence and back‐scatter electron microscopy were performed to discriminate primary (syn‐)depositional, from secondary diagenetic features. Meteoric diagenesis is expressed by substantial early dissolution and dark blue luminescent sparry cements carrying negative δ13C and δ18O. Sedimentary cycles are not correlated by isotope chemostratigraphy. Both matrix δ13C and δ18O range from ca −7·5 to +4·0‰ and show clear positive covariance (R = 0·97) whose nature differs from that of previous basin‐oriented studies on the lake: negative values are here unconnected to original freshwater lacustrine conditions but reflect extensive meteoric diagenesis, while positive values probably represent primary saline lake water chemistry. Noisy geochemical curves relate to heterogeneities in (primary) porosity, resulting in selective carbonate diagenesis. This study exemplifies that ancient lacustrine carbonates, despite extensive meteoric weathering, are able to retain key information for both palaeoenvironmental reconstruction and the understanding of diagenetic processes in relation to those primary conditions. Also, it emphasizes the limitation of chemostratigraphy in fossil carbonates, and specifically in settings that are sensitive for the preservation of primary environmental signals, such as lake margins prone to meteoric diagenesis