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Nowadays, the ethernet is developing much faster than memory and CPU technologies, protocol processing has become the bottleneck of TCP performance on end systems. Modern NICs usually support offload techniques such as checksum offload and TCP Segmentation Offload(TSO), allowing the end system to offload some processing work onto the NIC hardware. In this paper, we propose an implementation of Large...
Lots of studies have shown that memory hardware error rates are orders of magnitude higher than previously reported. In order to fight with these memory hardware errors, many memory testing tools have been developed, especially software level online memory testers, which means these memory testers implemented in software can work with the OS (operating system) at the same time. However, validation...
Most computer programs are written using either or both 32- and 64-bit floating-point formats (FP32 and FP64). To save memory space, improve the computation speed, and improve energy efficiency by eliminating waste bit data in floating-point data, we need reduced-precision formats that can represent various and lower floating-point precisions. This paper proposes the implementation of reduced-precision...
The chunk fragmentation problem inherently associated with deduplication systems significantly slows down the restore performance, as it causes the restore process to assemble chunks which are distributed in a large number of containers as a result of storage indirection. Existing solutions attempting to address the fragmentation problem either sacrifice deduplication efficiency or require additional...
Sparse Matrix-Vector multiplication (SpMV) is a computational kernel widely used in many applications. There are many different implementations using different processors and algorithms for SpMV. The performances of different SpMV implementations are quite different, and it is basically difficult to choose the implementation that has the best performance for a given sparse matrix and a given platform...
The imbalanced learning problem is becoming pervasive in today's data mining applications. This problem refers to the uneven distribution of instances among the classes which poses difficulty in the classification of rare instances. Several undersampling as well as oversampling methods were proposed to deal with such imbalance. Many undersampling techniques do not consider distribution of information...
Although fuzzy c-means algorithm has shown great capability to spherical clusters, it can not perform very well on non-spherical data sets yet. To deal with this problem, kernel-based fuzzy clustering has been presented by mapping data points into a high-dimensional Hilbert space with kernel functions. However, the computational complexity of kernel matrix is always quadratic, usually makes kernel...
The aim of this presentation is to show how various ideas coming from the nonlinear stability theory of functional differential systems, stochastic modeling, and machine learning, can be put together in order to create an approximating model that explains the working mechanisms behind a certain type of reservoir computers. Reservoir computing is a recently introduced brain-inspired machine learning...
Unmanned aerial vehicles (UAV), also referred to as drones, are a growing field in computer science with applications in military systems, delivery services, emergency relief and evacuation. One of the primary obstructions to the allowance of UAV journeys over populated areas is the lack of sophisticated automated systems that detect drone landing sites. In this paper, we propose a landing area detection...
Security is a prime concern in today's era of technology when dealing with digital data. All the information is managed by the file system which is the core layer of security in an Operating System. Due to lack of security at this layer, private information can be accessed by an intruder or in case of theft data can be read via mounting it on to a mount point and accessing the information. Other layer...
Linear spectral unmixing consists on the identification of spectrally pure constituents, called endmembers and their corresponding proportions or abundances using a linear model. Traditionally, most of the attention has been focussed on the exploitation of spectral information when identifying a set of endmembers and, only recently, some techniques try to take advantage of complementary information...
Locality-based feature learning has drawn more and more attentions recently. However, most of locality-based feature learning methods only consider a kind of local neighbor information, and such the locality-based methods are difficult to well reveal intrinsic geometrical structure of raw high-dimensional data. In this paper, we propose a novel multi-locality correlation feature learning algorithm...
Android applications (apps) generate a consistent amount of data traffic. A noticeable share of this generated data traffic is used to convey third party advertisement, or to collect information about the user and its phone, generally with the target of profiling users. Such a traffic is not needed to the correct app execution and can be considered unwanted overhead. In this paper we propose Data-Sluice,...
Research funding bodies strongly encourage research projects to disseminate discovered knowledge and transfer developed technology to industry. Unfortunately, capturing, sharing, reproducing and building upon experimental results has become close to impossible in computer systems' R&D. The main challenges include the ever changing hardware and software technologies, lack of standard experimental...
Reversible data hiding refers to embedding secret message into a cover and the cover image can be recovered exactly. In the texture synthesis approach texture image which is smaller in size is resampled in order to form a texture image which is comparatively of larger size but similar to the original one. It helps in concealing the secret information in the image. Patch based texture synthesis pattern...
This paper proposes a new technique for simultaneous estimation of missing values using Weighted kNN approach whose weight is computed by the product of Grey relational grade and weight vector of SVM. A mixture kernel is used which is a hybridization of Polynomial and Radial Basis Kernel. Applying this proposed technique to fisher iris dataset, we find that the mixture kernel gives better result....
Keylogger is a specific type of spywares, that attempts to steal user information, by keep tracking user keyboard, and log every keystroke in a log file; to be used by a third party. Keylogger is one of the most serious problems which blustering information security in this era. And it still considered an open problem. Most of the keylogger softwares available, intercept the key after it has been...
We developed a high-performance low-ordered unstructured finite-element solver for disaster mitigation simulations, and applied it to seismic ground strain analysis of Tokyo. Here we combined high performance computing methods for making a fast solver scalable up to the full K computer with 663,552 CPU cores. We also developed methods for high-performance matrix-vector computation using SIMD units...
The blur removal due to camera shake and motion of the scene is been considered as a research topic in Image processing. To build a more sharper and reduced blurred image, an algorithm can be selected which is capable of grouping a burst of images (or more than one burst for high dynamic range), from that less blurred image from each frame can be preferred through a weighted average in the Fourier...
Spatio - temporal methods is the process of innovations and finding the patterns from the knowledge representations through outliers. This kind of data representing the (i) the states of an object (ii) position or event in space at a particular period of time. It refers to the Objects whose attribute values are entirely different from its neighbourhood. Always their locations are different even the...
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