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The security of image during its communication over public channel got extreme concern with the sharp rise of sharing of information nowadays, especially in this COVID-19 pandemic period. Visual cryptography scheme (VCS), which is already familiar image encryption scheme due to its simplicity, finds its applicability in real-life field. A secure image transmission system based on XOR-based VCS is proposed in this paper, which utilizes bit-plane slicing and run-length encoding to encode noise-like shares in a compressed and numeric form with two objectives – to reduce the transmission overhead and to secure the communication of the shares, at the same time. [ABSTRACT FROM AUTHOR]

The Shewhart and exponentially weighted moving average (EWMA) charts for nonconformities are the most widely used procedures of choice for monitoring Poisson observations in modern industries. Individually, the Shewhart EWMA charts are only sensitive to large and small shifts, respectively. To enhance the detection abilities of the two schemes in monitoring all kinds of shifts in Poisson count data, this study examines the performance of combined applications of the Shewhart, and EWMA Poisson control charts. Furthermore, the study proposes modifications based on well-structured statistical data collection technique, ranked set sampling (RSS), to detect shifts in the mean of a Poisson process more quickly. The relative performance of the proposed Shewhart-EWMA Poisson location charts is evaluated in terms of the average run length (ARL), standard deviation of the run length (SDRL), median run length (MRL), average ratio ARL (ARARL), average extra quadratic loss (AEQL) and performance comparison index (PCI). Consequently, all the new Poisson control charts based on RSS method are generally more superior than most of the existing schemes for monitoring Poisson processes. The use of these combined Shewhart-EWMA Poisson charts is illustrated with an example to demonstrate the practical implementation of the design procedure. [ABSTRACT FROM AUTHOR]

This paper presented a new run-length encoding based method of extracting features from multi-target image. The algorithm uses linked list structure to store data. The data are comprised of three parts, the message in run length, the equal values and the features of the image, which are in one-to-one mapping. While scanning, we only record the features of the image according to the label. After scanning the last pixel, we merge the linked list which contains the features of image according to the equal list. Compared with other approaches, the proposed one is faster and simpler, especially it can label the binary image exactly, no matter how complicated the image is. [ABSTRACT FROM PUBLISHER]

Page-based software Distributed Shared Memory (sDSM) systems suffer from their high memory consistency costs. Utilizing an effective prefetch technique can reduce this overhead. However, it is hard to predict accurately for applications exhibiting dynamic memory accessing and paging behavior. In this paper, we use Intel Cluster OpenMP (CLOMP) to study this problem. First, we present a stride augmented run-length encoding (sRLE) method to reconstruct series of numbers into 2D rectangles which facilitates a more accurate paging behavior analysis. Historical page miss records of OpenMP parallel and sequential regions are reconstructed and compressed by sRLE. Second, we design and implement a dynamic page prefetch technique (DReP) based on these reconstructed records to predict and issue prefetches. DReP and its implementation are evaluated through simulations and experiments. The simulation results show that DReP significantly improves the efficiency (~34%) and coverage (~47%) of existing prefetch techniques. Moreover, the experimental results show that DReP significantly reduces the memory consistency costs of CLOMP by 86% for extreme false sharing scenario. With the assistance of sRLE, DReP reduces ~45% and ~38% memory consistency costs for LINPACK and NPB-OMP benchmarks on GigE and DDR IB networks respectively. An detailed breakdown analysis shows that the introduced software overhead of DReP is negligible (~2%). [ABSTRACT FROM PUBLISHER]

In data warehousing it is common for users to want 20-40 columns of data to be searchable. In database marketing, it can be 100's of attributes. Each query to mine the data may use a different combination of criteria. In this article, run-length encoding transforms the record space of all k key attributes into a one-dimensional run-length array. Such a compression scheme is proven to be compatible wit h common database access operations. As a result, in a bitmap space of k attributes, just k one-dimensional attribute arrays plus a one-dimensional run-length array need to be maintained. Physical database records do not exist as a whole because they are resolved after being input to the system and absorbed by those k+1 one-dimensional arrays. The storage overhead has been greatly reduced due to the limited size of those one-dimensional arrays: the size of the attribute array is equal to the cardinality of its attribute domain; the size of the run-length array is equal to, in the worst case, or very likely much smaller than the total number of the database records due to the probability of "1"s appearing next to each other in the bitmap space. A fully specified query retrieves a single record in at most two disk-block accesses and potentially at much faster speed as all those k+1 one-dimensional arrays can easily reside in the main memory. The experimental simulation confirms that the search, insertion and deletion operations can be executed efficiently in a compressed bitmap. [ABSTRACT FROM AUTHOR]