We propose a new chaotic map combined with delay and cascade, called tent delay-sine cascade with logistic map (TDSCL). Compared with the original one-dimensional simple map, the proposed map has increased initial value sensitivity and internal randomness and a larger chaotic parameter interval. The chaotic sequence generated by TDSCL has pseudo-randomness and is suitable for image encryption. Based on this chaotic map, we propose an image encryption algorithm with a symmetric structure, which can achieve confusion and diffusion at the same time. Simulation results show that after encryption using the proposed algorithm, the entropy of the cipher is extremely close to the ideal value of eight, and the correlation coefficients between the pixels are lower than 0.01, thus the algorithm can resist statistical attacks. Moreover, the number of pixel change rate (NPCR) and the unified average changing intensity (UACI) of the proposed algorithm are very close to the ideal value, which indicates that it can efficiently resist chosen-plain text attack.

Cloud systems suffer from distributed concurrency bugs, which often lead to data loss and service outage. This paper presents CLOUDRAID, a new automatical tool for finding distributed concurrency bugs efficiently and effectively. Distributed concurrency bugs are notoriously difficult to find as they are triggered by untimely interaction among nodes, i.e., unexpected message orderings. To detect concurrency bugs in cloud systems efficiently and effectively, CLOUDRAID analyzes and tests automatically only the message orderings that are likely to expose errors. Specifically, CLOUDRAID mines the logs from previous executions to uncover the message orderings that are feasible but inadequately tested. In addition, we also propose a log enhancing technique to introduce new logs automatically in the system being tested. These extra logs added improve further the effectiveness of CLOUDRAID without introducing any noticeable performance overhead. Our log-based approach makes it well-suited for live systems. We have applied CLOUDRAID to analyze six representative distributed systems: Apache Hadoop2/Yarn, HBase, HDFS, Cassandra, Zookeeper, and Flink. CLOUDRAID has succeeded in testing 60 different versions of these six systems (10 versions per system) in 35 hours, uncovering 31 concurrency bugs, including nine new bugs that have never been reported before. For these nine new bugs detected, which have all been confirmed by their original developers, three are critical and have already been fixed.

Wearable device is becoming more and more popular, the emergence of wearable equipment, is widely used in daily life, medical, industrial and scientific research and so on. But most of the wearable equipment still needs people’s actual operation, whi

Existing methods place data or code in scratchpad memory (SPM) by relying on heuristics or resorting to integer programming or mapping it to a graph-coloring problem. In this article, the SPM allocation problem for arrays is formulated as an interval coloring problem. The key observation is that in many embedded C programs, two arrays can be modeled such that either their live ranges do not interfere or one contains the other (with good accuracy). As a result, array interference graphs often form a special class of superperfect graphs (known as comparability graphs), and their optimal interval colorings become efficiently solvable. This insight has led to the development of an SPM allocation algorithm that places arrays in an interference graph in SPM by examining its maximal cliques. If the SPM is no smaller than the clique number of an interference graph, then all arrays in the graph can be placed in SPM optimally. Otherwise, we rely on containment-motivated heuristics to split or spill array live ranges until the resulting graph is optimally colorable. We have implemented our algorithm in SUIF/machSUIF and evaluated it using a set of embedded C benchmarks from MediaBench and MiBench. Compared to a graph-coloring algorithm and an optimal ILP algorithm (when it runs to completion), our algorithm achieves close-to-optimal results and is superior to graph coloring for the benchmarks tested.