A particularly insidious type of concurrency bug is atomicity violations. While there has been substantial work on automatic detection of atomicity violations, each existing technique has focused on a certain type of atomic region. To address this limitation, this paper presents AtomTracker, a comprehensive approach to atomic region inference and violation detection. AtomTracker is the first scheme to (1) automatically infer generic atomic regions (not limited by issues such as the number of variables accessed, the number of instructions included, or the type of code construct the region is embedded in) and (2) automatically detect violations of them at runtime with negligible execution overhead. AtomTracker provides novel algorithms to infer generic atomic regions and to detect atomicity violations of them. Moreover, we present a hardware implementation of the violation detection algorithm that leverages cache coherence state transitions in a multiprocessor. In our evaluation, we take eight atomicity violation bugs from real-world codes like Apache, MySql, and Mozilla, and show that AtomTracker detects them all. In addition, AtomTracker automatically infers all of the atomic regions in a set of microbenchmarks accurately. Finally, we also show that the hardware implementation induces a negligible execution time overhead of 0.2-4.0% and, therefore, enables AtomTracker to find atomicity violations on-the-fly in production runs.

Past work has focused on detecting data races as proxies for Sequential Consistency (SC) violations. However, most data races do not violate SC. In addition, lock-free data structures and synchronization libraries sometimes explicitly employ data races but rely on SC semantics for correctness. Consequently, to uncover SC violations, we need to develop a more precise technique.This paper presents Vulcan, the first hardware scheme to precisely detect SC violations at runtime, in programs running on a relaxed-consistency machine. The scheme leverages cache coherence protocol transactions to dynamically detect cycles in memoryaccess orders across threads. When one such cycle is about to occur, an exception is triggered. For the conditions considered in this paper and with enough hardware, Vulcan suffers neither false positives nor false negatives. In addition, Vulcan induces negligible execution overhead, requires no help from the software, and only takes as input the program executable. Experimental results show that Vulcan detects three new SC violation bugs in the Pthread and Crypt libraries, and in the fmm code from SPLASH-2. Moreover, Vulcan's negligible execution overhead makes it suitable for on-the-fly use.

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Green parks in urban areas are believed to enhance the well-being of residents. The importance of green spaces to support health and fitness in urban areas has recently regained interest. Reports released in 2010-2016 by the World Health Organization (WHO) on urban planning, environment, and health stated that green spaces can have a positive impact on physical activity, social and mental well-being, enhance air quality and decrease noise exposure. We analyzed the number of check-ins in various parks of Shanghai by utilizing geotagged social media network check-in data. This article presents a descriptive study using social media data by obtaining the three-year comparison of spatial and temporal patterns of park visits to raise public awareness that green parks provide a healthy environment that can be beneficial for the wellbeing of urban citizens. We investigated the visitor spatiotemporal behavior in more than 115 green parks in 10 districts of Shanghai with approximately 250,000 check-ins. We examined 3 years of geotagged data and our main findings are: (i) the spatial and temporal variations of users in urban green parks (ii) the gender differences in space and time with relation to urban green parks. The main objective of this article is to present evident data for policymakers on the advantages of providing green spaces access to urban citizens and to facilitate cities with systematic approaches to provide green space access to improve the health of urban citizens. INDEX TERMS Urban green parks, big data, social networks, spatiotemporal, KDE, data mining.

Data races are a major contributor to parallel software unreliability. A type of race that is both common and typically harmful is the Asymmetric data race. It occurs when at least one of the racing threads is inside a critical section. Current proposals that target them are software-based. They slow down execution and require significant compiler, operating system (OS), or application changes.This paper proposes the first scheme to tolerate asymmetric data races in production runs with negligible execution overhead. The scheme, called Pacman, exploits cache coherence hardware to temporarily protect the variables that a thread accesses in a critical section from other threads' requests. Unlike previous schemes, Pacman induces negligible slowdown, needs no support from the compiler or (in the baseline design) from the OS, and requires no application source code changes. In addition, its hardware is relatively unintrusive. We test Pacman with the SPLASH-2, PARSEC, Sphinx3, and Apache codes, and discover two unreported asymmetric data races.

Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.

An IF-Condition Invariance Violation (ICIV) occurs when, after a thread has computed the control expression of an IF statement and while it is executing the THEN or ELSE clauses, another thread updates variables in the IF's control expression. An ICIV can be easily detected, and is likely to be a sign of a concurrency bug in the code. Typically, the ICIV is caused by a data race, which we call IF-Condition Data Race (ICR).In this paper, we analyze the data races reported in the bug databases of popular software systems and show that ICRs occur relatively often. Then, we present two techniques to handle ICRs dynamically. They rely on simple code transformations and, in one case, additional hardware help. One of them (SW-IF) detects the races, while the other (HW-IF) detects and prevents them. We evaluate SW-IF and HW-IF using a variety of applications. We show that these new techniques are effective at finding new data race bugs and run with low overhead. Specifically, HW-IF finds 5 new (unreported) race bugs and SW-IF finds 3 of them. In addition, 8-threaded executions of SPLASH-2 codes show that, on average, SW-IF adds 2% execution overhead, while HW-IF adds less than 1%.

During mesh processing operations (e.g. simplifications, compression, and watermarking), a 3D triangle mesh is subject to various visible distortions on mesh surface which result in a need to estimate visual quality. The necessity of perceptual quality evaluation is already established, as in most cases, human beings are the end users of 3D meshes. To measure such kinds of distortions, the metrics that consider geometric measures integrating human visual system (HVS) is called perceptual quality metrics. In this paper, we direct an expansive study on 3D mesh quality evaluation mostly focusing on recently proposed perceptual based metrics. We limit our study on greyscale static mesh evaluation and attempt to figure out the most workable method for real-time evaluation by making a quantitative comparison. This paper also discusses in detail how to evaluate objective metric's performance with existing subjective databases. In this work, we likewise research the utilization of the psychometric function to expel non-linearity between subjective and objective values. Finally, we draw a comparison among some selected quality metrics and it shows that curvature tensor based quality metrics predicts consistent result in terms of correlation.