Checking Cleanness in Linked Lists

Inefficient use of memory, including leaks and bloat, remain a significant challenge for C and C++ developers. Applications with these problems become slower over time as their working set grows and can become unresponsive. At the same time, memory leaks and bloat remain notoriously difficult to debug, and comprise a large number of reported bugs in mature applications. Previous tools for diagnosing memory inefficiencies-based on garbage collection, binary rewriting, or code sampling-impose high overheads (up to 100X) or generate many false alarms. This paper presents Hound, a runtime system that helps track down the sources of memory leaks and bloat in C and C++ applications. Hound employs data sampling , a staleness-tracking approach based on a novel heap organization, to make it both precise and efficient. Hound has no false positives, and its runtime and space overhead are low enough that it can be used in deployed applications. We demonstrate Hound's efficacy across a suite of synthetic benchmarks and real applications.

For C++ programs often appear memory leaks, cross-border access memory and memory does not match the release of error, we have analyzed existing memory error detection tools and methods, implemented in the Linux platform to run the memory testing tool MemDetect prototype based on the open source dynamic instrumentation-Valgrind using function family's memory information block management method and life cycle method. The prototype can detect memory leaks, cross-border access memory and memory does not match the release problems effectively, the validity and efficiency of the prototype are proved by comparing with Purify Rational.

Opposite links of positive and negative symptomatology with memory errors in schizophrenia

Many research works has done on security surveillance camera system.Most of the experiment result shows good result in lab environment but the performance is not good enough when it apply to real life application.The experiment environment of research lab is different from outside world; therefore illumination condition is also different.Although false alarm works well in research environment but it has many problems in real world or dynamic background.False alarm is caused by shadow, smoke and light illumination condition and different type of noises these are major concern for effective surveillance camera system.So how to remove shadow effectively is more important than error detection and accurate result.Which is the main objective in surveillance system?The focus of our work is to remove the shadow efficiently.The computer vision researchers always give importance to the experimental issues such as image input color, morphological algorithms or moving object detection not to the real environmental issues.If the input image and shadow is similar with human then other algorithm not performs well while our proposed algorithm works very accurately.Experimental result shows our proposed algorithm can find out human and shadow from the input image.We have used different simulated illumination condition to find out the noise and successfully subtracted shadow.For our simulation environment we used unity 3D and Direct 10, tessellation (bump mapping).

The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

We present our experience of combining, in a realistic setting, a static analyzer with a statistical analysis. This combination is in order to reduce the inevitable false alarms from a domain-unaware static analyzer. Our analyzer named Airac(Array Index Range Analyzer for C) collects all the true buffer-overrun points in ANSI C programs. The soundness is maintained, and the analysis’ cost-accuracy improvement is achieved by techniques that static analysis community has long accumulated. For still inevitable false alarms (e.g. Airac raised 970 buffer-overrun alarms in commercial C programs of 5.3 million lines and 737 among the 970 alarms were false), which are always apt for particular C programs, we use a statistical post analysis. The statistical analysis, given the analysis results (alarms), sifts out probable false alarms and prioritizes true alarms. It estimates the probability of each alarm being true. The probabilities are used in two ways: 1) only the alarms that have true-alarm probabilities higher than a threshold are reported to the user; 2) the alarms are sorted by the probability before reporting, so that the user can check highly probable errors first. In our experiments with Linux kernel sources, if we set the risk of missing true error is about 3 times greater than false alarming, 74.83% of false alarms could be filtered; only 15.17% of false alarms were mixed up until the user observes 50% of the true alarms.KeywordsFalse AlarmProbable False AlarmTrue ErrorUser AnnotationProgram PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The incorrect use of pointers, such as null pointer dereference and memory leak, is one of the most common sources of program errors. In this talk, I will describe our techniques for automatically verifying the absence of such pointer errors, which we have been developing for the past three years, based on a new program logic called separation logic. This talk has two goals. The first is to show, by demo, the current status of techniques for automatically verifying pointer safety. The second is to present interesting instances of the interplay between automatic verification and program logic. In order to reduce the complexity of formal (manual) verification of programs, separation logic has unusual proof rules that exploit programming disciplines used by skilled software developers. I will explain how such rules have been used to improve the performance of our automatic verification techniques. Regarding the influence of automatic verification on program logic, I will describe new types of theorem proving questions on separation logic that were motivated by automatic verification.

In the previous chapter, we reviewed memory management in Objective-C for a non- ARC environment. But what happens when ARC is enabled? This chapter explores the changes incurred when ARC is employed. Automatic Reference Counting was briefly explained in Chapter 1, but it is really best summarized in Apple’s own words: Automatic Reference Counting (ARC) in Objective-C makes memory management the job of the compiler. By enabling ARC with the new Apple LLVM compiler, you will never need to type retain or release again, dramatically simplifying the development process, while reducing crashes and memory leaks. The compiler has a complete understanding of your objects, and releases each object the instant it is no longer used, so apps run as fast as ever, with predictable, smooth performance.1 KeywordsSource CodeObject TypeOwnership StatusStatic ArrayDynamic ArrayThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Infrared (IR) point target detection and tracking is challenging due to lack of texture and detailed information of small dim targets. The problem becomes even more challenging when there is a real-time requirement too. A key goal in robust detection is to reduce missed detections (MD) and false alarms (FA). Complex traditional state-of-the-art point target detection algorithms may give accurate results but typically incur larger execution times which renders them unsuitable for real-time applications. On the other hand, deep learning methods for point target detection do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time detection and tracking of multiple point targets in diverse background conditions. We apply a ’top-hat’ operator as the first stage of our detection algorithm and this is followed by a systematic thresholding scheme to yield a good balance between MD and FA. This is followed by a methodology to find the exact target position from the detections, and a track association scheme in conjunction with Kalman filter for state estimation. Based on the proposed approach for target detection and track association, we perform 2D tracking of image coordinates as well as 3D tracking of azimuth and elevation angles. We verify the effectiveness of our method on 12 different IR image sequences over existing state-of-the-art methods in terms of accuracy as well as speed.

Precise heap reachability information is a prerequisite for many static verification clients. However, the typical scenario is that the available heap information, computed by say an up-front points-to analysis, is not precise enough for the client of interest. This imprecise heap information in turn leads to a deluge of false alarms for the tool user to triage. Our position is to approach the false alarm problem not just by improving the up-front analysis but by also employing after-the-fact, goal-directed refutation analyses that yield targeted precision improvements. We have investigated refutation analysis in the context of detecting statically a class of Android memory leaks. For this client, we have found the necessity for an overall analysis capable of path-sensitive reasoning interprocedurally and with strong updates--a level of precision difficult to achieve globally in an up-front manner. Instead, our approach uses a refutation analysis that mixes highly precise, goal-directed reasoning with facts derived from the up-front analysis to prove alarms false and thus enabling effective and sound filtering of the overall list of alarms.

Ground-based radar interferometry (GBSAR) is a useful method to control the stability of engineering objects and elements of geographical spaces at risk of deformation or displacement. To secure accurate and credible measurement results, it is crucial to consider atmospheric conditions as they influence the corrections to distance measurements. These conditions are especially important considering the radar bandwidth used. Measurements for the stability of engineering objects are not always performed in locations where meteorological monitoring is prevalent; however, information about the range of variability in atmospheric corrections is always welcome. The authors present a hybrid method to estimate the probable need of atmospheric corrections, which allows partly eliminating false positive alarms of deformations as caused by atmospheric fluctuations. Unlike the numerous publications on atmospheric reductions focused on the current state of the atmosphere, the proposed solution is based on applying a classic machine learning algorithm designed for the SARIMAX (Seasonal Autoregressive Integrated Moving Average with covariate at time) time series data model for satellite data shared by NASA (National Aeronautics and Space Administration) during the Landsat MODIS (Moderate Resolution Imaging Spectroradiometer) mission before performing residual estimation during the monitoring phase. Example calculations (proof of concept) were made for ten-year satellite data covering a region for experimental flood bank stability observations as performed using the IBIS-L (Image by Interferometric Survey—Landslide) radar and for target monitoring data (ground measurements).

Sound over-approximation methods are effective for proving the absence of errors, but inevitably produce false alarms that can hamper programmers. In contrast, under-approximation methods focus on bug detection and are free from false alarms. In this work, we present two novel proof systems designed to locate the source of errors via backward under-approximation, namely Sufficient Incorrectness Logic (SIL) and its specialization for handling memory errors, called Separation SIL. The SIL proof system is minimal, sound and complete for Lisbon triples, enabling a detailed comparison of triple-based program logics across various dimensions, including negation, approximation, execution order, and analysis objectives. More importantly, SIL lays the foundation for our main technical contribution, by distilling the inference rules of Separation SIL, a sound and (relatively) complete proof system for automated backward reasoning in programs involving pointers and dynamic memory allocation. The completeness result for Separation SIL relies on a careful crafting of both the assertion language and the rules for atomic commands.

In this paper a new learning algorithm, Lever Training Machine (LTM), is presented for binary classification. LTM is a supervised learning algorithm and its main idea is inspired from a physics principle: Lever Principle. Figuratively, LTM involves rolling a hyper-plane around the convex hull of the target training set, and using the equilibrium position of the hyper-plane to define a decision surfaces. In theory, the optimal goal of LTM is to maximize the correct rejection rate. If the distribution of target set is convex, a set of such decision surfaces can be trained for exact discrimination without false alarm. Two mathematic experiments and the practical application of face detection confirm that LTM is an effective learning algorithm.

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

This paper presents an exact performance analysis for the evaluating a mean acquisition time in a cellular code division multiple access system. New expressions for the probabilities of detection, miss, and false alarm are derived in a frequency-selective Rician fading channel. From the numerical results, it is shown that our formula gives the accurate results of the mean acquisition time in the performance analysis of the parallel acquisition system with the reference filter.