While process monitoring and command-line parameters are great sources for telemetry that can be useful for detecting malicious Rundll32, they require environment-specific tuning. As you can imagine, Rundll32 is used by many legitimate tools. To avoid flooding your security team with a ton of false positives, establish a baseline on what activity is normal in your environment and then write rules that will exclude the known activity. This is a great starting point, but keep in mind that these analytics will likely require a lot of tuning and monitoring to get to the point where they reliably produce high-fidelity alerting.
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bpo-45129: Due to significant security concerns, the reuse_addressparameter of asyncio.loop.create_datagram_endpoint(), disabled inPython 3.9, is now entirely removed. This is because of the behavior ofthe socket option SO_REUSEADDR in UDP.
The $if version test may be used to perform comparisons against specific Readline versions. The version expands to the current Readline version. The set of comparison operators includes = (and ==), !=, =, . The version number supplied on the right side of the operator consists of a major version number, an optional decimal point, and an optional minor version (e.g., "7.1"). If the minor version is omitted, it is assumed to be "0". The operator may be separated from the string version and from the version number argument by whitespace. The following example sets a variable if the Readline version being used is 7.0 or newer:
The Reader Control supports endpoint and kinetic detection, sequential and simultaneous multi-detection, spectral scanning and multiple well scanning modes. In addition, it controls reagent injectors, gas (O2 and CO2) and temperature, as well as shaking. A script wizard allows for the multiplexing of different measurement protocols and more complex operations.
Insoluble particles like cells or aggregates typically do not evenly distribute across the well. Matrix scanning, a key feature on BMG LABTECH readers, scans a whole well creating a matrix with up to 900 points/well resolution. In MARS, the different data points can be analysed separately or as clusters. Data can also be displayed as a 3D well map. This feature can also be used to view kinetic absorbance data three dimensionally.
ApplicationVerifieris a free tool from Microsoft (available as part of the Windows SDK)that can be used to flush out programming errors. Starting with M68Application Verifier can be enabled for chrome.exe without needingto disable the sandbox. After adding chrome.exe to the list ofapplications to be stressed you need to expand the list of Basicschecks and disable the Leak checks. You may also need to disableHandles and Locks checks depending on your graphics driver andspecific Chrome version, but the eventual goal is to have Chrome runwith Handles and Locks checks enabled. When bugs are foundChrome will trigger a breakpoint so running all Chrome processesunder a debugger is recommended. Chrome will run much more slowlybecause Application Verifier puts every heap allocation on aseparate page. Note that with PartitionAlloc Everywhere mostChromium allocations don't actually go through the Windows heapand are therefore unaffected by Application Verifier.
Diagnosis of malaria parasitemia from blood smears is a subjective and time-consuming task for pathologists. The automatic diagnostic process will reduce the diagnostic time. Also, it can be worked as a second opinion for pathologists and may be useful in malaria screening. This study presents an automatic method for malaria diagnosis from thin blood smears. According to this fact that malaria life cycle is started by forming a ring around the parasite nucleus, the proposed approach is mainly based on curve fitting to detect parasite ring in the blood smear. The method is composed of six main phases: stain object extraction step, which extracts candidate objects that may be infected by malaria parasites. This phase includes stained pixel extraction step based on intensity and colour, and stained object segmentation by defining stained circle matching. Second step is preprocessing phase which makes use of nonlinear diffusion filtering. The process continues with detection of parasite nucleus from resulted image of previous step according to image intensity. Fourth step introduces a complete search process in which the circle search step identifies the direction and initial points for direct least-square ellipse fitting algorithm. Furthermore in the ellipse searching process, although parasite shape is completed undesired regions with high error value are removed and ellipse parameters are modified. Features are extracted from the parasite candidate region instead of whole candidate object in the fifth step. By employing this special feature extraction way, which is provided by special searching process, the necessity of employing clump splitting methods is removed. Also, defining stained circle matching process in the first step speeds up the whole procedure. Finally, a series of decision rules are applied on the extracted features to decide on the positivity or negativity of malaria parasite presence. The algorithm is applied on 26 digital images which are provided
After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.
Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The mostmore important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools. less
Due to the increasing demand for high-quality ceramic crowns and bridges, the CAD/CAM-based production of dental restorations has been a subject of intensive research during the last fifteen years. A prerequisite for the efficient processing of the 3D measurement of prepared teeth with a minimal amount of user interaction is the automatic determination of the preparation line, which defines the sealing margin between the restoration and the prepared tooth. Current dental CAD/CAM systems mostly require the interactive definition of the preparation line by the user, at least by means of giving a number of start points. Previous approaches to the automatic extraction of the preparation line rely on single contour detection algorithms. In contrast, we use a combination of different contour detection algorithms to find several independent potential preparation lines from a height profile of the measured data. The different algorithms (gradient-based, contour-based, and region-based) show their strengths and weaknesses in different clinical situations. A classifier consisting of three stages (range check, decision tree, support vector machine), which is trained by human experts with real-world data, finally decides which is the correct preparation line. In a test with 101 clinical preparations, a success rate of 92.0% has been achieved. Thus the combination of different contour detection algorithms yields a reliable method for the automatic extraction of the preparation line, which enables the setup of a turn-key dental CAD/CAM process chain with a minimal amount of interactive screen work. 2ff7e9595c
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