Maat

The malware analysis and detection research community relies on the online platform VirusTotal to label Android apps based on the scan results of around 60 antiviral scanners. Unfortunately, there are no standards on how to best interpret the scan results acquired from VirusTotal, which leads to the utilization of different threshold-based labeling strategies (e.g., if 10 or more scanners deem an app malicious, it is considered malicious). While some of the utilized thresholds may be able to accurately approximate the ground truths of apps, the fact that VirusTotal changes the set and versions of the scanners it uses makes such thresholds unsustainable over time. We implemented a method, Maat , that tackles these issues of standardization and sustainability by automatically generating a Machine Learning ( ML )-based labeling scheme, which outperforms threshold-based labeling strategies. Using the VirusTotal scan reports of 53K Android apps that span 1 year, we evaluated the applicability of Maat ’s Machine Learning ( ML )-based labeling strategies by comparing their performance against threshold-based strategies. We found that such ML -based strategies (a) can accurately and consistently label apps based on their VirusTotal scan reports, and (b) contribute to training ML -based detection methods that are more effective at classifying out-of-sample apps than their threshold-based counterparts.

The Value of Consistency: Portfolio Labeling Strategies and Impact on Winery Brand Equity

The reported research examines the impact of product portfolio labeling strategies on brand reputation and equity. A netnographic approach allowed to observe winery portfolio labeling approaches and create a typology of winery labeling strategies. Expert evaluation served to assess the dependent variable brand equity by deploying a regression analysis. For the observed wine industry, being part of the food industry, creating consistent and recognizable brands has a direct relevance for reducing (sustainability-related) food information overload and thereby building sustainable brand equity. The results uncover the relative importance of each of the six identified labeling strategies as well as their impact on reputation and brand equity creation. The results point to the need to establish a consistent, strategically founded product communication. Such an approach, with a positive effect on reputation building can serve to build sustainable brand equity. “Stuck in the middle”-type strategies apparently diminish winery brand equity exploitation. The findings contribute to the knowledge on food labels in product communication strategies and their impact on organizational brand equity, thereby having high relevance for the implementation of environmental certification initiatives in an organizational context. The article deploys a novel research approach in an under-researched area to provide new insights for further research as well as implications for practice.

Chemical labeling strategies for small molecule natural product detection and isolation

Chemical labeling enhances the analysis of complex mixtures via HPLC-MS in both targeted and untargeted metabolomics workflows.

Cell segmentation using deep learning: comparing label and label-free approaches using hyper-labeled image stacks

Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channels available to each respective training dataset make it difficult to directly compare the effectiveness of these strategies across studies. Here we explore training models using subimage stacks composed of channels sampled from larger, ‘hyper-labeled’, image stacks. This allows us to directly compare a variety of labeling strategies and training approaches on identical cells. This approach revealed that fluorescence-based strategies generally provide higher segmentation accuracies but were less accurate than label-free models when labeling was inconsistent. The relative strengths of label and label-free techniques could be combined through the use of merging fluorescence channels and out-of-focus brightfield images. Beyond comparing labeling strategies, using subimage stacks for training was also found to provide a method of simulating a wide range of labeling conditions, increasing the ability of the final model to accommodate a greater range of experimental setups.