Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Understanding Malvertising Through Ad-Injecting Browser Extensions

Abstract: Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.

Command suggestions during automatic speech recognition

Abstract: Features are disclosed for identifying and providing command suggestions during automatic speech recognition. As utterances are interpreted, suggestions may be provided based on even partial interpretations to guide users of a client device to commands available via speech recognition.

LEEP: A New Measure to Evaluate Transferability of Learned Representations

Abstract: We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once. We analyze the properties of LEEP theoretically and demonstrate its effectiveness empirically. Our analysis shows that LEEP can predict the performance and convergence speed of both transfer and meta-transfer learning methods, even for small or imbalanced data. Moreover, LEEP outperforms recently proposed transferability measures such as negative conditional entropy and H scores. Notably, when transferring from ImageNet to CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing method in terms of the correlations with actual transfer accuracy.

Request routing utilizing cost information

Abstract: A system, method and computer-readable medium for request routing based on cost information are provided. A client request processing a resource identifier for requested content transmits a first DNS query to a content delivery network service provider. The content delivery network service provider transmits an alternative resource identifier in response to the client computing device DNS query. The alternative resource identifier is selected as a function of cost information. The client computing device then issues a second DNS query to the same content delivery network service provider. The content delivery network service provider can then either resolve the second DNS query with an IP address of a cache component or transmit another alternative resource identifier that will resolve to the content delivery network service provider. The process can repeat with the content delivery network service provider's network until a DNS nameserver resolves a DNS query from the client computing device.

Metabolic and ionoregulatory responses of the Amazonian cichlid, Astronotus ocellatus, to severe hypoxia

Abstract: We examined the metabolic and ionoregulatory responses of the Amazonian cichlid, Astronotus ocellatus, to 20 h exposure to severe hypoxia (0.37 ± 0.19 mg O2/l; 4.6% air saturation) or 8 h severe hypoxia followed by 12 h recovery in normoxic water. During 20 h exposure to hypoxia, white muscle [ATP] was maintained at normoxic levels primarily through a 20% decrease in [creatine phosphate] (CrP) and an activation of glycolysis yielding lactate accumulation. Muscle lactate accumulation maintained cytoplasmic redox state ([NAD+]/[NADH]) and was associated with an inactivation of the mitochondrial enzyme pyruvate dehydrogenase (PDH). The inactivation of PDH was not associated with significant changes in cytoplasmic allosteric modulators ([ADPfree], redox state, or [pyruvate]). Hypoxia exposure caused a ∼65% decrease in gill Na+/K+ ATPase activity, which was not matched by changes in Na+/K+ ATPase α-subunit protein abundance indicating post-translational modification of Na+/K+ ATPase was responsible for the decrease in activity. Despite decreases in gill Na+/K+ ATPase activity, plasma [Na+] increased, but this increase was possibly due to a significant hemoconcentration and fluid shift out of the extracellular space. Hypoxia caused an increase in Na+/K+ ATPase α-subunit mRNA abundance pointing to either reduced mRNA degradation during exposure to hypoxia or enhanced expression of Na+/K+ ATPase α-subunit relative to other genes.