![]() ![]() Models to outperform the state of the art on benchmark datasets. Such features, along with the network's baseline features, allow the proposed Work, we develop models based on a pre-trained convolutional neural network forĮxtracting sentiment, emotion and personality features for sarcasm detection. Language that standard text categorization techniques cannot grasp. Sarcasm, however, can beĮxpressed in very subtle ways and requires a deeper understanding of natural ![]() Task primarily as a text categorization problem. To date, most approaches to sarcasm detection have treated the "apparently positive" sentence and, hence, negatively affect polarity detection In sentiment analysis, for example, sarcasm can flip the polarity of an Range 70C to 90C), 2)~is more likely to occur if the aggressor row is activeįor longer time (e.g., RowHammer vulnerability increases by 36% if we keep aĭRAM row active for 15 column accesses), and 3)~is more likely to occur inĬertain physical regions of the DRAM module under attack (e.g., 5% of the rowsĪre 2x more vulnerable than the remaining 95% of the rows).Download a PDF of the paper titled A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks, by Soujanya Poria and 3 other authors Download PDF Abstract: Sarcasm detection is a key task for many natural language processing tasks. RowHammer bit flip 1)~is very likely to occur in a bounded range, specific toĮach DRAM cell (e.g., 5.4% of the vulnerable DRAM cells exhibit errors in the Among our 16 new observations, we highlight that a To this end, we present an experimental characterization usingĢ48~DDR4 and 24~DDR3 modern DRAM chips from four major DRAM manufacturersĭemonstrating how the RowHammer effects vary with three fundamental properties:ġ)~DRAM chip temperature, 2)~aggressor row active time, and 3)~victim DRAMĬell's physical location. The RowHammer vulnerability that are not yet rigorously studied by prior works,īut can potentially be $i$) exploited to develop more effective RowHammerĪttacks or $ii$) leveraged to design more effective and efficient defense Our goal in this paper is to provide insights into fundamental properties of Vulnerability of modern DRAM chips to more effectively secure current and Therefore, it is essential toĭevelop a better understanding and in-depth insights into the RowHammer Reduced by more than 10X in the last decade. Than older chips such that the required hammer count to cause a bit flip has ![]() Previously marketed as RowHammer-safe, are even more vulnerable to RowHammer Recent studies demonstrate that modern DRAM chips, including chips ![]() RowHammer vulnerability worsens as DRAM cell size and cell-to-cell spacing (i.e., hammering) a DRAM row can cause bit flips in physically nearby rows. Kim and Onur Mutlu Download PDF Abstract: RowHammer is a circuit-level DRAM vulnerability where repeatedly accessing Download a PDF of the paper titled A Deeper Look into RowHammer`s Sensitivities: Experimental Analysis of Real DRAM Chips and Implications on Future Attacks and Defenses, by Lois Orosa and Abdullah Giray Ya\u and Haocong Luo and Ataberk Olgun and Jisung Park and Hasan Hassan and Minesh Patel and Jeremie S. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |