Results 11 to 20 of about 1,102 (116)

Cloud Storage File Recoverability [PDF]

, 2017
Data loss is perceived as one of the major threats for cloud storage. Consequently, the security community developed several challenge-response protocols that allow a user to remotely verify whether an outsourced file is still intact.
Armknecht, Frederik, Cid, Carlos, Gorke, Christian, Janson, Christian   +3 more
core   +2 more sources

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

, 2021
Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data.
Gómez-Bombarelli, Rafael, Schwalbe-Koda, Daniel, Tan, Aik Rui   +2 more
core   +1 more source

Recognition model of IIoT equipment in coal mine

Gong-kuang zidonghua
The computing and storage resources of the industrial Internet of things (IIoT) equipment in the coal mine are limited, making it vulnerable to illegal network intrusion, causing sensitive data leakage or malicious tampering, and threatening the safety ...
HAO Qinxia, LI Huimin
doaj   +1 more source

A secure privacy preserving deduplication scheme for cloud computing [PDF]

, 2019
© 2019 Elsevier B.V. Data deduplication is a key technique to improve storage efficiency in cloud computing. By pointing redundant files to a single copy, cloud service providers greatly reduce their storage space as well as data transfer costs.
Fan, Y, Liang, W, Lin, X, Nanda, P, Tan, G   +4 more
core   +1 more source

A software approach to defeating side channels in last-level caches

, 2016
We present a software approach to mitigate access-driven side-channel attacks that leverage last-level caches (LLCs) shared across cores to leak information between security domains (e.g., tenants in a cloud).
Arcangeli A., Elizaveta L., Kim T., Li M., Rane A., Varadarajan V., Yarom Y.   +6 more
core   +1 more source

Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

Journal of Intelligent Systems
This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods.
Khaleel Yahya Layth, Habeeb Mustafa Abdulfattah, Albahri A. S., Al-Quraishi Tahsien, Albahri O. S., Alamoodi A. H.   +5 more
doaj   +1 more source

MojiTalk: Generating Emotional Responses at Scale

, 2018
Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only ...
Wang, William Yang, Zhou, Xianda
core   +1 more source

Context-Aware Generative Adversarial Privacy

, 2017
Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often
Chen, Xiao, Huang, Chong, Kairouz, Peter, Rajagopal, Ram, Sankar, Lalitha   +4 more
core   +2 more sources

CoNLL 2017 Shared Task : Multilingual Parsing from Raw Text to Universal Dependencies [PDF]

, 2017
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
Attia, Mohammed, Badmaeva, Elena, Banerjee, Esha, Burchardt, Aljoscha, Cinková, Silvie, de Marneffe, Marie-Catherine, dePaiva, Valeria, Droganova, Kira, Elkahky, Ali, Fernández Alcalde, Héctor, Ginter, Filip, Gökırmak, Memduh, Habash, Nizar, Hajič, Jan, Hajič, jr., Jan, Harris, Kim, Hlaváčová, Jaroslava, Kanayama, Hiroshi, Kanerva, Jenna, Kayadelen, Tolga, Kettnerová, Václava, Kirchner, Jesse, Kwak, Sookyoung, Lando, Tatiana, Lertpradit, Saran, Leung, Herman, Li, Josie, Luotolahti, Juhani, Macketanz, Vivien, Mandl, Michael, Manning, Christopher D., Manurung, Ruli, Marheinecke, Katrin, Martínez Alonso, Héctor, Mendonça, Gustavo, Missilä, Anna, Nedoluzhko, Anna, Nitisaroj, Rattima, Nivre, Joakim, Ojala, Stina, Petrov, Slav, Pitler, Emily, Popel, Martin, Potthast, Martin, Pyysalo, Sampo, Reddy, Siva, Rehm, Georg, Sanguinetti, Manuela, Schuster, Sebastian, Shimada, Atsuko, Simi, Maria, Stella, Antonio, Straka, Milan, Strnadová, Jana, Sulubacak, Umut, Taji, Dima, Tyers, Francis, Urešová, Zdeňka, Uszkoreit, Hans, Yu, Zhuoran, Zeman, Daniel, Çöltekin, Çağrı   +61 more
core   +3 more sources

Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review

Pediatric Investigation, EarlyView.
Six artificial intelligence strategies advance autism research from tool optimization to paradigm shift: causal modeling, spatiotemporal networks, multimodal integration, digital twins, social cognition mapping, collaborative learning, and context‐aware interventions for precision care.
Ting Zhang, Zhenkun Cao, Wei Li, Zhihai Lv   +3 more
wiley   +1 more source