Benchmarking Algorithms for Federated Domain Generalization
CoRR, 2023 While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges. Additionally, we explore domain-based heterogeneity in clients' local datasets - a realistic Federated DG scenario. Prior Federated DG evaluations are limited in terms of the
Ruqi Bai +2 more
openaire +3 more sources
N-Trans: Parallel Detection Algorithm for DGA Domain Names
Future Internet, 2022 Domain name generation algorithms are widely used in malware, such as botnet binaries, to generate large sequences of domain names of which some are registered by cybercriminals.
Cheng Yang +4 more
doaj +1 more source
Algorithms and complexity for functions on general domains [PDF]
Journal of Complexity, 2020 minor revision; to appear in Journal of ...
openaire +3 more sources
Adaptation of the simulated evolution algorithm for wind farm layout optimization [PDF]
FME Transactions, 2022 Wind energy is a potential replacement for traditional, fossil-fuel-based power generation sources. One important factor in the process of wind energy generation is to design of the optimal layout of a wind farm to harness maximum energy.
Khan Salman A.
doaj +1 more source
Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms [PDF]
Journal of Information Security and Applications, 2019 Nowadays, malware campaigns have reached a high level of sophistication, thanks to the use of cryptography and covert communication channels over traditional protocols and services.
C. Patsakis, Fran Casino
semanticscholar +1 more source
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge [PDF]
, 2022 Assessing the Mitotic Count has a known high degree of intra- and inter-rater variability. Computer-aided systems have proven to decrease this variability and reduce labeling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains.
Frauke Wilm +3 more
openaire +2 more sources
Flow-Based Programming for Machine Learning
Future Internet, 2022 Machine Learning (ML) has gained prominence and has tremendous applications in fields like medicine, biology, geography and astrophysics, to name a few.
Tanmaya Mahapatra, Syeeda Nilofer Banoo
doaj +1 more source
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA ...
Nitay Calderon +3 more
semanticscholar +1 more source
CharBot: A Simple and Effective Method for Evading DGA Classifiers
IEEE Access, 2019 Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names, which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect
Jonathan Peck +7 more
doaj +1 more source
Video semantic content analysis framework based on ontology combined MPEG-7 [PDF]
, 2007 The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level.
A. Artale +10 more
core +1 more source