In this page scientific papers, related to the development of the ALOHA project, are announced.

• D. Maiorca, A. Demontis, B. Biggio, F. Roli, G. Giacinto, «Adversarial detection of flash malware: Limitations and open issues», in Computers and Security, Volume 96, 101901, 96:101901, Elsevier, September 2020.
  
  
  
• D. Solans, B. Biggio, C. Castillo, «Poisoning Attacks on Algorithmic Fairness», in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), virtual event, September 14-18, 2020.
  
  
  
• S. Minakova and T. Stefanov, «Buffer Sizes Reduction for Memory-efficient CNN Inference on Mobile and Embedded Devices», in Proceedings of the DSD 2020 Euromicro Conference on Digital System Design, Portroz, Slovenia, virtual event, August 26-28, 2020. Video presentation here.
  
  
  
• D. Sapra and A. D. Pimentel, «Deep Learning Model Reuse and Composition in Knowledge Centric Networking», in the Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN 2020), Honolulu, Hawaii, USA, August 26-28, 2020.
  
  
  
• D. Sapra and A. D. Pimentel, «An Evolutionary Optimization Algorithm for Gradually Saturating Objective Functions», in the Proceedings of the ACM International Genetic and Evolutionary Computation Conference (GECCO 2020), Cancun, Mexico, July, 2020.
  
  
  
• S. Minakova, E. Tang, T. Stefanov, «Combining task- and data-level parallelism for high-throughput CNN inference on embedded CPUs-GPUs MPSoCs», in the Proceedings of the SAMOS XX International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, Pythagoreio, Samos Island, Greece, virtual event, July 4-6, 2020. Video presentation here.
  
  
  
• D. Sapra and A. D. Pimentel,  «Constrained Evolutionary Piecemeal Training to Design Efficient Neural Networks», in the Proceedings of the 33rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2020), Kitakyushu, Japan, July, 2020.
  
  
  
• A. Sotgiu, A. Demontis, M. Melis, B. Biggio, G. Fumera, X. Feng, F. Roli, «Deep neural rejection against adversarial examples», in EURASIP Journal on Information Security, 5, Springer, April 2020.
  
  
  
• N. Shepeleva, W. Zellinger, M. Lewandowski, B. Moser, «ReLU Code Space: A Basis for Rating Network Quality Besides Accuracy», in ICLR 2020 Workshop on Neural Architecture Search (NAS 2020), April 2020.
  
  
  
• A. Demontis, M. Melis, M. Pintor, M. Jagielski, B. Biggio, A. Oprea, C. Nita-Rotaru, F. Roli. «Why do adversarial attacks transfer? Explaining transferability of evasion and poisoning attacks», in the Proceedings of the 28th USENIX Security Symposium (USENIX Security 19), August 14–16, 2019 Santa Clara, CA, USA), USENIX Association, 2019.
  
  
  
• D. Maiorca, B. Biggio, G. Giacinto, «Towards adversarial malware detection: Lessons learned from PDF-based attacks», in ACM Computing Surveys, Vol. 52, No. 4, Article 78, August 2019.
  
  
  
• P. Meloni, D. Loi, P. Busia, G. Deriu, F. Conti, L. Benini, N. Fragoulis, I. Theodorakopoulos, A. D. Pimentel, D. Sapra, M. Pintor, B. Biggio, M. Masin, T. Stefanov, S. Minakova, B. Moser, N. Shepeleva, F. Palumbo, «Optimization and deployment of CNNs at the edge: the ALOHA experience», Computing Frontiers, ACM Digital Library, May 2019.

• M. A. Scrugli, D. Loi, L. Raffo, and Paolo Meloni, «A Runtime-Adaptive Cognitive IoT Node for Healthcare Monitoring», Computing Frontiers, ACM Digital Library, May 2019.

• P. Meloni, D. Loi, G. Deriu, A. D. Pimentel, D. Sapra, Bernhard A. Moser, Natalia Shepeleva, F. Conti, L. Benini, O. Ripolles, D. Solans, M. Pintor, B. Biggio, T. Stefanov, S. Minakova, N. Fragoulis, I. Theodorakopoulos, M. Masin, and F. Palumbo, «Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project», in Proceedings of the 30th International Conference on Microelectronics (ICM2018), IEEE Xplore Digital Library, December 16-19, 2018.

• L. Li, C. Sau, T. Fanni, J. Li, T. Viitanen, F. Christophe, F. Palumbo, L. Raffo, H. Huttunen, J. Takala, S. S. Bhattacharyya, «An integrated hardware/software design methodology for signal processing systems», in Journal of System Architectures, Elsevier, Volume 93, February 2019, Pages 1-19.
  Abstract and Acknowledgment.

• P. Meloni, D. Loi, G. Deriu, A. D. Pimentel, D. Sapra, Bernhard A. Moser, Natalia Shepeleva, F. Conti, L. Benini, O. Ripolles, D. Solans, M. Pintor, B. Biggio, T. Stefanov, S. Minakova, N. Fragoulis, I. Theodorakopoulos, M. Masin, and F. Palumbo, «ALOHA: An architectural-aware framework for deep learning at the edge», in INTESA '18 Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications, ACM Digital Library, pages 19–26, October 2018.
  Abstract and Acknowledgment.

• B. Biggio, and F. Roli., «Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning», in Pattern Recognition, Elsevier, vol. 84, 317–331, 2018.
  Abstract and Acknowledgment.

• B. Kolosnjaji, A. Demontis, B. Biggio, D. Maiorca, G. Giacinto, C. Eckert, and F. Roli, «Adversarial malware binaries: Evading deep learning for malware detection in executables», in 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, September 3-7, 2018.
  Abstract and Acknowledgment.

• M. Melis, D. Maiorca, B. Biggio, G. Giacinto, and F. Roli., «Explaining black-box android malware detection», in 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, September 3-7, 2018.
  Abstract and Acknowledgment.

• M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li, «Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning», in 39th IEEE Symposium on Security and Privacy, San Francisco, CA, US, May 21-23, 2018.
  Abstract and Acknowledgment.