MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction

In this work, we propose MUSTACHE, a new page cache replacement algorithm whose logic is learned from observed memory access requests rather than fixed like existing policies. We formulate the page request prediction problem as a categorical time series forecasting task. Then, our method queries the learned page request forecaster to obtain the next $k$ predicted page memory references to better approximate the optimal B\'el\'ady's replacement algorithm. We implement several forecasting techniques using advanced deep learning architectures and integrate the best-performing one into an existing open-source cache simulator. Experiments run on benchmark datasets show that MUSTACHE outperforms the best page replacement heuristic (i.e., exact LRU), improving the cache hit ratio by 1.9% and reducing the number of reads/writes required to handle cache misses by 18.4% and 10.3%

Turning Federated Learning Systems Into Covert Channels

Federated learning (FL) goes beyond traditional, centralized machine learning by distributing model training among a large collection of edge clients. These clients cooperatively train a global, e.g., cloud-hosted, model without disclosing their local, private training data. The global model is then shared among all the participants which use it for local predictions. In this paper, we put forward a novel attacker model aiming at turning FL systems into covert channels to implement a stealth communication infrastructure. The main intuition is that, during federated training, a malicious sender can poison the global model by submitting purposely crafted examples. Although the effect of the model poisoning is negligible to other participants, and does not alter the overall model performance, it can be observed by a malicious receiver and used to transmit a single bit

Analysis of DTC nutrigenetic services in Italy: state of the art, agreement to the ESHG statement and future outlooks

Background: In both USA and Europe operate companies selling Direct-to-consumer genetic tests (DTC). These tests are offered to healthy people aiming to identify predispositions to complex diseases and to take preventive measures. Several DTC-nutrigenetic tests (DNTs) are available on the market. They propose the definition of a personalized diet, on the basis of the investigated genetic variants, which would reduce the risk of developing those diseases which have been associated to specific genetic markers. However, the risk/benefit balance of exposing unselected population to genetic testing without any medical surveillance is far from be established. Furthermore, it lacks an accepted procedure to select which genetic markers needs to be investigated, to evaluate their specific role and, as consequence, to define a personalized diet. Within this context, the European Society of Human Genetics (ESHG) released a statement regarding the DTC tests that has been ratified by several national societies including the Italian one. 
In the present study we analyzed the DNT offered in Italy, the state of the art and the abidance with the ESHG statement. 
Methods: We queried web search engine for the DNT offered to italian population, portraying a non-specialized customer. We examined the DNTs vendor websites and/or directly contacted the companies to collect information on: 1) genetic marker essayed, 2) diseases and phenotypes considered and 3) kind of dietary advices provided. Finally, we evaluated the abidance to the ESHG statement. The study was conducted between November, 2010 and May, 2011.
Results: Six companies operate in Italy with a total of seven different DNTs offered. Both studied phenotypes and investigated genetic markers were very different among companies, with a relative higher level of agreement for phenotype than for genes. None of the companies described the methods used to select markers and to define diet advices. None of the companies showed a complete agreement to the statement of the ESHG. 
Conclusion: Although DNT companies' efforts are worthy, a standardization of methods and a more strictly agreement with ESHG statement should be encouraged

Measuring corporate digital divide through websites : insights from Italian firms

Published online: 30 July 2024With the increasing pervasiveness of Information and Communication Technology (ICT) in the fabric of economic activities, the corporate digital divide has become a crucial issue for the assessment of Information Technology (IT) competencies and the digital gap between firms and territories. With little granular data available to measure the phenomenon, most studies have used survey data. To address this empirical gap, we scanned the homepages of 182,705 Italian companies and extracted ten characteristics related to their digital footprint to develop a new index for the corporate digital assessment. Our results show a significant digital divide between Italian companies according to size, sector and geographical location, opening new perspectives for monitoring and data-driven analysis.This work was partially supported by the consortium Artes 4.0 - Advanced Robotics and Enabling Digital Technologies and Systems and the Department of Excellence “Economic and Digital Resilience (RED)” project of IMT Lucca