scholarly journals Machine Learning of Physical Unclonable Functions using Helper Data

2021 ◽  
pp. 1-36
Author(s):  
Emanuele Strieder ◽  
Christoph Frisch ◽  
Michael Pehl
Keyword(s):  
Machine Learning ◽  
Error Correcting Code ◽  
Public Information ◽  
Block Codes ◽  
Mitigation Strategies ◽  
Direct Access ◽  
Physical Unclonable Functions ◽  
Repetition Code ◽  
The Impact ◽  
Helper Data

Physical Unclonable Functions (PUFs) are used in various key-generation schemes and protocols. Such schemes are deemed to be secure even for PUFs with challenge-response behavior, as long as no responses and no reliability information about the PUF are exposed. This work, however, reveals a pitfall in these constructions: When using state-of-the-art helper data algorithms to correct noisy PUF responses, an attacker can exploit the publicly accessible helper data and challenges. We show that with this public information and the knowledge of the underlying error correcting code, an attacker can break the security of the system: The redundancy in the error correcting code reveals machine learnable features and labels. Learning these features and labels results in a predictive model for the dependencies between different challenge-response pairs (CRPs) without direct access to the actual PUF response. We provide results based on simulated data of a k-SUM PUF model and an Arbiter PUF model. We also demonstrate the attack for a k-SUM PUF model generated from real data and discuss the impact on more recent PUF constructions such as the Multiplexer PUF and the Interpose PUF. The analysis reveals that especially the frequently used repetition code is vulnerable: For a SUM-PUF in combination with a repetition code, e.g., already the observation of 800 challenges and helper data bits suffices to reduce the entropy of the key down to one bit. The analysis also shows that even other linear block codes like the BCH, the Reed-Muller, or the Single Parity Check code are affected by the problem. The code-dependent insights we gain from the analysis allow us to suggest mitigation strategies for the identified attack. While the shown vulnerability advances Machine Learning (ML) towards realistic attacks on key-storage systems with PUFs, our analysis also facilitates a better understanding and evaluation of existing approaches and protocols with PUFs. Therefore, it brings the community one step closer to a more complete leakage assessment of PUFs.

scholarly journals Znaczenie Biuletynu Informacji Publicznej w zapewnieniu jawności działań administracji publicznej na przykładzie zamówień publicznych

Prawo ◽  
2017 ◽  
Vol 323 ◽  
pp. 277-287
Author(s):  
Adam Mika
Keyword(s):  
Public Administration ◽  
Public Procurement ◽  
Great Influence ◽  
Public Information ◽  
Direct Access ◽  
Information Bulletin ◽  
Public Contracts ◽  
The Impact

The importance of Public Information Bulletin BIP for realizing transparency of public administration activities in public procurement area Public Information Bulletin BIP has a great influence on direct access to public information. Its importance is bigger and bigger in Public Procurement Law nowadays. Publications in BIP are cur­rently required in cases of in house-procurement and public contracts for social and other specific services. The aim of this paper is to evaluate the impact of BIP on transparency of public adminis­tration activities in public procurement area, especially the impact of the lack of publication in BIP on validity of contracts.

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

2021 ◽  
Vol 12 (04) ◽  
pp. 808-815
Author(s):  
Lin Lawrence Guo ◽  
Stephen R. Pfohl ◽  
Jason Fries ◽  
Jose Posada ◽  
Scott Lanyon Fleming ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Medicine ◽  
Mitigation Strategies ◽  
Learning Performance ◽  
Learning Models ◽  
Dataset Shift ◽  
The Impact ◽  
Machine Learning Models

Abstract Objective The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was measured, the procedures used to preserve model performance, and their effects. Results Of 4,457 potentially relevant publications identified, 15 were included. The impact of temporal dataset shift was primarily quantified using changes, usually deterioration, in calibration or discrimination. Calibration deterioration was more common (n = 11) than discrimination deterioration (n = 3). Mitigation strategies were categorized as model level or feature level. Model-level approaches (n = 15) were more common than feature-level approaches (n = 2), with the most common approaches being model refitting (n = 12), probability calibration (n = 7), model updating (n = 6), and model selection (n = 6). In general, all mitigation strategies were successful at preserving calibration but not uniformly successful in preserving discrimination. Conclusion There was limited research in preserving the performance of machine learning models in the presence of temporal dataset shift in clinical medicine. Future research could focus on the impact of dataset shift on clinical decision making, benchmark the mitigation strategies on a wider range of datasets and tasks, and identify optimal strategies for specific settings.

scholarly journals Capturing Global, Predicting Local for Controlling Antimicrobial Resistance: a retrospective multivariable analysis

2021 ◽  
Author(s):  
Raghav Awasthi ◽  
Samprati Agrawal ◽  
Vaidehi Rakholia ◽  
Lovedeep Singh Dhingra ◽  
Aditya Nagori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Staphylococcus Aureus ◽  
Antimicrobial Resistance ◽  
Multivariable Analysis ◽  
Causal Modeling ◽  
Mitigation Strategies ◽  
Supervised Machine Learning ◽  
World Health ◽  
Modeling Framework ◽  
The Impact

Background: Antimicrobial resistance (AMR) is a complex multifactorial outcome of health, socio-economic and geopolitical factors. Therefore, tailored solutions for mitigation strategies could be more effective in dealing with this challenge. Knowledge-synthesis and actionable models learned upon large datasets are critical in order to diffuse the risk of entering into a post-antimicrobial era. Objective: This work is focused on learning Global determinants of AMR and predicting susceptibility of antibiotics at isolate level (Local) for WHO (world health organization) declared critically important pathogens Pseudomonas aeruginosa, Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, Enterobacter cloacae, Staphylococcus aureus. Methods: In this study, we used longitudinal data (2004-2017) of AMR having 633820 isolates from 72 Middle and High-income countries. We integrated the Global burden of disease (GBD), Governance (WGI), and Finance data sets in order to find the unbiased and actionable determinants of AMR. We chose a Bayesian Decision Network (BDN) approach within the causal modeling framework to quantify determinants of AMR. Finally Integrating Bayesian networks with classical machine learning approaches lead to effective modeling of the level of AMR. Results: From MAR (Multiple Antibiotic Resistance) scores, we found that developing countries are at higher risk of AMR compared to developed countries, for all the critically important pathogens. Also, Principal Components Analysis(PCA) revealed that governance, finance, and disease burden variables have a strong association with AMR. We further quantified the impact of determinants in a probabilistic way and observed that heath system access and government effectiveness are strong actionable factors in reducing AMR, which was in turn confirmed by what-if analysis. Finally, our supervised machine learning models have shown decent performance, with the highest on Staphylococcus aureus. For Staphylococcus aureus, our model predicted susceptibility to Ceftaroline and Oxacillin with the highest AUROC, 0.94 and 0.89 respectively.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

The Film World’s Cooperative Store: Institutions and Films of the 1930s and 1940s

2018 ◽  
Author(s):  
C. Claire Thomson
Keyword(s):  
World War Ii ◽  
Meat Products ◽  
Public Information ◽  
Early History ◽  
World War ◽  
International Context ◽  
German Occupation ◽  
History Of ◽  
The Uk ◽  
The Impact

This chapter traces the early history of state-sponsored informational filmmaking in Denmark, emphasising its organisation as a ‘cooperative’ of organisations and government agencies. After an account of the establishment and early development of the agency Dansk Kulturfilm in the 1930s, the chapter considers two of its earliest productions, both process films documenting the manufacture of bricks and meat products. The broader context of documentary in Denmark is fleshed out with an account of the production and reception of Poul Henningsen’s seminal film Danmark (1935), and the international context is accounted for with an overview of the development of state-supported filmmaking in the UK, Italy and Germany. Developments in the funding and output of Dansk Kulturfilm up to World War II are outlined, followed by an account of the impact of the German Occupation of Denmark on domestic informational film. The establishment of the Danish Government Film Committee or Ministeriernes Filmudvalg kick-started aprofessionalisation of state-sponsored filmmaking, and two wartime public information films are briefly analysed as examples of its early output. The chapter concludes with an account of the relations between the Danish Resistance and an emerging generation of documentarists.

A MODELING SYSTEM FOR CLIMATE CHANGE RISK ASSESSMENT, MANAGEMENT AND HEDGING IN COASTAL AREAS

2017 ◽  
Author(s):  
Sergei Soldatenko ◽  
Sergei Soldatenko ◽  
Genrikh Alekseev ◽  
Genrikh Alekseev ◽  
Alexander Danilov ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Coastal Areas ◽  
Mitigation Strategies ◽  
System Structure ◽  
The Arctic ◽  
Risk Modeling ◽  
Wide Range ◽  
Adverse Weather ◽  
The Impact

Every aspect of human operations faces a wide range of risks, some of which can cause serious consequences. By the start of 21st century, mankind has recognized a new class of risks posed by climate change. It is obvious, that the global climate is changing, and will continue to change, in ways that affect the planning and day to day operations of businesses, government agencies and other organizations and institutions. The manifestations of climate change include but not limited to rising sea levels, increasing temperature, flooding, melting polar sea ice, adverse weather events (e.g. heatwaves, drought, and storms) and a rise in related problems (e.g. health and environmental). Assessing and managing climate risks represent one of the most challenging issues of today and for the future. The purpose of the risk modeling system discussed in this paper is to provide a framework and methodology to quantify risks caused by climate change, to facilitate estimates of the impact of climate change on various spheres of human activities and to compare eventual adaptation and risk mitigation strategies. The system integrates both physical climate system and economic models together with knowledge-based subsystem, which can help support proactive risk management. System structure and its main components are considered. Special attention is paid to climate risk assessment, management and hedging in the Arctic coastal areas.

scholarly journals Improving Drought Tolerance in Mungbean (Vigna radiata L. Wilczek): Morpho-Physiological, Biochemical and Molecular Perspectives

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1534
Author(s):  
Chandra Mohan Singh ◽  
Poornima Singh ◽  
Chandrakant Tiwari ◽  
Shalini Purwar ◽  
Mukul Kumar ◽  
...  
Keyword(s):  
Drought Stress ◽  
Drought Tolerance ◽  
Crop Production ◽  
Mitigation Strategies ◽  
Theoretical Research ◽  
Whole Genome Sequence ◽  
Complex Nature ◽  
Drought Tolerant ◽  
Breeding Programmes ◽  
The Impact

Drought stress is considered a severe threat to crop production. It adversely affects the morpho-physiological, biochemical and molecular functions of the plants, especially in short duration crops like mungbean. In the past few decades, significant progress has been made towards enhancing climate resilience in legumes through classical and next-generation breeding coupled with omics approaches. Various defence mechanisms have been reported as key players in crop adaptation to drought stress. Many researchers have identified potential donors, QTLs/genes and candidate genes associated to drought tolerance-related traits. However, cloning and exploitation of these loci/gene(s) in breeding programmes are still limited. To bridge the gap between theoretical research and practical breeding, we need to reveal the omics-assisted genetic variations associated with drought tolerance in mungbean to tackle this stress. Furthermore, the use of wild relatives in breeding programmes for drought tolerance is also limited and needs to be focused. Even after six years of decoding the whole genome sequence of mungbean, the genome-wide characterization and expression of various gene families and transcriptional factors are still lacking. Due to the complex nature of drought tolerance, it also requires integrating high throughput multi-omics approaches to increase breeding efficiency and genomic selection for rapid genetic gains to develop drought-tolerant mungbean cultivars. This review highlights the impact of drought stress on mungbean and mitigation strategies for breeding high-yielding drought-tolerant mungbean varieties through classical and modern omics technologies.

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