scholarly journals ACSAC 2020: Furthering the Quest to Tackle Hard Problems and Find Practical Solutions

2021 ◽  
Vol 19 (6) ◽  
pp. 23-24
Author(s):  
Danfeng Daphne Yao ◽  
Terry Benzel
Keyword(s):  
Hard Problems

Public-Key Encryption

2017 ◽  
Author(s):  
Keith M. Martin
Keyword(s):  
Elliptic Curve ◽  
Public Key Cryptography ◽  
Public Key ◽  
Public Key Encryption ◽  
Hybrid Encryption ◽  
Public Key Cryptosystems ◽  
Encryption And Decryption ◽  
Encryption Schemes ◽  
Hard Problems ◽  
Set Up

In this chapter, we introduce public-key encryption. We first consider the motivation behind the concept of public-key cryptography and introduce the hard problems on which popular public-key encryption schemes are based. We then discuss two of the best-known public-key cryptosystems, RSA and ElGamal. For each of these public-key cryptosystems, we discuss how to set up key pairs and perform basic encryption and decryption. We also identify the basis for security for each of these cryptosystems. We then compare RSA, ElGamal, and elliptic-curve variants of ElGamal from the perspectives of performance and security. Finally, we look at how public-key encryption is used in practice, focusing on the popular use of hybrid encryption.

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

scholarly journals Preface for the Number-Theoretic Methods in Cryptology conferences

2020 ◽  
Vol 14 (1) ◽  
pp. 393-396
Author(s):  
Antoine Joux ◽  
Jacek Pomykała
Keyword(s):  
Number Theory ◽  
International Association ◽  
Easy Access ◽  
Annual Series ◽  
Hard Problems

AbstractNumber-Theoretic Methods in Cryptology (NutMiC) is a bi-annual series of conferences that waslaunched in 2017. Its goal is to spur collaborations between cryptographers and number-theorists and to encourage progress on the number-theoretic hard problems used in cryptology. The publishing model for the series is also mixing the traditions of the cryptography and number theory communities. Articles were accepted for presentation at the conference by a scientific commitee and werereviewed again at a slower pace for inclusion in the journal post-proceedings.In 2019, the conference took place at the Institut de Mathématiques de Jussieu, Sorbonne University,Paris. The event was organized in collaboration with the international association for cryptologic research (IACR) and supported by the European Union’s H2020 Program under grant agreement number ERC-669891. This support allowed us to have low registration costs and offer easy access to all interested researchers.We were glad to have the participation of five internationally recognized invited speakers who greatly contributed to the success of the conference.Nutmic 2019 Co-Chairs,Antoine Joux and Jacek Pomykała

scholarly journals Nanolaser-based emulators of spin Hamiltonians

Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4193-4198 ◽  
Author(s):  
Midya Parto ◽  
William E. Hayenga ◽  
Alireza Marandi ◽  
Demetrios N. Christodoulides ◽  
Mercedeh Khajavikhan
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Exchange Interactions ◽  
Np Hard ◽  
Spin Models ◽  
Geometric Frustration ◽  
Spin Hamiltonians ◽  
Hard Problems ◽  
On Chip ◽  
Classical Spin Models

AbstractFinding the solution to a large category of optimization problems, known as the NP-hard class, requires an exponentially increasing solution time using conventional computers. Lately, there has been intense efforts to develop alternative computational methods capable of addressing such tasks. In this regard, spin Hamiltonians, which originally arose in describing exchange interactions in magnetic materials, have recently been pursued as a powerful computational tool. Along these lines, it has been shown that solving NP-hard problems can be effectively mapped into finding the ground state of certain types of classical spin models. Here, we show that arrays of metallic nanolasers provide an ultra-compact, on-chip platform capable of implementing spin models, including the classical Ising and XY Hamiltonians. Various regimes of behavior including ferromagnetic, antiferromagnetic, as well as geometric frustration are observed in these structures. Our work paves the way towards nanoscale spin-emulators that enable efficient modeling of large-scale complex networks.

scholarly journals A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

2021 ◽  
Vol 11 (11) ◽  
pp. 5107
Author(s):  
Miguel Ortíz-Barrios ◽  
Antonella Petrillo ◽  
Fabio De Felice ◽  
Natalia Jaramillo-Rueda ◽  
Genett Jiménez-Delgado ◽  
...  
Keyword(s):  
Job Shop ◽  
Selection Process ◽  
Fuzzy Ahp ◽  
Flexible Job Shop ◽  
Criteria Weights ◽  
Dispatching Algorithm ◽  
Scheduling Method ◽  
Hard Problems ◽  
Fourth Step

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery.

scholarly journals Utilizing Shelve Slots: Sufficiency Conditions for Some Easy Instances of Hard Problems

1994 ◽  
Vol 10 (2) ◽  
pp. 216-229 ◽  
Author(s):  
Moshe Dror ◽  
Benjamin T. Smith ◽  
Martin Trudeau
Keyword(s):  
Hard Problems ◽  
Sufficiency Conditions

NP vs QMA_\log(2)

2010 ◽  
Vol 10 (1&2) ◽  
pp. 141-151
Author(s):  
S. Beigi
Keyword(s):  
Quantum Algorithms ◽  
Np Hard ◽  
Complete Problems ◽  
Hard Problems ◽  
Np Complete ◽  
Np Hard Problems

Although it is believed unlikely that $\NP$-hard problems admit efficient quantum algorithms, it has been shown that a quantum verifier can solve NP-complete problems given a "short" quantum proof; more precisely, NP\subseteq QMA_{\log}(2) where QMA_{\log}(2) denotes the class of quantum Merlin-Arthur games in which there are two unentangled provers who send two logarithmic size quantum witnesses to the verifier. The inclusion NP\subseteq QMA_{\log}(2) has been proved by Blier and Tapp by stating a quantum Merlin-Arthur protocol for 3-coloring with perfect completeness and gap 1/24n^6. Moreover, Aaronson et al. have shown the above inclusion with a constant gap by considering $\widetilde{O}(\sqrt{n})$ witnesses of logarithmic size. However, we still do not know if QMA_{\log}(2) with a constant gap contains NP. In this paper, we show that 3-SAT admits a QMA_{\log}(2) protocol with the gap 1/n^{3+\epsilon}} for every constant \epsilon>0.

scholarly journals Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

2020 ◽  
Vol 10 (6) ◽  
pp. 2075 ◽  
Author(s):  
Shih-Cheng Horng ◽  
Shieh-Shing Lin
Keyword(s):  
Optimization Problems ◽  
Solution Space ◽  
Inequality Constraints ◽  
Optimization Methods ◽  
Ordinal Optimization ◽  
Np Hard ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Hard Problems ◽  
Np Hard Problems

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods.

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