A recursive algorithm for solving “a secret sharing” problem

Abstract

This paper presents a recursive algorithm for solving “a secret sharing” problem. This problem is one of the unsolved problems in the Second International Students Olympiad in Cryptography (NSUCRYPTO2015). Recently, Geut et al. solved the problem in a special case. We show that our algorithm is able to solve it in general.     

Unsupervised classification for polarimetric SAR images based on the improved CFSFDP algorithm

In polarimetric synthetic aperture radar (PolSAR) image processing, the number of classes is an important factor for PolSAR image classification. Therefore, how to accurately estimate the number of PolSAR image classes is an important issue. In this article, we propose a novel unsupervised classification method which can accurately estimate the number of classes for PolSAR images. First, the PolSAR image is initialized into many small clusters by using the complementary information from Yamaguchi decomposition and distribution characteristics of data. Second, the improved clustering by fast search and find of density peaks, named as improved CFSFDP algorithm, is introduced to select the appropriate category number. Finally, to improve the representation of each category, the PolSAR data set is classified by an iterative fine-tuning process based on a complex K-Wishart function. The performance of the proposed classification approach is presented and analysed on three real data sets. The experimental results show that the proposed classification method can accurately estimate the category number and enhance the classification accuracy in comparison with other traditional methods. It is also shown that the data distribution characteristic has the additional information beyond the target scattering decomposition, and this information is important for the initialization.     

Experimental application and enhancement of the XFEM–GA algorithm for the detection of flaws in structures

The extended finite element formulation (XFEM) combined with genetic algorithms (GAs) have previously been shown to be very effective in the detection of flaws in structures. By this approach, the XFEM is used to model the forward problem and a GA is used as the optimization scheme, converging to the true flaw. The convergence is obtained by minimizing the error between sensor measurements and data obtained by solving the forward problem.The current study proposes several advances of this XFEM–GA algorithm, more specifically: (i) a novel genetic algorithm that accelerates the convergence of the scheme and alleviates entrapment in local optima, (ii) a generic XFEM formulation of an elliptical hole which is utilized to detect any type of flaw (cracks or holes) of any shape, and (iii) experimental verification of the approach for an arbitrary crack in a 2D plate.Convergence studies on various benchmark problems including the experimental verification clearly show the potential of this approach to detection of arbitrary flaws.     

Comments on “the parameter identification of a population model of China”

The parametric model used by Zhu and Wan for expressing survival rate as a function of age can be replaced by a simpler model without discernable loss of accuracy. The parameters of this simpler model are subject to easy interpretation.     

An accurate interest matching algorithm based on prediction of the space–time intersection of regions for the distributed virtual environment

Interest matching is an important data-filtering mechanism for a large-scale distributed virtual environment. Many of the existing algorithms perform interest matching at discrete timesteps. Thus, they may suffer the missing-event problem: failing to report the events between two consecutive timesteps. Some algorithms solve this problem, by setting short timesteps, but they have a low computing efficiency. Additionally, these algorithms cannot capture all events, and some spurious events may also be reported. In this paper, we present an accurate interest matching algorithm called the predictive interest matching algorithm, which is able to capture the missing events between discrete timesteps. The PIM algorithm exploits the polynomial functions to model the movements of virtual entities, and predict the time intervals of region overlaps associated with the entities accurately. Based on the prediction of the space–time intersection of regions, our algorithm can capture all missing events and does not report the spurious events at the same time. To improve the runtime performance, a technique called region pruning is proposed and used in our algorithm. In experiments, we compare the new algorithm with the frequent interest matching algorithm and the space–time interest matching algorithm on the HLA/RTI distributed infrastructure. The results prove that although an additional matching effort is required in the new algorithm, it outperforms the baselines in terms of event-capturing ability, redundant matching avoidance, runtime efficiency and scalability.     

An algorithm for the determination of the vibrational energy distribution in “hot” molecules. Description and test

An algorithm for the determination of the vibrational energy of molecules energized above their threshold for unimolecular reaction is presented. Using easily obtainable experimental data the method performs their deconvolution applying well-established unimolecular reaction concepts. Their mathematical foundations are described as well as its implementation in IBM AT compatible TB BASIC. In the case example the vibrational energy distribution is known (can be calculated thermochemically), and therefore can be used to test the method performance. It is shown that using precise unimolecular reaction parameters and well-defined average energies transferred by collision 〈ΔE〉 the agreement between deconvoluted and thermochemical results is good (i.e. within the uncertainties involved in the thermochemical test calculation).     

A software-based dynamic-warp scheduling approach for load-balancing the Viola–Jones face detection algorithm on GPUs

Face detection is a key component in applications such as security surveillance and human–computer interaction systems, and real-time recognition is essential in many scenarios. The Viola–Jones algorithm is an attractive means of meeting the real time requirement, and has been widely implemented on custom hardware, FPGAs and GPUs. We demonstrate a GPU implementation that achieves competitive performance, but with low development costs. Our solution treats the irregularity inherent to the algorithm using a novel dynamic warp scheduling approach that eliminates thread divergence. This new scheme also employs a thread pool mechanism, which significantly alleviates the cost of creating, switching, and terminating threads. Compared to static thread scheduling, our dynamic warp scheduling approach reduces the execution time by a factor of 3. To maximize detection throughput, we also run on multiple GPUs, realizing 95.6 FPS on 5 Fermi GPUs.     

An improved formalism for quantum computation based on geometric algebra—case study: Grover’s search algorithm

The Grover search algorithm is one of the two key algorithms in the field of quantum computing, and hence it is desirable to represent it in the simplest and most intuitive formalism possible. We show firstly, that Clifford’s geometric algebra, provides a significantly simpler representation than the conventional bra-ket notation, and secondly, that the basis defined by the states of maximum and minimum weight in the Grover search space, allows a simple visualization of the Grover search analogous to the precession of a spin- ${\frac{1}{2}}$ particle. Using this formalism we efficiently solve the exact search problem, as well as easily representing more general search situations. We do not claim the development of an improved algorithm, but show in a tutorial paper that geometric algebra provides extremely compact and elegant expressions with improved clarity for the Grover search algorithm. Being a key algorithm in quantum computing and one of the most studied, it forms an ideal basis for a tutorial on how to elucidate quantum operations in terms of geometric algebra—this is then of interest in extending the applicability of geometric algebra to more complicated problems in fields of quantum computing, quantum decision theory, and quantum information.     

Comments on “Fast architecture for decimal digit multiplication”

This paper proposes some corrections and comments to the BCD multiplier presented in the paper “Fast architecture for decimal digit multiplication”, published in the Journal of Microprocessors and Microsystems (Volume 39, 2015, issues 4–5, pages 296–301). Some corrections are first proposed to the presented binary multiplier, while the discussion is later extended to some issues that have been found regarding the binary-to-BCD converter.     

Comments on “general failure of logic programs”

The paper [1] purports to present a classification of the general failure sets of logic programs and a simple proof of the theorem on the soundness and completeness of the negation-as-failure rule. In this note we clarify some conflicting terminology between [1] and the papers [2, 3] to which it predominantly refers. Our main purpose, however, is to point out major errors, in particular, one in the proof of the above mentioned theorem.     

Comments on “A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm”

In the recent paper entitled “A digital watermarking scheme based on singular value decomposition and tiny genetic algorithm” by C.-C. Lai (2011) [1], a robust digital image watermarking scheme based on singular value decomposition and a tiny genetic algorithm is proposed. This comment shows that this watermarking scheme is fundamentally flawed in that the extracted watermark is not the embedded watermark but determined by the reference watermark.     

Comments on “The Replication of the Hard Problem of Consciousness in AI and Bio-AI”

In their joint paper entitled “The Replication of the Hard Problem of Consciousness in AI and BIO-AI” (Boltuc et al. Replication of the hard problem of conscious in AI and Bio- AI: An early conceptual framework 2008), Nicholas and Piotr Boltuc suggest that machines could be equipped with phenomenal consciousness, which is subjective consciousness that satisfies Chalmer’s hard problem (We will abbreviate the hard problem of consciousness as “H-consciousness”). The claim is that if we knew the inner workings of phenomenal consciousness and could understand its’ precise operation, we could instantiate such consciousness in a machine. This claim, called the extra-strong AI thesis, is an important claim because if true it would demystify the privileged access problem of first-person consciousness and cast it as an empirical problem of science and not a fundamental question of philosophy. A core assumption of the extra-strong AI thesis is that there is no logical argument that precludes the implementation of H-consciousness in an organic or in-organic machine provided we understand its algorithm. Another way of framing this conclusion is that there is nothing special about H-consciousness as compared to any other process. That is, in the same way that we do not preclude a machine from implementing photosynthesis, we also do not preclude a machine from implementing H-consciousness. While one may be more difficult in practice, it is a problem of science and engineering, and no longer a philosophical question. I propose that Boltuc’s conclusion, while plausible and convincing, comes at a very high price; the argument given for his conclusion does not exclude any conceivable process from machine implementation. In short, if we make some assumptions about the equivalence of a rough notion of algorithm and then tie this to human understanding, all logical preconditions vanish and the argument grants that any process can be implemented in a machine. The purpose of this paper is to comment on the argument for his conclusion and offer additional properties of H-consciousness that can be used to make the conclusion falsifiable through scientific investigation rather than relying on the limits of human understanding.