Precomputation Phase Research Articles

A Novel JSF-Based Fast Implementation Method for Multiple-Point Multiplication

ECC is a popular public-key cryptographic algorithm, but it lacks an effective solution to multiple-point multiplication. This paper proposes a novel JSF-based fast implementation method for multiple-point multiplication. The proposed method requires a small storage space and has high performance, making it suitable for resource-constrained IoT application scenarios. This method stores and encodes the required coordinates in the pre-computation phase and uses table lookup operations to eliminate the conditional judgment operations in JSF-5, which improves the efficiency by about 70% compared to the conventional JSF-5 in generating the sparse form. This paper utilizes Co-Z combined with safegcd to achieve low computational complexity for curve coordinate pre-computation, which further reduces the complexity of multiple-point multiplication in the execution phase of the algorithm. The experiments were performed with two short Weierstrass elliptic curves, nistp256r1 and SM2. In comparison to the various CPU architectures used in the experiments, our proposed method showed an improvement of about 3% over 5-NAF.

High-speed implementation of rainbow table method on heterogeneous multi-device architecture

Heterogeneous multi-device architecture has been widely used to solve computationally intensive problems. This paper proposes a high-speed implementation of the rainbow table method based on perfect table in a CPU and GPU based computing system. Firstly, we introduce a memory optimization method to reduce the size of the rainbow table. Then, we show how to parallelize each phase of the rainbow table method using as many devices as possible to achieve optimal performance. We conduct a series of comparative experiments with different cryptographic hash functions on a heterogeneous architecture powered by an Intel Xeon 8176M CPU and two NVIDIA Tesla V100 GPUs. Compared with the state-of-the-art third-party tools, Cryptohaze and RainbowCrack, our implementation can reduce the size of the rainbow table by at least 57.1%, and we can achieve significant speedup: 2.03× and 131.3× speedup in the precomputation phase (table generation); 1.97× and 1.44× speedup in the online phase (searching the pre-image of a hash value), respectively.

Fast model order reduction boundary element method for large-scale acoustic systems involving surface impedance

This paper presents a newly developed fast model order reduction boundary element method (FMORBEM) for three-dimensional acoustic wave problems with the impedance boundary condition at relatively high frequencies. This method proceeds in two phases: a precomputation phase and a solution phase. In the precomputation phase, a simple yet powerful cutoff technique which benefits from the amplitude decay property of the kernel functions in the Burton–Miller formulation is proposed. Because it does not operate on the underlying boundary element full-order model (FOM), the computational cost and memory consumption in the process of offline constructing a proper subspace are nearly O(N) complexity with N being the number of degrees of freedom of the FOM. After that, the complete system matrices are computed and projected onto the spanned subspace column-by-column, yielding acceptable computational complexity of O(N2). In the solution phase, the resulting reduced-order model (ROM) allows for efficient storage and fast analyses, which merely requires O(n2) arithmetic operations by using direct solvers with n being the dimension of the ROM. Application of this method to the high-frequency scattering analyses of acoustic systems, i.e. simple sphere models and complex scatterers, shows the improved computational efficiency and reduced memory requirement as compared to the conventional boundary element method and further illustrates that it may be competitive with the fast multipole method based on iterative solvers.

Diffraction- and Reflection-Aware Multiple Sound Source Localization

In this article, we present a novel localization method for multiple sources in indoor environments. Our approach can estimate different propagation paths, including the reflection and diffraction paths of sound waves based on a backward ray tracing technique. To estimate diffraction propagation paths, we combine a ray tracing algorithm with a uniform theory of diffraction model by exploiting the diffraction properties as propagation paths bend around the wedges of obstacles. We reconstruct the 3-D environments and wedges of obstacles in the precomputation phase and utilize these outcomes to generate primary, reflection, and diffraction acoustic rays in the runtime phase. We localize multiple sources when identifying the convergence regions of these acoustic rays based on Monte Carlo localization (MCL). Our approach supports not only stationary but also moving sources of human speech and clapping sounds. Our approach can also handle nonline-of-sight (NLOS) sources and distinguish between active and inactive source states. We evaluated and analyzed our algorithm in multiple scenarios containing obstacles and NLOS sources. Our approach can localize moving sources with the average of distance errors of 0.65 and 0.74 m in single and multiple source cases, respectively, in rooms, 7 m by 7 m in size with a height of 3 m; errors are measured according to the L2 distance between the estimated and actual source positions. We observed a 130% improvement of the localization accuracy over the prior work (J.-M. Valin <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ).

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome.

Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact.

New Meet-in-the-Middle Attacks on FOX Block Cipher

Abstract FOX block cipher was designed with a Lai–Massey scheme, in which the round function uses the Substitution-Permutation-Substitution structure. A meet-in-the-middle (MITM) attack is one of the most important issues for the security of the block cipher, which consists of a precomputation phase for constructing a distinguisher and an online phase for key recovery. This paper studies the MITM attacks against FOX. The first MITM distinguishers of 5-round FOX64, 7-round FOX64-256 and 5-round FOX128 are presented when using the differential enumeration technique with truncated differential characteristics. Then, based on these distinguishers, the attacks for key recovery on 7-round FOX64, 11-round FOX64-256 and 7-round FOX128 are presented with the state-test and state-search techniques. It is shown that the attack on 11-round FOX64-256 is proposed for the first time; attacks on 7-round FOX64 and 7-round FOX128 can be improved with lower time and memory complexities compared with the currently known attacks.

Interleaving Search and Heuristic Improvement

Abstraction heuristics are a leading approach for deriving admissible estimates in cost-optimal planning. However, a drawback with respect to other families of heuristics is that they require a preprocessing phase for choosing the abstraction, computing the abstract distances, and/or suitable cost-partitionings. Typically, this is performed in advance by a fixed amount of time, even though some instances could be solved much faster with little or no preprocessing. We interleave the computation of abstraction heuristics with search, avoiding a long precomputation phase and allowing information from the search to be used for guiding the abstraction selection. To evaluate our ideas, we implement them on a planner that uses a single symbolic PDB. Our results show that delaying the preprocessing is not harmful in general even when an important amount of preprocessing is required to obtain good performance.

A Huygens' surface approach to rapid characterization of peripheral nerve stimulation.

Purpose:Peripheral nerve stimulation (PNS) modeling has a potential role in designing and operating MRI gradient coils but requires computationally demanding simulations of electromagnetic fields and neural responses. We demonstrate compression of an electromagnetic and neurodynamic model into a single versatile PNS matrix (P-matrix) defined on an intermediary Huygens’ surface to allow fast PNS characterization of arbitrary coil geometries and body positions.Methods:The Huygens’ surface approach divides PNS prediction into an extensive precomputation phase of the electromagnetic and neurodynamic responses, which is independent of coil geometry and patient position, and a fast coil-specific linear projection step connecting this information to a specific coil geometry. We validate the Huygens’ approach by performing PNS characterizations for 21 body and head gradients and comparing them with full electromagnetic-neurodynamic modeling. We demonstrate the value of Huygens’ surface-based PNS modeling by characterizing PNS-optimized coil windings for a wide range of patient positions and poses in two body models.Results:The PNS prediction using the Huygens’ P-matrix takes less than a minute (instead of hours to days) without compromising numerical accuracy (error ≤ 0.1%) compared to the full simulation. Using this tool, we demonstrate that coils optimized for PNS at the brain landmark using a male model can also improve PNS for other imaging applications (cardiac, abdominal, pelvic, and knee imaging) in both male and female models.Conclusion:Representing PNS information on a Huygens’ surface extended the approach’s ability to assess PNS across body positions and models and test the robustness of PNS optimization in gradient design.

Parallelization of a branch-and-bound algorithm for the maximum weight clique problem

In this paper, parallelization techniques are proposed for the branch-and-bound algorithm OTClique for the maximum weight clique problem. OTClique consists of the precomputation phase and the branch-and-bound phase. The proposed algorithm parallelizes both of them. In the precomputation phase, the construction of optimal tables is parallelized. In the branch-and-bound phase, the proposed algorithm generates small subproblems and assigns them to threads. A technique to share lower and upper bounds is also proposed. Experiments using some benchmarks show that the proposed parallelization techniques improve the performance of OTClique. With an 8-core CPU, the computation time of OTClique becomes 6.91 times shorter on random graphs and 5.38 times on DIMACS benchmarks on average.

Online Saturated Cost Partitioning for Classical Planning

Cost partitioning is a general method for admissibly summing up heuristic estimates for optimal state-space search. Most cost partitioning algorithms can optimize the resulting cost-partitioned heuristic for a specific state. Since computing a new cost-partitioned heuristic for each evaluated state is usually too expensive in practice, the strongest planners based on cost partitioning over abstraction heuristics precompute a set of cost-partitioned heuristics before the search and maximize over their estimates during the search. This makes state evaluations very fast, but since there is no better termination criterion than a time limit, it requires a long precomputation phase, even for the simplest planning tasks. A prototypical example for this is the Scorpion planner which computes saturated cost partitionings over abstraction heuristics offline before the search. Using Scorpion as a case study, we show that by incrementally extending the set of cost-partitioned heuristics online during the search, we drastically speed up the planning process and even often solve more tasks.

DCE -miner: an association rule mining algorithm for multimedia based on the MapReduce framework

The amount of multimedia data has grown rapidly because of improvements in data collection and storage technologies. The association rule mining (ARM) technique is a type of data mining method widely used to extract useful information from data warehouses. In real-world big data applications, fast and effective data mining algorithms are emerging as a valuable approach. In this paper, we propose DCE-Miner, a fast association rule mining algorithm with low memory requirements based on the MapReduce framework. In the precomputation phase, we split large datasets into equal-sized smaller ones using data division method. In the frequent K-itemsets mining phase, the mappers read the small datasets and distribute the data to reducers based on the closed set characteristics associated with each partition. The reducers use bitmaps to accelerate the computation speed and store the possible frequent 2-itemsets to reduce future computation. Extensive experimental results show that on large-scale datasets with up to 40 million transactions, DCE-Miner achieves better performance and is more robust with respect to dataset sizes and support level than are the current algorithms.

Efficient Enumeration Algorithms for Regular Document Spanners

Regular expressions and automata models with capture variables are core tools in rule-based information extraction. These formalisms, also called regular document spanners , use regular languages to locate the data that a user wants to extract from a text document and then store this data into variables. Since document spanners can easily generate large outputs, it is important to have efficient evaluation algorithms that can generate the extracted data in a quick succession, and with relatively little precomputation time. Toward this goal, we present a practical evaluation algorithm that allows output-linear delay enumeration of a spanner’s result after a precomputation phase that is linear in the document. Although the algorithm assumes that the spanner is specified in a syntactic variant of variable-set automata, we also study how it can be applied when the spanner is specified by general variable-set automata, regex formulas, or spanner algebras. Finally, we study the related problem of counting the number of outputs of a document spanner and provide a fine-grained analysis of the classes of document spanners that support efficient enumeration of their results.

Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor

A masking method is a widely known countermeasure against side-channel attacks. To apply a masking method to cryptosystems consisting of Boolean and arithmetic operations, such as ARX (Addition, Rotation, XOR) block ciphers, a masking conversion algorithm should be used. Masking conversion algorithms can be classified into two categories: “Boolean to Arithmetic (B2A)” and “Arithmetic to Boolean (A2B)”. The A2B algorithm generally requires more execution time than the B2A algorithm. Using pre-computation tables, the A2B algorithm substantially reduces its execution time, although it requires additional space in RAM. In CHES2012, B. Debraize proposed a conversion algorithm that somewhat reduced the memory cost of using pre-computation tables. However, they still require ( 2 ( k + 1 ) ) entries of length ( k + 1 ) -bit where k denotes the size of the processed data. In this paper, we propose a low-memory algorithm to convert A2B masking that requires only ( 2 k ) ( k ) -bit. Our contributions are three-fold. First, we specifically show how to reduce the pre-computation table from ( k + 1 ) -bit to ( k ) -bit, as a result, the memory use for the pre-computation table is reduced from ( 2 ( k + 1 ) ) ( k + 1 ) -bit to ( 2 k ) ( k ) -bit. Second, we optimize the execution times of the pre-computation phase and the conversion phase, and determine that our pre-computation algorithm requires approximately half of the operations than Debraize’s algorithm. The results of the 8/16/32-bit simulation show improved speed in the pre-computation phase and the conversion phase as compared to Debraize’s results. Finally, we verify the security of the algorithm against side-channel attacks as well as the soundness of the proposed algorithm.

Decentralized attribute-based conjunctive keyword search scheme with online/offline encryption and outsource decryption for cloud computing

In recent years, the increasing popularity of cloud computing has led to a trend that data owners prefer to outsource their data to the clouds for the enjoyment of the on-demand storage and computing services. For security and privacy concerns, fine-grained access control and secure data retrieval for the outsourced data is of critical importance. Attribute-based keyword search (ABKS) scheme, as a cryptographic primitive which explores the notion of public key encryption with keyword search (PEKS) into the context of attribute-based encryption (ABE), can enable the data owner to flexibly share his data to a specified group of users satisfying the access policy and meanwhile, maintain the confidentiality and searchable properties of the sensitive data. However, in most of the previous ABKS schemes, the decryption service is not provided, and a fully trusted central authority is required, which is not practical in the scenario that the access policy is written over attributes or credentials issued across different trust domains and organizations. Moreover, the efficiency of storage and computation is also the bottleneck of implementation of ABKS scheme. In this paper, for the first time, we propose a decentralized ABKS scheme with conjunctive keyword search for the cloud storage system. Besides the multi-keyword search in the decentralized setting, our scheme outsources the undesirable costly operations of decryption to the cloud without degrading the user’s privacy. Furthermore, the encryption phase is also divided into two phases, an offline pre-computation phase which is independent with the plaintext message, access policy, and keyword set, and can be performed at any time when the data owner’s device is otherwise not in use, and an online encryption phase which only incurs very little computation costs. Security analysis indicates that our scheme is provably secure in the random oracle model. The asymptotic complexity comparison and simulation results also show that our scheme achieves high computation efficiency.

Fast maximum weight clique extraction algorithm: Optimal tables for branch-and-bound

A new branch-and-bound algorithm for the maximum weight clique problem is proposed. The proposed algorithm consists of two phases, a precomputation phase and a branch-and-bound phase. In the precomputation phase, the weights of maximum weight cliques in many small subgraphs are calculated and stored in optimal tables. In the branch-and-bound phase, each problem is divided into smaller subproblems, and unnecessary subproblems are pruned using the optimal tables. We performed experiments with the proposed algorithm and five existing algorithms for several types of graphs. The results indicate that only the proposed algorithm can obtain exact solutions for all graphs and that it performs much faster than other algorithms for nearly all graphs.

Linear Round Bit-Decomposition of Secret-Shared Values

In the field of signal processing in the encrypted domain, linear operations are usually easy to perform, whereas multiplications, and bitwise operations like comparison, are more costly in terms of computation and communication. These bitwise operations frequently require a decomposition of the secret value into bits. To minimize the communication complexity, previous studies have focused on solutions that require a constant number of communication rounds, often at the cost of a large number of multiplications. We develop a bit-decomposition protocol within a linear secret sharing system, where sharings of the bits are computed from an integer that is secret-shared among multiple parties. We consider new solutions that require fewer multiplications, but where the number of communication rounds is linear in the input size. Although our basic solution requires m communication rounds to extract the m least significant bits, we present a way of reducing it by an arbitrary factor, using additional precomputations. Given that the best constant round solutions need at least 23 communication rounds, our solution is preferable for integers up to 165 bits, leading to fewer rounds and a smaller number of secure multiplications. In one variant, it is even possible to compute all I bits through only one opening and one additional communication round containing l multiplications, when a precomputation phase of 2 + log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> I rounds and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sup> -l-1 secure multiplications has been performed.

Solving Kakuro Puzzle using Self Adapting Harmony Search Metaheuristic Algorithm

In paper presents a Self Adapting Harmony Search (SAHS) metahauristic based solution to the grid-based Japaneese game know as Kakuro. It has been shown that our approach can provide a time-efficient solution to obtaining a correct positioning of the numbers in the cells of a puzzle, with proper previous filtering of the combination of the numbers in the sets of sums. The pre-computation phase has given great improvements to the SAHS algorithm in order to reduce the search space. The proposed solution has been experimentally evaluated on various grids with different size and the results shown that the algorithm is very robust and always converged to the solution.

High-Order Perturbation Theory for Incident Flux Response Expansion Methods

In this work, a high-order perturbation method is developed to generate/compute response functions for the coarse mesh transport (COMET) method, which provides whole-core neutron transport solutions to various reactor core types. In this approach, the response functions are first generated at a reference eigenvalue point, and the response functions at an arbitrary eigenvalue are computed as a high-order perturbation from the reference point. The method has been tested at both the lattice level (response function generation) and the core level (whole-core transport calculations). At the lattice level, it is found that the response functions predicted by the perturbation method agree very well with those directly computed by the Monte Carlo method. The average relative difference in the surface-to-surface response functions is 0.29% to 0.46% when the eigenvalue k ranges from 0.6667 to 1.5. In whole-core transport calculations, the COMET calculations using the high-order perturbation method are almost identical to those using the interpolation method. The eigenvalue, assembly, and pin fission densities predicted by COMET agree very well with the MCNP reference solution. This indicates that the high-order perturbation response function generation method can achieve the same accuracy as the interpolation method while significantly improving the computational efficiency of the precomputation phase.

Enumeration of monadic second-order queries on trees

We consider the enumeration problem of Monadic Second-Order (MSO) queries with first-order free variables over trees. In Bagan [2006] it was shown that this problem is in CONSTANT-DELAY lin . An enumeration problem belongs to CONSTANT-DELAY lin if for an input structure of size n it can be solved by: —an O ( n ) precomputation phase building an index structure, —followed by a phase enumerating the answers with no repetition and a constant delay between two consecutive outputs. In this article we give a different proof of this result based on the deterministic factorization forest decomposition theorem of Colcombet [2007].

On the generalisation of special moduli for faster interleaved montgomery modular multiplication

In this study, the authors give a generalisation of special moduli for faster interleaved Montgomery modular multiplication algorithm with simplified pre-computational phase for GF(pn ), where p ≥ 2 is a prime number and n is a positive integer. The authors propose different sets of moduli that can be used in elliptic curve crytographic applications and pairing-based cryptography. Moreover, this method also leads to efficient implementations for the elliptic curve parameters given in standards. It is shown that one can obtain efficient Montgomery modular multiplication architecture in view of the number of AND gates and XOR gates by choosing proposed sets of moduli. The authors eliminate final substraction step with proposed sets of moduli. These methods are easy to implement for hardware.