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In recent years, single device sound scene classification has achieved good results, however, progress in multi-device sound scene classification has been slow. To solve the problem of large differences in the number of multi-device samples, this paper proposed a pairwise feature fusion algorithm. By calculating the differences in the spectrogram for each pair of paired samples and averaging these differences after accumulation, obtaining the average spectral characteristics of each device for the conversion of device samples. The algorithm effectively improved the generalization ability of the model while increasing the number of device samples. Meanwhile, in order to obtain global information, it proposed a lightweight attention module, which could make the model focus on the training of the whole sound sequence information on the basis of reduced computation by performing self-attention operations on the input features after compression in the frequency domain. The experimental results show that the proposed algorithm has better advantages in terms of model size and classification accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

Efficient solution of the single source shortest path (SSSP) problem on road networks is an important requirement for numerous real-world applications. This paper introduces an algorithm for the SSSP problem using compression method. Owning to precomputing and storing all-pairs shortest path (APSP), the process of solving SSSP problem is a simple lookup of a little data from precomputed APSP and decompression. APSP without compression needs at least 1TB memory for a road network with one million vertices. Our algorithm can compress such an APSP into several GB, and ensure a good performance of decompression. In our experiment on a dataset about Northwest USA (with 1.2 millions vertices), our method can achieve about three orders of magnitude faster than Dijkstra algorithm based on binary heap. [ABSTRACT FROM AUTHOR]

In this paper, a compressed membership problem for finite automata, both deterministic (DFAs) and non-deterministic (NFAs), with compressed transition labels is studied. The compression is represented by straight-line programs (SLPs), i.e. context-free grammars generating exactly one string. A novel technique of dealing with SLPs is employed: the SLPs are recompressed, so that substrings of the input word are encoded in SLPs labelling the transitions of the NFA (DFA) in the same way, as in the SLP representing the input text. To this end, the SLPs are locally decompressed and then recompressed in a uniform way. Furthermore, in order to reflect the recompression in the NFA, we need to modify it only a little, in particular its size stays polynomial in the input size. Using this technique it is shown that the compressed membership for NFA with compressed labels is in NP, thus confirming the conjecture of Plandowski and Rytter (Jewels Are Forever, pp. 262-272, Springer, Berlin, ) and extending the partial result of Lohrey and Mathissen (in CSR, LNCS, vol. 6651, pp. 275-288, Springer, Berlin, ); as this problem is known to be NP-hard (in Plandowski and Rytter, Jewels Are Forever, pp. 262-272, Springer, Berlin, ), we settle its exact computational complexity. Moreover, the same technique applied to the compressed membership for DFA with compressed labels yields that this problem is in P, and this problem is known to be P-hard (in Markey and Schnoebelen, Inf. Process. Lett. 90(1):3-6, ; Beaudry et al., SIAM J. Comput. 26(1):138-152, ). [ABSTRACT FROM AUTHOR]

This paper continues the STACS contribution by Diekert, Ushakov, and the author as well as the IJAC publication by the same authors. We extend the results published there in two ways. First, we show that the data structure of power circuits can be generalized to work with arbitrary bases q≥2. This results in a data structure that can hold huge integers, arising by iteratively forming powers of q. We show that the properties of power circuits known for q=2 translate to the general case. This generalization is non-trivial and additional techniques are required to preserve the time bounds of arithmetic operations that were shown for the case q=2. The extended power circuit model permits us to conduct operations in the Baumslag-Solitar group BS(1, q) as efficiently as in BS(1,2). This allows us to solve the word problem in the generalization H(1, q) of Higman's group in polynomial time. The group H(1, q) is an amalgamated product of four copies of the Baumslag-Solitar group BS(1, q) rather than BS(1,2) in the original group H= H(1,2). As a second result, we allow arbitrary numbers f≥4 of copies of BS(1, q), leading to an even more generalized notion of Higman groups H(1, q). We prove that the word problem of the latter can still be solved within the ${\mathcal {O}}(n^{6})$ time bound that was shown for H(1,2). [ABSTRACT FROM AUTHOR]

With the development of deepfake technology, deepfake detection has received widespread attention. Although some deepfake forensics techniques have been proposed, they are still very difficult to implement in real-world scenarios. This is due to the differences in different deepfake technologies and the compression or editing of videos during the propagation process. Considering the issue of sample imbalance with few-shot scenarios in deepfake detection, we propose a multi-feature channel domain-weighted framework based on meta-learning (MCW). In order to obtain outstanding detection performance of a cross-database, the proposed framework improves a meta-learning network in two ways: it enhances the model's feature extraction ability for detecting targets by combining the RGB domain and frequency domain information of the image and enhances the model's generalization ability for detecting targets by assigning meta weights to channels on the feature map. The proposed MCW framework solves the problems of poor detection performance and insufficient data compression resistance of the algorithm for samples generated by unknown algorithms. The experiment was set in a zero-shot scenario and few-shot scenario, simulating the deepfake detection environment in real situations. We selected nine detection algorithms as comparative algorithms. The experimental results show that the MCW framework outperforms other algorithms in cross-algorithm detection and cross-dataset detection. The MCW framework demonstrates its ability to generalize and resist compression with low-quality training images and across different generation algorithm scenarios, and it has better fine-tuning potential in few-shot learning scenarios. [ABSTRACT FROM AUTHOR]

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. Moreover, most of the data collected in many problems seem to have some inherent properties that lend themselves to natural groupings. Clustering algorithms are used extensively not only to organize and categorize data, but are also useful for data compression and model construction. This paper reviews on types of clustering techniques- k-Means Clustering, Hierarchical Clustering, DBScan clustering, Optics, EM Algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The Longest Common Subsequence Problem is the problem of finding a longest string that is a subsequence of every member of a given set of strings. It has applications in FPGA circuit minimization, data compression, and bioinformatics, among others. The problem is NP-hard in its general form, which implies that no exact polynomial-time algorithm currently exists for the problem. Consequently, inexact algorithms have been proposed to obtain good, but not necessarily optimal, solutions in an affordable time. In this paper, a hyper-heuristic algorithm incorporated within a constructive beam search is proposed for the problem. The proposed hyper-heuristic is based on two basic heuristic functions, one of which is new in this paper, and determines dynamically which one to use for a given problem instance. The proposed algorithm is compared with state-of-the-art algorithms on simulated and real biological sequences. Extensive experimental reveals that the proposed hyper-heuristic is superior to the state-of-the-art methods with respect to the solution quality and the running-time. [Copyright &y& Elsevier]

In this paper, a new approach to solving a problem of steganography algorithm stability to cover-attack is proposed. The algorithm is based on the general approach to analysis of state and functioning of information system which was previously developed by the author. Consequently, we obtain the sufficient conditions for low-sensitivity of stegano message to compression while ensuring the reliability of perception. The proposed approach automates the process of analyzing the existing or emerging steganography algorithms from viewpoint of its resistance to compression. This became possible due to estimation of singular values perturbation of cover's matrix during the stegano transformation process. Additionally the proposed algorithm allows a priori selection of less sensitive to compression stegano messages from their variety. [ABSTRACT FROM AUTHOR]

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms. [ABSTRACT FROM AUTHOR]