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Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Finding heavy-elements (heavy-hitters) in streaming data is one of the central, and well-understood tasks. Despite the importance of this problem, when considering the sliding windows model of streaming (where elements eventually expire) the problem of finding L 2 -heavy elements has remained completely open despite multiple papers and considerable success in finding L 1 -heavy elements. Since the L 2 -heavy element problem doesn't satisfy certain conditions, existing methods for sliding windows algorithms, such as smooth histograms or exponential histograms are not directly applicable to it. In this paper, we develop the first polylogarithmic-memory algorithm for finding L 2 -heavy elements in the sliding window model. Our technique allows us not only to find L 2 -heavy elements, but also heavy elements with respect to any L p with 0 < p ≤ 2 on sliding windows. By this we completely “close the gap” and resolve the question of finding L p -heavy elements in the sliding window model with polylogarithmic memory, since it is well known that for p > 2 this task is impossible. We demonstrate a broader applicability of our method on two additional examples: we show how to obtain a sliding window approximation of the similarity of two streams, and of the fraction of elements that appear exactly a specified number of times within the window (the α -rarity problem). In these two illustrative examples of our method, we replace the current expected memory bounds with worst case bounds. [ABSTRACT FROM AUTHOR]

Abstract: Similarity measure plays an important role in many image processing fields. This paper introduces the medium mathematic systems, and establishes a novel image medium similarity measure between two pixels and that of between two image sets based on the measure of medium truth degree. Moreover, an image edge detection algorithm, an image matching algorithm and an image medium fidelity measure method governed by the medium similarity measure are discussed in this paper. Experimental results show that the proposed similarity measure is effective. [Copyright &y& Elsevier]

Abstract: This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance. [Copyright &y& Elsevier]

Several sampling criteria had been proposed for C 2 smooth surfaces such that the reconstructed meshes from point samples are homeomorphic to the original surfaces. In this paper, based on a widely used sample criterion, we present proofs that give the upper and lower bounds of mesh quality (in terms of several triangle aspect ratios) for the reconstructed mesh. To make the proposed theoretical bounds useful in practical applications with real-world point data, we propose a novel mesh reconstruction method that works in three steps: (1) approximate Delaunay mesh reconstruction; (2) point data upsampling and (3) hole filling. Finally, examples are presented, which illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Dynamic graph theory is a novel, growing area that deals with graphs that change over time and is of great utility in modeling modern wireless, mobile and dynamic environments. As a graph evolves, possibly arbitrarily, it is challenging to identify the graph properties that can be preserved over time and understand their respective computability. In this paper we are concerned with the treewidth of dynamic graphs. We focus on metatheorems , which allow the generation of a series of results based on general properties of classes of structures. In graph theory two major metatheorems on treewidth provide complexity classifications by employing structural graph measures and finite model theory. Courcelle's Theorem gives a general tractability result for problems expressible in monadic second order logic on graphs of bounded treewidth, and Frick and Grohe demonstrate a similar result for first order logic and graphs of bounded local treewidth. We extend these theorems by showing that dynamic graphs of bounded (local) treewidth where the length of time over which the graph evolves and is observed is finite and bounded can be modeled in such a way that the (local) treewidth of the underlying graph is maintained. We show the application of these results to problems in dynamic graph theory and dynamic extensions to static problems. In addition we demonstrate that certain widely used dynamic graph classes have bounded local treewidth. [ABSTRACT FROM AUTHOR]

Recent research shows that significant energy saving can be achieved in wireless sensor networks (WSNs) with a mobile base station that collects data from sensor nodes via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. In this paper we study the problem of finding a data collection path for a mobile base station moving along a fixed track in a wireless sensor network to minimize the latency of data collection. The main contribution is an O ( m n log ⁡ n ) expected time algorithm, where n is the number of sensors in the networks and m is the complexity of the fixed track. [ABSTRACT FROM AUTHOR]

In this paper, we study 1-space bounded 2-dimensional bin packing and square packing. A sequence of rectangular items (square items) arrive one by one, each item must be packed into a square bin of unit size on its arrival without any information about future items. When packing items, 90°-rotation is allowed. 1-space bounded means there is only one “active” bin. If the “active” bin cannot accommodate the coming item, it will be closed and a new bin will be opened. The objective is to minimize the total number of bins used for packing all items in the sequence. Our contributions are as follows: For 1-space bounded 2-dimensional bin packing, we propose an online packing algorithm with a tight competitive ratio of 5.06. A lower bound of 3.17 on the competitive ratio is proven. Moreover, we study 1-space bounded square packing, where each item is a square with side length no more than 1. A 4.3-competitive algorithm is achieved, and a lower bound of 2.94 on the competitive ratio is given. All these bounds surpass the previously best known results. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an efficient feature extraction method, named local structure preserving discriminant analysis (LSPDA), is presented. LSPDA constructs the local scatter and the between-class scatter to characterize the sub- and multi-manifold information respectively. More specifically, the local structure is constructed according to a certain kind of similarity between data points which takes special consideration of both the local information and the class information based on a parameter-free neighborhood decision rule, and the between-class structure is constructed according to the importance degrees of the not-same-class points measured by a strictly monotonically decreasing function. After the local scatter and the between-class scatter have been characterized, the novel feature extraction criterion is derived via maximizing the difference between the between-class scatter and the local scatter. Experimental results on the Wine dataset, AR, FERET, CMU PIE, ORL and LFW face databases show the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper describes the theoretical principles of a verification method with proof scores in the CafeOBJ algebraic specification language. The verification method focuses on specifications with conditional equations and realizes systematic theorem proving (or interactive verification). The method is explained using a simple but instructive example, and the necessary theoretical foundations, which justify every step of the verification, are described with precise mathematical definitions. Some important theorems that result from the definitions are also presented. [Copyright &y& Elsevier]