Reviewerly/exHarmony huggingface dataset:
Document Classification Based on semantic and Improved Convolutional Neural Network

In order to improve the accuracy of text classification, we present a new convolution neural network model combining keyword and word-meaning transformation. We first preprocess the text and break words, and use sense labeling for semantic keywords and word-meaning transformation. and we divide the texts into two parts---word and word-meaning. Next, we use embedding layer to transform the word and word-meaning into corresponding word embedding. Then, we use improved convoluted neural network to train the model and extract higher-order features of text type data, and use multi-layer perceptron and SoftMax layer to classify the texts to predict the category of each text. Experimental results show that our document classification algorithm can get a high accuracy and the effect of classification of news topic detection gets well.
Attribute-Based Identity Authentication Scheme Based on Linear Codes

Abstract In the multi-user application environment, the simple use of user private key to achieve identity authentication is too single and can not carry out fine-grained access control to users. And there is the problem of user key management, which is easy to produce system bottleneck. In order to solve the above problems, a traceable and revocable attribute-based authentication scheme is constructed based on linear codes, which divides user access rights in a fine-grained manner. The method of direct revocation is adopted in the scheme, and the cost of revocation is lower. When a key abuse occurs or the system suffers a denial of service attack, the true identity of the malicious user can be tracked based on the signature. Finally, the MBDH problem proves that the scheme meets unforgeability, and the performance comparison and numerical analysis show that the scheme has a shorter key length and less computational overhead.
The one-cop-moves game on graphs with some special structures

This paper considers the one-cop-moves game played on a graph. In this game, a set of cops and a robber occupy the vertices of the graph and move alternately along the graph's edges with perfect information about each other's positions. The goal of the cops is to capture the robber. At cops' turns, exactly one cop is allowed to move from his location to an adjacent vertex; at robber's turns, she is allowed to move from her location to an adjacent vertex or to stay still. We want to find the minimum number of cops to capture the robber. This number is known as the cop number. In this paper, we investigate the cop number of several classes of graphs, including graphs with treewidth at most 2, Halin graphs, and Cartesian product graphs. We also give a characterization of k-winnable graphs in the one-cop-moves game.
Computing Maximal Lyndon Substrings of a String

There are two reasons to have an efficient algorithm for identifying all right-maximal Lyndon substrings of a string: firstly, Bannai et al. introduced in 2015 a linear algorithm to compute all runs of a string that relies on knowing all right-maximal Lyndon substrings of the input string, and secondly, Franek et al. showed in 2017 a linear equivalence of sorting suffixes and sorting right-maximal Lyndon substrings of a string, inspired by a novel suffix sorting algorithm of Baier. In 2016, Franek et al. presented a brief overview of algorithms for computing the Lyndon array that encodes the knowledge of right-maximal Lyndon substrings of the input string. Among those presented were two well-known algorithms for computing the Lyndon array: a quadratic in-place algorithm based on the iterated Duval algorithm for Lyndon factorization and a linear algorithmic scheme based on linear suffix sorting, computing the inverse suffix array, and applying to it the next smaller value algorithm. Duval’s algorithm works for strings over any ordered alphabet, while for linear suffix sorting, a constant or an integer alphabet is required. The authors at that time were not aware of Baier’s algorithm. In 2017, our research group proposed a novel algorithm for the Lyndon array. Though the proposed algorithm is linear in the average case and has O(nlog(n)) worst-case complexity, it is interesting as it emulates the fast Fourier algorithm’s recursive approach and introduces τ-reduction, which might be of independent interest. In 2018, we presented a linear algorithm to compute the Lyndon array of a string inspired by Phase I of Baier’s algorithm for suffix sorting. This paper presents the theoretical analysis of these two algorithms and provides empirical comparisons of both of their C++ implementations with respect to the iterated Duval algorithm.
Frequent Itemsets Methods for Text Clustering

Text clustering is a crucial application of data mining. It can be used to structure hypertext documents or large sets of text. Many research works have dived into document clustering as a technique for improving search, information retrieval, document browsing, automatic topic identification, as well as the primitive task of clustering. Major challenges are entangling researchers, especially when working with large scale datasets, such as very high dimensionality and cluster labeling. To tackle these challenges, a number of techniques using frequent itemsets mining methods in text clustering have been proposed. In this paper, we review such techniques while highlighting their strengths and limitations. With the analysis of associated methodologies, we also propose a general framework for the task of text clustering using frequent itemsets mining algorithms.
Heuristic Approach Performances for Artificial Neural Networks Training

This chapter aimed to evaluate heuristic approach performances for artificial neural networks (ANN) training. For this purpose, software that can perform ANN training application was developed using four different algorithms. First of all, training system was developed via back propagation (BP) algorithm, which is the most commonly used method for ANN training in the literature. Then, in order to compare the performance of this method with the heuristic methods, software that performs ANN training with genetic algorithm (GA), particle swarm optimization (PSO), and artificial immunity (AI) methods were designed. These designed software programs were tested on the breast cancer dataset taken from UCI (University of California, Irvine) database. When the test results were evaluated, it was seen that the most important difference between heuristic algorithms and BP algorithm occurred during the training period. When the training-test durations and performance rates were examined, the optimal algorithm for ANN training was determined as GA.
Modeling Transition Matrix for a Collaborative Rating Prediction Recommendation System via Nonnegative Tensor Decomposition

We investigate the problem of user preferences changing over time for a collaborative rating prediction recommendation system. The rating data of a time-aware collaborative recommendation can be represented as a 3rd order tensor, each of which denotes users, items and time. We mainly focus on establishing a user preference transfer model according to the time latent factor matrice, which is computed by tensor decomposition. Subsequently, a novel nonnegative tensor decomposition with temporal similarity (TS-NTD) is put forward to predict rating values. The proposed temporal similarity measure succeeds in describing various changes in user preferences. We evaluate the TS-NTD on two real world datasets, and experimental results show that our model has higher accuracy than state-of-the-art models.
Research on the industrialization mode development and case evaluation system of major scientific and technological achievements of power sensor

The ultimate purpose of the accumulation of scientific and technological achievements is to form a product industrial chain. The products in the laboratory stage can only be used as technical experience, not bring high profits. Only by assigning the products to the market value can bring company and the country new profit growth points and generate income. In view of the weak sensor core industry in China at present, this paper puts forward the development mode of power sensor industrialization from the perspective of electricity, and forecasts the input-output efficiency ratio of the mode development by using the data envelopment analysis method, and comprehensively evaluates the development trend of power sensor industrialization in recent years. Through the analysis and calculation results of this paper, it has a strong guiding significance for power enterprises in sensor industry planning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> .
Investments in Russian online education (2016-2018)

The subject of this research is investments in development of online education in Russia. The object of this research is online education in the current conditions of digital economy. The author analyzes the dynamics of investments in capital assets and federal expenditure over the period from 2016 to 2018. The growing role of online education on the Russian education market is indicated. Analysis is conducted on the increased costs of education development. The conclusions are formulated on need for attracting private investments in online educational projects, as well as active development of public-private partnership. It is underlined that although federal expenditure (investments) on education is increasing, it is clearly insufficient and requires attraction of private investments through development of public-private partnership. The following conclusions were made: 1) the government develops mechanisms of public-private partnerships; 2. Only regional investment funds remain in Russia; the Investment Fund of the Russian Federation no longer exists, since its creation on the federal level was not justifiable; 3. Russian education increases its rating on the international level, which influence the development of higher education in the Russian Federation along with export of education; 4. Internet usage among Russian population is uneven. The scientific novelty consists in the attempt to determine the trends in export of Russian education.
Constraint Inversion Model of Core Science Complex Network

Abstract In order to reduce the research costs, increase the efficiency of scientific researches, and improve the inversion abilities of core technologies, in the paper, a constrained inversion model aiming at tracking the source of core technology was proposed. Firstly, a complex network was built and it treated the most important product or technology as the core node vector. In the network, the social and technical attributes of the product were assigned as the elements of the core node vector. Therefore, the relationships among the core node and other nodes represented connective strength which were fairly important for calculating the path weights. Essentially, the weights were integrated attributes derive from a called score matrix. Then a constrained optimization criterion was employed to evaluate the scores of paths so that the best optimal path was determined. For reducing the number of searching and the times of calculating, all the constraints relationships of node vectors were classified into several types. The experimental results demonstrate that the proposed approach has a superior performance against the current state-of-the art methods over a real case.
Signature Forgery Recognition Using CNN

This paper presents the recognition of hardwritten signatures. This is troublesome as even the human eye does not have that much visual capacity to distinguish everything in the manually written signature. It is hard for people to recognize the original and the fashioned ones. By utilizing profound realization which utilizes the refined reproduction of human cerebrum, we can recognize the fraud done in signature with higher precision. Confirmation of signature may be achieved using either offline or online mode, depending on the program. Digital systems use contextual data from a target taken at the moment of making the target. Disconnected structures hack away at the mark’s image tested. A methodology for offline signature authentication actually utilizes a number of simple geometric highlights based on form. The highlights that are considered are area, eccentricity, center of gravity, pressure, pen up/down, and inclination. Before extricating the highlights, preprocessing of a filtered picture is important to detach the marked part and to expel any fake commotion present.
Does This Code Change Affect Program Behavior? Identifying Nonbehavioral Changes with Bytecode

Developers occasionally conduct some source code changes that do not affect program behavior. We call such changes nonbehavioral changes. In this research, we propose a technique for determining whether a given commit includes only nonbehavioral changes or not by checking the differences of bytecode on the commit. If the bytecode is not affected by the commit, the proposed technique determines that the commit includes only nonbehavioral changes. As a result of experiments on six Java open source projects, out of the commits in which Java source files were changed, the commits of 8.6~22.4% consisted of only nonbehavioral changes. We also found new 25 patterns of nonbehavioral changes compared to a previous study.
Sliding Covariance Matrix: Co-learning Spatiotemporal Geometry Feature for Skeleton Based Action Recognition

The covariance matrix is a generic feature representation in vision applications. It can accurately and efficiently capture geometric features of Riemannian manifold especially in the condition of data size is medium-scaled. When the covariance matrix is applied in describing skeleton data, how to represent spatial and temporal relations of skeleton joints, meanwhile ensuring the matrix is nonsingular is a challenging problem. In this work, we first propose a sliding window-based frame appending model acquiring a nonsingular covariance matrix descriptor for all skeleton frames. Then, sliding covariance matrixes for all sliding windows are sequentially fed to the modified Long Short-Term Memory (LSTM) network for extracting the spatiotemporal characteristics and action recognition. The proposed method is verified by the experiments on five medium-sized skeleton datasets and the results show that the proposed method improves the accuracy by 6%–20% compared to the state-of-the-art models. Meanwhile, the experiment results clarify that when the data size is not so large, our proposed method can describe spatiotemporal characters of skeleton data more accurately and efficiently than deep network methods.
Analysis of Steady-State Underwater Electric Field Characterization in Coastal Sea Areas

Recently, the study of underwater electric field has been attracted much attention that have interesting applications in a variety of measurement devices and equipment for marine electric field underwater. After the laboratory measurement of the underwater electric field being completed, a series of offshore verification in the coastal area are required to further verify the performance of test equipment in general. Therefore, the relevant electric field characterization of the coastal area needs to be measured explicitly. In this paper, we carries out a series of environmental observations in a certain coastal sea area of Dalian by using a long baseline observation device and obtains some important conclusions through statistical analysis, which has certain guiding significance for the study of the law of the low-frequency underwater electric field in specific sea area.
Unified Target-based Sentiment Analysis by Dual-pointer Tagging Scheme

Target-based sentiment analysis aims at extracting opinion targets and predicting their sentiment polarities from a given sentence. Previous researches have been trying to joint the two sub-task in a single architecture. However, the target extraction schemes of these approaches are facing various issues, such as ignoring the sentiment consistency of composite targets, or being too sensitive or time-consuming in heuristic search. We propose a model on top of the BERT encoder by designing a dual-pointer binary classifier for target extraction and a joint multi-class classifier for sentiment classification. Experiments on two datasets show the effectiveness of our model.
Philosophy Teaching Management Under the Information Environment

With the progress of the information age, “Internet + education” will trigger a global education reform and build a new form of education, and “Internet + teaching” is the core component of the “Internet + education” reform. “Internet + teaching” is not just the networking and digitization of teaching, but a structural change in the teaching paradigm. This article aims to explore the theoretical paradigm of the teaching paradigm under the impetus of “Internet + teaching” based on literature research and practice reflection. This paper expounds and analyzes the transformation and innovation of teaching philosophy, teaching theory, and teaching mode under the impetus of “Internet + philosophy teaching” from the three levels of philosophical paradigm, sociological paradigm, and artificial paradigm. The article believes that “Internet + philosophy teaching” will accelerate the transformation of the teaching paradigm of the information age, and the Internet thinking based on ecological postmodernism will transform the philosophical paradigm of traditional teaching and lead the systematic transformation of teaching concepts, theory and practice. This paper hopes to provide a basis for systematic exploration and practical innovation of the new paradigm theory of “Internet + philosophy teaching”.
Isomap Based on Cat Swarm Algorithm with t-distribution Parameters

ISOMAP algorithm of machine learning is a kind of nonlinear dimensionality reduction algorithm based on manifold learning. In the ISOMAP algorithm, KNN is used to construct the nearest neighbor graph of the sample set, calculate the shortest path, and MDS algorithm is used to obtain the objective function, so as to solve the minimum value problem in the objective function and complete the dimensionality reduction of data. Gradient descent method or stochastic gradient descent is often used to solve the problem of the minimum value. However, the gradient descent method or the stochastic gradient descent method is easy to trap into local minimum. The main purpose of this paper is to give a global optimization algorithm, mainly is cat swarm algorithm of swarm intelligence algorithm, instead of gradient optimization algorithm, in order to overcome its shortcomings, to obtain better global convergence and convergence speed..
Rewrites as Terms through Justification Logic

Justification Logic is a refinement of modal logic where the modality is annotated with a reason s for “knowing” A and written . The expression s is a proof of A that may be encoded as a lambda calculus term of type A, according to the propositions-as-types interpretation. Our starting point is the observation that terms of type are reductions between lambda calculus terms. Reductions are usually encoded as rewrites essential tools in analyzing the reduction behavior of lambda calculus and term rewriting systems, such as when studying standardization, needed strategies, Lévy permutation equivalence, etc. We explore a new propositions-as-types interpretation for Justification Logic, based on the principle that terms of type are proof terms encoding reductions (with source s). Note that this provides a logical language to reason about rewrites.
A Progressive Approach for Computing the Earth Mover’s Distance

Earth Mover's Distance (EMD) is defined as the minimum cost to transfer the components from one histogram to the other. As a robust similarity measurement, EMD has been widely adopted in many real world applications, like computer vision, machine learning and video identification. Since the time complexity of computing EMD is rather high, it is essential to devise effective techniques to boost the performance of EMD-based similarity search. In this paper, we focus on deducing a tighter lower bound of EMD, which still remains the bottleneck of applying EMD in real application scenarios. We devise an efficient approach to incrementally compute the EMD based on the primal-dual techniques from linear programming. Besides, we further propose progressive pruning techniques to eliminate the dissimilar results as well as enable early termination of the computation. We conduct extensive experiments on three real world datasets. The results show that our method achieves an order of magnitude performance gain than state-of-the-art approaches.
A new weighted fuzzy c-means based on the collective behaviour of starling birds

In this paper, a new weighted fuzzy c-means clustering algorithm is proposed. The presented approach consists of emulating the collective behaviour of starling birds to form homogeneous and well-separated clusters. In a flock of starlings, each individual maintains a connection with its neighborhood to determine its position in space. This connection allows the individual to approach the flock-mates that have the same direction as its own, and simultaneously, to move away from other individuals that have a different direction. Based on this metaphor, in this work, we propose the use of the three elementary movements of the starling bird, separation, alignment, and cohesion, to update the weight parameter associated with each individual in the dataset. The accuracy of the proposed algorithm has been assessed by two clustering validation indices. Compared to some existing algorithms, our algorithm provides better results. An example of artificial data is used to contrast some cases of this approach.
Reliability Enhancement of Wireless Sensor Network Using Error Rate Estimation (ERE) Method

Wireless sensor networks play an important role to incorporate various sensors. The reliability offers a stable and trusted system. In the proposed work, reliability is improved by using error rate estimation (ERE) method. The experimental setup is carried out under three conditions such as indoor, open air, and hall. This will incorporate the overall conditions from moderate to extreme conditions. The temperature, humidity, and light intensity are measured on different interval of time. The sensor nodes readings were recorded via Wi-Fi module of the sensor node. Firstly, error is measured, and then deviation is computed. The condition-based reliability is evaluated after that the overall reliability is measured. Significant and practical reliability is achieved in the proposed work.
Hierarchical Multitask Learning for Improved Underwater Recognition on Imbalanced Tasks

Deep learning methods for automatic visual inspection can help detect potential anomalies in real-time operations and reduce the burden of full-length video inspection analysis. Such learning-based methods require, however, huge amounts of diverse labeled examples to be trained on. The lack of variance in examples from less frequent entities (eg. anomalies) makes it difficult for deep models to generalize such tasks, leading to poor performance. We then propose to use multitask learning to improve the generalization capacity, thus the performance of deep classification models at the recognition of less frequent entities in the underwater domain. In particular, we exploit hierarchical relationships between classification tasks to wisely choose which tasks to combine. For the imbalanced task labet, our approach is shown to improve multitask performance from 0.74 to 0.89, in terms of average precision. Efficiency is also improved, with inference time being reduced by almost fifty percent, thus making it significantly cheaper to use visual inspection models in realtime operations.