Reviewerly/exHarmony huggingface dataset:
Rule-Based HierarchicalRank: An Unsupervised Approach to Visible Tag Extraction from Semi-structured Chinese Text

The large and growing amounts of semi-structured Chinese text present both challenges and opportunities to enhance text mining and knowledge discovery. One such challenge is to automatically extract a small set of visible tag from a document that can accurately reveal the document’s topic and can facilitate fast information processing. Unfortunately, at this stage, there is still a certain gap between the existing methods and truly engineering application. In order to narrow this gap, we propose Rule-Based HierarchicalRank (RBH), an unsupervised method for visible tag extraction from semi-structured Chinese text via a documents’ title and non-title two levels. In different level, we use inconsistent methods to extract the candidate visible tags. The experiment results show that the performance of the RBH method is far better than all the baseline methods on visible tag extraction task on two distinct experiment datasets. Specifically, On Paper-Dataset, the rule-based HierarchicalRank methods’ precision and F1-score achieves 18.6% and 14.1%, while TOP K = 5. In addition, on Event-Dataset, the best precision of our method is higher 7% than the state-of-the-art method PositionRank with TOP K = 1. Furthermore, the best Recall of RBH achieves 37.7% when TOP K = 5.
Design of Big Data Intelligent Sharing System for the Creative Achievement of College Students in Internet+ Age

Aiming to improve the ability of big data to share the creative achievements of college students in the Internet+ age, and to build a big data intelligent sharing system for college students' creative achievements in the Internet+ age, a design method of big data intelligent sharing system is proposed based on multi-layer architecture for college students' creative achievements in the Internet+ age. The multi-level big data fusion method is used to design the algorithm of big data intelligent sharing of the creative achievements of college students in the Internet+ age, and the statistical characteristics of big data of the creative achievements of college students in the Internet+ age are extracted. The multi-layer template feature matching method is used to combine big data's creative achievements in the Internet+ age with fuzzy clustering, and the intelligent sharing multi-layer architecture model of big data, the creative achievements of college students in the Internet+ age, is established. Under the structure system of BES, we construct the bottom database of big data intelligent sharing system, which is the creative achievement of college students in the Internet+ age. The program loading and automatic control of big data intelligent sharing system for university students' creative achievements is carried out by cross-compiling method. Under the embedded multi-level architecture, the software development and design of big data intelligent sharing system for college students' creative achievements are realized. The test results show that the system can effectively realize the intelligent sharing of the creative achievements of college students in the Internet+ age big data, the information fusion degree is higher.
NUMBER OF QUESTIONS FOR CREATING ONLINE QUIZZES

This article presents results of a study of the opinion of former and current students on issues related to the evaluation with e-tests. The experience of lecturers conducting assessment in technical and computer disciplines with tests in e-learning environment Moodle from Faculty of Technics and Technologies of Yambol is also analyzed. Analyzes and research carried out show that the progress of the technologies and their entry into education leads to one negative effect. Some of the students use digital technologies to access exam test questions and learn only these questions rather than the teaching content. Conclusions are made on the minimum number of questions from which the tests should be formed. Measures have been proposed for easy and fast automated raising of the total number of questions in the bank by developing Plugins in Moodle, allowing the creation of new types of test questions.
Boosting Variational Generative Model via Condition Enhancing and Lexical-Editing

Conditional Variational Autoencoder (CVAE) has shown promising performance in text generation. However, CVAE is inadequate to generate sentences that are highly coherent to its condition due to error accumulation in decoding and KL-vanishing problem. In this paper, we propose an Edit-CVAE (ECVAE) in which we attempt to exploit information-related data to address the problem by (1) explicitly editing the generated sentence. (2) enriching the latent representation. While maintaining the diversity and information consistency. Experiment results on dialogue and Chinese poetry generation show that our method substantially increases generative coherence while maintaining the diversity and information consistency.
An Improved Distance Metric Clustering Algorithm for Association Rules

Abstract By mining association rules in large data, we can reveal useful information contained in the data and find out the relationship between things or the law of motion. However, because of the huge transaction data, the association rules obtained by mining are complex and massive. It is difficult to find useful association relations, especially when the re-demand is uncertain. To solve this problem, this paper first uses Apriori algorithm to mine association rules from a data set, then defines similarity measure between association rules, and applies DBSCAN clustering algorithm to association rules analysis. The analysis results show that this method is effective in association rules analysis.
The Analysis of Academic Communication Based on Co-Author Perspective - Take Library and Information Science as an Example

In terms of academic communication, this paper explores whether there is an "academic circle" and the close academic communication through the module division of co-authors. The frequency distribution of academic communication is explored through the regression of the number and frequency of co-authorship between two authors. Through the statistics of the number of coauthors of each paper over time, the changes in the breadth of academic communication were explored. This paper selects the literature data of the journals of library and information science from 2010 to 2018 for experiments. The results show that in the past 10 years, the exchange center of scholars in library and information field has been significantly centralized, which meets zipf's law, the exchange frequency distribution meets rotka's law, and the academic exchange has increased in breadth.
Indonesian Elders in Assisted Living

This paper describes a design journey that began with a cultural probe exploration of the lived experiences of residents of a home for elderly women in Jogjakarta, Indonesia. The paper describes how requirements for a tangible experience installation were brought together and how an initial design concept was imagined. Having bought into a technology solution, the design team encountered problems that lead to some conflict that stalled the project. With the passage of time, the teams were able to come to a new solution. This paper reflects on the journey and offers some insights for others working in similar situations.
Humanitarian framework of modern pedagogical education in Russia

The goal of the paper is to review and classify current trends and outcomes of modern pedagogical education in Russia on the back of humanitarization of higher education as well as to justify principles of humanitarization of higher education content. The relevance of this subject stems from current shifts of socio-economic environment in Russia, which have brought new humanitarian paradigms to the forefront. The authors examine pedagogical process as a humanitarian phenomenon pursuing the aim to specify the goals and objectives of modern pedagogical education. The article highlights current demand to develop and promote pedagogical tools of humanitarian training as a model based on cultural practices which consequently implies structuring of the education content and process according to a person’s nature, mind and spirit, contributing to the integrity of a human’s inner world and the development of a whole person.
Modeling and Decoding Complex Problem Solving Process by Artificial Neural Networks

It is hypothesized that the process of complex problem solving in human brain consists of two basic phases, namely, planning and execution. In this study, we propose a computational model in order to verify this hypothesis. For this purpose, we develop a holistic approach for decoding the planning and execution phases of complex problem solving, using the functional magnetic resonance imaging data (fMRI), recorded when the subjects play the Tower of London (TOL) game. In the first step of the proposed study, we estimate a brain network, called Artificial Brain Network (ABN), by designing an artificial neural network, whose weights correspond to the edge weights of the brain network established among the anatomic regions. Then, we decode the planning and execution tasks of complex problem slowing by training a multi-layer perceptron. It is shown that the edge weights of the artificial brain network capture the functional connectivity among anatomic brain regions. When trained on the edge weights of brain networks extracted from average BOLD activation of anatomical regions, the proposed model successfully discriminates the planning and execution phases of complex problem solving process. We compare the suggested computational brain network model to the state of the art models reported in the literature and observe that the decoding performance of the suggested model is better then the available methods in the literature.
Using Centroid Keywords and Word Mover's Distance for Single Document Extractive Summarization

This paper presents unsupervised method of single document extractive summarization. The main idea behind the method is in selecting sentences based on Word Mover's Distance Similarity between each sentence and set of centroid keywords. This approach leverages both compositional property of word embeddings and advantages of recently discovered powerful text to text distance metric. ROUGE results on DUC 2002 data set showed that quality of produced summaries can compete with well-known state of the art systems. In this work we also discuss limitations of gold summaries in evaluating quality of summarization systems.
Scientific and methodological activities of the modern music teacher

This article reveals the role of methodological work and scientific and methodological activities in the professional and personal development of a music teacher and the improvement of his social status. The professional and pedagogical activity of a modern music teacher is viewed as a complex structure, where scientific and methodological activity is given a special place. The author raises the problem of developing the methodological competence of a music teacher, emphasizing the importance of special training in the field of scientific and pedagogical research and the conditions necessary to enhance its scientific and methodological potential.
Extended Abstract: Expanding Digital Audience Analysis Techniques to Include Social Media Analytics (SMA)

This project explores what is meant by “digital audience analysis” as the concept has evolved. The author explores traditional forms of audience analysis and explains how these techniques have expanded to include digital audiences such as users of software, online documentation, and websites. Then, the author explores how social media further expands the idea of digital audience analysis to include social media analytics (SMA) in the form of structured and unstructured data. Another approach to social media analysis addresses context rather than analytics by closely examining user-generated data for evidence of user stories, self descriptions, and key questions. Together the expansions illustrate a taxonomy of digital audience analysis techniques that may be useful for professional communicators.
Classifier Learning from Imbalanced Corpus by Autoencoded Over-Sampling

Class imbalance is a common problem in classifier learning but it is difficult to solve. Textual data are ubiquitous and their analytics have great potential in many applications. In this paper, we propose a solution to build accurate sentiment classifiers from imbalanced textual data. We first establish topic vectors to capture local and global patterns from a corpus. Synthetic minority over-sampling technique is then used to balance the data while avoiding overfitting. However, we found that residue overfitting is still prominent. To address this problem, we propose an autoencoded oversampling framework to reconstruct balanced datasets. Our extensive experiments on different datasets with various imbalanced ratios and number of classes have found that our approach is sound and effective.
Demo: Accelerating Depth-Map on Mobile Device Using CPU-GPU Co-processing

With the growing use of smartphones, generating depth-map to accompany user acquisitions is becoming increasingly important for both manufacturers and consumers. Depth from Small Motion (DfSM) has been shown to be suitable approach since depth-maps can be generated with minimal effort such as handshaking motion, and without knowing camera calibration parameter. Direct porting of a desktop PC implementation of DfSM on mobile devices propose a major challenge due to its long execution time. The algorithm has been designed to run on desktop computers that have higher energy-efficient optimizations compared to mobile device with slower processors. In this paper, we investigate ways to speed up the DfSM algorithm to run faster on mobile devices. After porting the algorithm to the mobile platform, we applied several optimization techniques using mobile CPU-GPU co-processing by exploiting OpenCL capabilities. We evaluate the impact of our optimizations on performance, memory allocation, and demonstrate about 3 $$\times $$ speedup over mobile CPU implementation. We also show the portability of our optimizations by running on two different ANDROID devices.
SSRF Vulnerability Attack and Prevention Based on PHP

This paper firstly introduced the SSRF and the reason of its formation, and a simple demonstration with PHP codes was made. Then, four attack types of SSRF and their combination with other WEB vulnerabilities were introduced and analyzed. And then, the introduction of two kinds of SSRF preventive measures was made and the general defense flow chart was listed. Web security is an important field of Network Space Security, while SSRF is also one of the top 10 vulnerabilities in web security. So, its analysis is of practical significance.
Research on Data Interruption Anomaly Detection of Multi-Period Power System Based on Cloud Computing

In order to solve the common problems of high interference and poor detection accuracy in the current multi-period power system data anomaly detection process, a cloud computing-based multi-period power system data interruption anomaly detection method is proposed. Combined with feature acquisition, the abnormal data features are collected in and out, and the acquired feature parameters are de-noised and frequency modulated by genetic algorithm. The final time limit is used to detect the abnormal interruption of data in multi-period power system. Finally, the experiment proves that the anti-interference performance of the method is obviously improved compared with the traditional detection method, and the accuracy of the detection result can be effectively guaranteed.
A New Monarch Butterfly Optimization Algorithm with SA Strategy

Monarch butterfly optimization (MBO) is a newly proposed meta-heuristic algorithm for solving optimization problems. It has been proved experimentally that MBO is superior to the other five state-of-the-art meta-heuristic algorithms on most test problems. This paper presents a new improved MBO algorithm with Simulated Annealing (SA) strategy, in which the SA strategy is involved into the migration operator and butterfly adjusting operator. So the newly proposed algorithm SAMBO not only accepts all the butterfly individuals whose fitness are better than their parents’ but also randomly selects some individuals that are worse than their parents to disturbance the convergence of algorithm. In the final, the experiments are carried out on 14 famous continuous nonlinear functions; the results demonstrate that SAMBO algorithm is significantly better than the original MBO algorithm on most test functions.
Centralized Reasoning Translation and Its Computing Complexity for Heterogeneous Semantic Mappings

Bridge rules provide an important mechanism for describing semantic mapping and propagating knowledge for distributed dynamic description logics (D3L). The current research focuses on the homogeneous bridge rules that only contain atomic elements. In this paper, the research is extended to the D3L reasoning problem with heterogeneous bridge rules that contain composite elements in subset ends. The regularity of the distributed knowledge base is defined. Through the alteration of the bridge rules and transforming different forms into existing language mechanisms, we present an algorithm that can convert the D3L knowledge base with dynamic description logic $$ {\mathcal{DSROIQ}} $$ as the local ontology language into a single $$ {\mathcal{DSROIQ}} $$ knowledge base. Next, we study the properties of the algorithm. We prove that the algorithm will terminate in polynomial time and that the satisfiability of the target knowledge base is equivalent to the satisfiability of the original knowledge base. Thus, we prove that the worst-case time complexity of the centralized reasoning on the regular D3L knowledge base with such bridge rules is the same as that on a single $$ {\mathcal{DSROIQ}} $$ knowledge base. The method proposed in this paper makes centralized reasoning for D3L obtain the same worst-case time complexity as the existing distributed reasoning method and solves the problem that the latter cannot handle heterogeneous composite bridge rules.
Encoding Epistemic Strategies for General Game Playing

We propose a general approach for encoding epistemic strategies for playing incomplete information games. A game strategy involves selecting actions in order to maximise an outcome (e.g., winning the game). In an epistemic strategy the selection of actions is based on reasoning about the knowledge of other players. We show how epistemic strategies can be encoded by supplementing a GDL-II game description with a set of epistemic rules to produce a GDL-III game that an appropriate reasoner can use to play the original GDL-II game. We prove the formal correctness of this approach and provide a practical evaluation to show its efficacy for playing the co-operative multi-player game of Hanabi. It was found that the encoded epistemic rules were able to provide players with a strategy that allowed them to play Hanabi near optimally.
First-Order Quasi-canonical Proof Systems

Quasi-canonical Gentzen-type systems with dual-arity quantifiers is a wide class of proof systems. Using four-valued non-deterministic semantics, we show that every system from this class admits strong cut-elimination iff it satisfies a certain syntactic criterion of coherence. As a specific application, this result is applied to the framework of Existential Information Processing (EIP), in order to extend it from its current propositional level to the first-order one—a step which is crucial for its usefulness for handling information that comes from different sources (that might provide contradictory or incomplete information).
Research on RFID Information Security Technology Based on Elliptic Curve Algorithms

The security problem of RFID system is a great potential security hazard in its application. Due to the limitation of hardware conditions, traditional public key cryptography can not be directly used in security mechanism. Compared with the traditional RSA public key cryptography, the elliptic curve cryptography has the advantages of shorter key, faster processing speed and smaller storage space, which is very suitable for use in the RFID system.
A data privacy preservation approach and a case study in data analytics

Data Privacy (DP) has always been an issue and more so today than ever before because of the advanced tools available to take advantage of data for all sorts of reasons including unethical. It has, therefore, become one of the big challenges that Big Data has thrown about in recent years. There are a number of attempts at dealing with DP using mainly data encoding, homomorphic encryption in particular, and other mathematical devices that allow sets to be worked on in place of others for the benefit of getting the same or equivalent solutions. They do, however, have limitations often due to the high dimensionality and extremely large volume of the data. The curse of dimensionality and volume are of course inherent to the concept of Big Data. In this paper, we suggest a new approach that relies on complexity theory and NP-Completeness in particular. We describe our approach and illustrate it on a very common problem in data analytics, namely clustering or unsupervised machine learning. Results and their discussion will be included.
Real Time Object Detection on Aerial Imagery

Aerial images usually are huge (around 2K resolution). Such high-resolution images contain thousands of small objects, and detecting all of them is a very challenging problem. The complexity of detection and classification in real-time is much higher than the usual images (<1K with high Object to Image Ratio OIR). Deep learning has many algorithms for object detection, but they are not designed for handling aerial images, and these algorithms are often sub-optimal for small scale object detection and their precise localization. In this work, a novel technique based on a modified SSD architecture OIR-SSD has proposed for real-time object detection on aerial images attaining high mean Average Precision (mAP). OIR-SSD has two approaches. The approach-I proposed for higher mAP, whereas the approach-II proposed to achieve real-time object detection. The approach-I has improved mAP from 0.72 to 0.92 (28% improvement) on Stanford data-set while from 0.04 to 0.44 (1100% improvement) on Visedrone2018 at 4 Frames Per Second (FPS) whereas the approach-II has improved mAP from 0.72 to 0.82 at 42 FPS.
The Portrait Depiction of the Market Members Based on Data Mining

Aiming at the problem of portrait of members in shopping malls, this paper analyzes the similarities and differences of consumption behaviors between member groups and nonmember groups, and constructs the LRFMC model with [Formula: see text]-means algorithm to analyze the value of membership. Second, active states of members are divided according to the consumption time interval, and KNN algorithm model is established to predict member states and used to predict the membership status. Finally, it discusses which types of goods are more suitable for promotional activities and can bring more profits to the shopping mall.