Reviewerly/exHarmony huggingface dataset:
A Research on Overlapping Relationship Extraction Based on Multi-objective Dependency

The joint extraction of entity and relation is an important task in information extraction. Previously, most models in entity relationship extraction assumed that the relationship was discrete. Unfortunately, this assumption is often violated. In order to solve the problem of overlapping in the entity relationship, considering the relationship between extraction under the premise of have the features of multiple targets, this paper puts forward a multi-objective depend on the relationship between extraction model, which transforms the relationship extraction task into a sequence-tagged task. The model uses Iterated Dilated Convolutional Neural Network (IDCNN) and BiLSTM to encode the words in order to more fully extract the semantics in the text. First, determine the target entity subject (s), and then predict all corresponding object (o) and relationship (r) according to s. Experiments show that our model is significantly better than the baseline methods.
Completely Unsupervised Cross-Modal Hashing

Cross-modal hashing is an effective and practical way for large-scale multimedia retrieval. Unsupervised hashing, which is a strong candidate for cross-modal hashing, has received more attention due to its easy unlabeled data collection. However, although there has been a rich line of such work in academia, they are hindered by a common disadvantage that the training data must exist in pairs to connect different modalities (e.g., a pair of an image and a text, which have the same semantic information), namely, the learning cannot perform with no pair-wise information available. To overcome this limitation, we explore to design a Completely Unsupervised Cross-Modal Hashing (CUCMH) approach with none but numeric features available, i.e., with neither class labels nor pair-wise information. To the best of our knowledge, this is the first work discussing this issue, for which, a novel dual-branch generative adversarial network is proposed. We also introduce the concept that the representation of multimedia data can be separated into content and style manner. The modality representation codes are employed to improve the effectiveness of the generative adversarial learning. Extensive experiments demonstrate the outperformance of CUCMH in completely unsupervised cross-modal hashing tasks and the effectiveness of the method integrating modality representation with semantic information in representation learning.
Improving Fine-grained Opinion Mining Approach with a Deep Constituency Tree-long Short Term Memory Network and Word Embedding

Objective: One key task of fine-grained opinion mining on product review is to extract product aspects and their corresponding opinion expressed by users. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. Recent years have seen a revival of the Long Short- Term Memory (LSTM), with its effectiveness being demonstrated on a wide range of problems. However, LSTM based approaches are still limited to linear data processing since it processes the information sequentially. As a result, they may perform poorly on user-generated texts, such as product reviews, tweets, etc., whose syntactic structure is not precise. Methods: In this research paper, we propose a constituency tree long short term memory neural network- based approach. We compare our model with state-of-the-art baselines on SemEval 2014 datasets. Results: Experiment results show that our models obtain competitive performances compared to various supervised LSTM architectures. Conclusion: Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support the human decision-making process based on opinion mining results.
An Empirical Investigation into the Effects of Code Comments on Issue Resolution

Comments are beneficial for developers to understand and maintain the code in software development life cycle. Well-commented code can generally help developers to resolve issues efficiently. Due to the complexity of code implementation, code comments may be generated to represent different types of information. And it is hard to keep all the code well-commented in real-world projects. In this case, it is meaningful to investigate how the different types of comments impact the resolution of issues. Then we can maintain the code comments purposefully, and we can also provide some suggestions for the comment generation techniques. To analyze the efforts of different comments on issue resolution, we classify code comments into two categories, i.e., functionality-aspect and non-functionality-aspect comments. In this paper, we analyze the effects of 53k pieces of code comments on the issues from 10 open-source projects within a period of 24 months. The results show that the majority of code comments are used to represent the functionality, e.g., the summary and purpose of code. Nevertheless, the other non-functionality-aspect comments have much stronger correlation with the resolution of software issues. For the resolved patches, the non-functionality-aspect comments are more frequently to be updated or added than the functionality-aspect comments. These findings confirm the important role of non-functionality-aspect comments during issue resolution, although their proportion is far less than that of functionality-aspect comments.
Transmission Line Scene Classification Based on Light-VGGNet

Abstract In recent years, power departments have gradually utilized UAV to carry out regular inspections of transmission lines to ensure the safety and stability of power systems. However, a large number of images without useful information are usually captured during the UAV inspection. These images that contain no transmission line information are transmitted to the ground station with informative images, which leads to the surge of workload. To solve this problem, we propose an intelligent image filtering method based on the VGGNet, which is called Light-VGGNet. Firstly, this paper collects the aerial images captured by cameras during the UAV inspection and builds an aerial dataset of transmission line scenes. Then, the Light-VGGNet is proposed, which achieves much lower memory consumption and faster running speed than those of the VGGNet. We use the aerial image dataset to train the proposed network. Finally, the best weight is loaded and utilized to predict the label of the test dataset.
Distributional Models in the Task of Hypernym Discovery

An approach to the solution of the first task of automatically taxonomy construction for the Russian language is described. This task consists in matching unknown input-words with hypernyms from the existing taxonomy. We show that useful results can be attained using pre-trained distribution models without additional training.
Issues in the Syntactic Parsing of Queries for a Natural Language Interface to Databases

In this chapter, an exhaustive parser is presented. The parser was developed to be used in a natural language interface to databases (NLIDB) project. This chapter includes a brief description of state-of-the-art NLIDBs, including a description of the methods used and the performance of some interfaces. Some of the general problems in natural language interfaces to databases are also explained. The exhaustive parser was developed, aiming at improving the overall performance of the interface; therefore, the interface is also briefly described. This chapter also presents the drawbacks discovered during the experimental tests of the parser, which show that it is unsuitable for improving the NLIDB performance.
Bacterial Foraging Optimization of Speech Coder for Software Defined Radio Application

The paper presents integration of Discrete Wavelet Cosine Transform technique and Bacterial Foraging Algorithm (BFO) for the development and optimization of speech coder. It is depicted how by filtering the limited number of high energy components of transformed coefficients with parallel programming can maintain the speech signal quality in coding over wide range of bit rates. The performance of existing and proposed speech coding techniqueattributes such as compression ratio, coding delay, computational complexity and quality of reconstructed speech is examined for multiple bit rates and compared with other existing speech coding techniques in Matlab environment. The result showsimprovement in performancewith respect to all attributes at the cost of increase in complexity.
Question Answering over Knowledge Base with Symmetric Complementary Attention

Knowledge Base Question Answering (KBQA), which aims to answer natural language questions with structured data from a knowledge base is an important Natural Language Processing (NLP) problem. To answer the question, we need to find the fact from the Knowledge Base whose subject and relation best match the question. Most existing methods treat this task as a pipeline of two separate subtasks: subject matching and relation matching. While ignoring the relevance between them. In this paper, we focus on solving this problem through a joint learning method. We present a neural joint model with a shared encoding layer to learn the two subtasks together to improve each other. In particular, we design a Symmetric Bidirectional Complementary Attention module based on the attention mechanism and the gate mechanism to model the relationship between the two subtasks. The experimental results demonstrate that our approach can obtain higher accuracy than the state-of-the-art method.
Dynamic Feature Combination by Agreement for Image Classification

Abstract Image classification is a basic and important task in computer vision. Recently, various neural networks have been designed and proved to be very powerful models for image classification. It is natural for thinking of how to gather their strengths together, which refers to feature combination tasks. Traditional combination methods mainly focus on designing specific combination algorithms to achieve higher performance. However, few works consider how to utilize their agreement on a given target (for example, a specific class) to achieve better combinations. This paper presents a novel dynamic feature combination method (DFCA) for image classification problems based on the agreement of the individual features. DFCA promisingly takes the agreement of not only the commonalities, but also the individualities of different features by dynamically updating the weighting coefficients of given features using a routing module. Experiment and extensive analysis on CIFAR-10 prove the effectiveness and promising characteristics of the proposed method.
Breast Mass Abnormalities Classification using Architectural Distortions

In breast masses we can use architectural distortion for recounting the arrangement of breast huddle abnormalities in mammograms either as malignant otherwise benign categories. The proposed, designed system assists the radiologists to accurately tag the breast cancer as benign and malignant and also minimizes the false-positive classification rate of breast masses efficiently. Radiologist marks the detected mass abnormality on a mammogram and extracts two textural features architectural distortion and denseness at the marked space. The denseness features furnishes and compute the radiographic heaviness of the distinguished area and the architectural distortion quality gives an estimation of its abnormality and structure. Skew and kurtosis define the distortions in architectural dimension and structure, and with these parametric variations, a benign tumor is differentiated from a malignant tumors MIAS database is taken for this work. The proposed method verified with plenty of experiments and quantitative comparison with other methods achieved better performance, the precision accuracy values of the system 97.43% of the malignant, and 97.36% for the benign images.
Psychological and Pedagogical Support of Educational Adaptation of College Students

The research is based on the need to update the understanding of the adaptive potential of the individual at the stage of professionalization of college students. The study was conducted to analyse the dynamics of educational adaptation on the example of indicators of adaptation in the study group and adaptation to educational activities, using the methodology of T.D. Dubovitskaya and A.V. Krylova. The authors tested the assumption that the result of specially organized psychological and pedagogical support might be an increase in the indicators of educational adaptation of first-year students. The dynamics of educational adaptation was assessed by comparing the indicators of students’ adaptation to educational activities and in the study group, obtained before and after the implementation of psychological and pedagogical support aimed at strengthening the components of self-regulation, developing social communication and skills and skills of working with information. Statistical analysis using the Wilcoxon T-test was used to assess the significance of differences. As a result of the research, based on qualitative and statistical analysis, it can be concluded that as a result of psychological and pedagogical support, students have a positive dynamics of educational adaptation, in particular, indicating that the girls’ adaptation to educational activities is more successful than adaptation in a group. The practical significance of the research is to justify the applicability of the system of psychological and pedagogical support for first-year College students to maintain adaptation to the socio-cultural environment of the university by creating conditions for strengthening the subjective component of educational motivation. It contributes to a better understanding of the effectiveness of their own efforts to work with educational material; creating conditions for strengthening self-regulation skills in the course of psychological counselling of students (as a direction of psychological and pedagogical support), for developing the cognitive potential of the individual and strengthening communication skills in the course of general support. Moreover, the study confirms the possibility of using the methodology for evaluating students’ educational motivation to track its dynamics using both qualitative and quantitative indicators.
Vector-Level and Bit-Level Feature Adjusted Factorization Machine for Sparse Prediction

Factorization Machines (FMs) are a series of effective solutions for sparse data prediction by considering the interactions among users, items, and auxiliary information. However, the feature representations in most state-of-the-art FMs are fixed, which reduces the prediction performance as the same feature may have unequal predictabilities under different input instances. In this paper, we propose a novel Feature-adjusted Factorization Machine (FaFM) model by adaptively adjusting the feature vector representations from both vector-level and bit-level. Specifically, we adopt a fully connected layer to adaptively learn the weight of vector-level feature adjustment. And a user-item specific gate is designed to refine the vector in bit-level and to filter noises caused by over-adaptation of the input instance. Extensive experiments on two real-world datasets demonstrate the effectiveness of FaFM. Empirical results indicate that FaFM significantly outperforms the traditional FM with a 10.89% relative improvement in terms of Root Mean Square Error (RMSE) and consistently exceeds four state-of-the-art deep learning based models.
Research on Full-Scale Measurement of Non-Uniformity of Wind Pressure of Power Transmission Conductor

Conductors of transmission lines are long and have high flexibility, which are obviously influenced by wind loads. The non-uniformity of wind pressure of conductors along the transmission line direction is crucial to the wind-resisting design of power transmission tower-line systems. In this paper, through distributing wind pressure sensors on the whole conductor, the wind load information of the whole conductor is monitored simultaneously. The statistical characteristics and value taking laws of the wind pressure nonuniformity of the conductor are investigated for mean and fluctuating wind, respectively. Results show that by adopting the Generalized Extreme Value distribution model, the probability distribution characteristics of non-uniformity coefficients of mean wind pressure of conductor can be reflected well. The value taking of its probability statistical parameters is stable, which meets ergodic properties of steady-state distribution samples. For the non-uniformity characteristics of fluctuating wind, its spatial correlation coefficient is gradually reduced with the distance increasing; when the distance is increased to about 60 m, in most cases, the decay rate of spatial correlation coefficient is reduced obviously and even reaches the steady state. Meanwhile, the probability distribution of turbulence integral scale meets the characteristics of extreme value distribution, with the mean value of 9.521m. More than 90% samples are distributed within 0-40m.
Online education of Ukrainian students in a pandemic crisis

The COVID-19 coronavirus pandemic and the quarantine imposed in Ukraine on March 12, 2020, have shown the readiness of every higher education institution to respond to today's global challenges and provide quality educational services. The purpose of the article is to reveal the education of students of Borys Grinchenko Kyiv University during quarantine. The system of e-learning of students based on the use of the LMS Moodle platform is covered. A number of problems in the distance learning process during the COVID-19 coronavirus pandemic have been identified. Prospects for the development of distance learning at the Borys Grinchenko Kyiv University have been identified.
WORKS BY A. S. PUSHKIN IN SIBERIANS’ READING DURING THE PRE-REVOLUTIONARY AND CONTEMPORARY PERIODS (TO THE 220 ANNIVERSARY SINCE THE BIRTH OF THE POET)

The number of titles and volumes of works by A. S. Pushkin in city libraries of Siberia during the late imperial period is counted. A. S. Pushkin’s place in the ratings of writers made by the author of the article on a basis of book borrowings in the libraries of Siberian cities at the beginning of the XX century is defined. The demand for works by A. S. Pushkin in Siberians’ reading during the pre-revolutionary period is revealed. The attitude of regional residents towards his works at the end of the XX – the beginning of the XXI century is analyzed.
Comparative Study on Numerical Simulation and Monitoring Data of Bridge Construction Monitoring Phase

Bridge health monitoring is not only a new technology for traditional bridge detection and structural assessment, but also given the significance of structural monitoring and evaluation, design verification and research and development. The basic data acquisition method is to use the high-precision non-destructive sensing technology in the field to analyze the structural system characteristics including structural response, and achieve the functions of structural monitoring and evaluation, design verification and research and development. However, the sensor faces the challenge of being calibrated as the use time is lengthened during use. It is especially difficult to evaluate the uncertainty in the field calibration process. This paper analyzes the bridge in the construction stage by numerical simulation combined with statistical algorithm analysis, and adopts two methods to reduce the error of numerical simulation. It is fitted with the actual monitoring data, and this method is used to compare and analyze the field monitoring data. It provides theoretical support for the uncertainty assessment of the field calibration process.
A Novel Shilling Attack Detection Method Based on T-Distribution over the Dynamic Time Intervals

The recommendation systems have become an important tool to solve the problem of information overload. However, the recommendation system is greatly fragile as it relies heavily on behavior data of users. It is very easy for a host of malicious merchants to inject shilling attacks in order to control the recommendation results. Some papers on shilling attack have proposed the detection methods, but they ignored experimental performance of injecting a small number of attacks and time overhead. To solve above issues, we propose a novel detection method of shilling attack based on T-distribution over dynamic time intervals. Firstly, we proposed Dynamic Time Intervals to divide the rating history of items into multiple time windows; secondly, the T-distribution is employed to calculate the similarity between windows, and the feature of T-distribution is obvious to detect small samples; thirdly, abnormal windows are identified by analyzing the T value, time difference and rating actions quantity of each window; fourthly, abnormal rating actions are detected by analyzing rating mean of abnormal windows. Extensive experiments are conducted. Comparing with similar shilling detection approaches, the experimental results demonstrate the effectiveness of the proposed method.
A Dynamic Object Oriented Approach for Context Aware Services in Distributed Pervasive Healthcare Environment

Ubiquitous Healthcare Environment is a natural object oriented paradigm which deals with different kinds of Man, Machine and Systems. This service oriented pervasive environment dictates the need for effective management of various subsystems namely patient profile, doctor's profile and other clinical and non-clinical activities. New care models have been defined based on intelligent context aware services among different stakeholders such as health operators, patient relatives and social community members. Voluminous and precise nature of data is a real challenge and needs optimal solution for effective storage, retrieval and query schemes. Integration of all the sub systems and various time bound services at different levels and priority makes the system more complex and needs the attention. In this paper, we describe an Object Oriented Database (OODB) model for context aware services in distributed pervasive healthcare environment for providing intelligent healthcare services to the society.
Image Privacy Protection by Particle Swarm Optimization Based Pivot Pixel Modification

The image classification models based on neural networks recently have outperformed most of the traditional models, and rapidly been developed and implemented by industry because of the capability of qualifying various computer vision tasks. Hence, the exposure of users' image data to unauthorized powerful models causes more information leak in a shorter time. Through experiments, we find that for each input image, the change of the image's prediction scores by each pixels' RGB value change is different. Also, the pattern of the sensitivity on each pixel is highly related to the category and composition of the input image. By utilizing this feature, we present Pivot Pixel Noise Generator by Particle Swarm Optimization to generate noise points on original images to lower the target model's accuracy of correctly predicting the target image's label, so to protect the information contained in the target image from the image classification models. The model performs in a semi-black-box manner and balances the number of queries to the target and total number of modified points. We also propose an initialization strategy for the model, PSO Knowledge Transfer, which initializes the model's parameters with experience learned from previous runs to further reduce the number of query times and noise points. The model is evaluated using the image classification benchmark model ResNet50 and shows an advantage compared to the baseline algorithm.