Reviewerly/exHarmony huggingface dataset:
Neural Networks for Modeling Metabolic Pathways

Metabolic pathways are enzyme-catalyzed reactions that can be described by the Michaelis-Menten equation. The inherent problem with Michaelis-Menten kinetics is that the model is not reversible. Reverse catalysis, which is common in metabolic pathways, can be better described by Cell Communication Protocol© which offers a Self-Organizing Map (SOM) for the visualization of the enzyme-catalyze reactions. Neural gas, an algorithm for finding optimal data, is added to the SOM and allows for an increase in the weight space to handle the potential complexity of enzyme-catalysis with the use of back propagation, the system allows for the modeling of reverse reactions.
K-mean Index Learning for Multimedia Datasets

Currently, one method to deal with the storage and computation of multimedia retrieval applications is an approximate nearest neighbor (ANN) search. Hashing algorithms and Vector quantization (VQ) are widely used in ANN search. So, K-mean clustering is a method of VQ that can solve those problems. With the increasing growth of multimedia data such as text view, image view, video view, audio view, and 3D view. Thus, it is a reason that why multimedia retrieval is very important. We can retrieve the results of each media type by inputting a query of that type. Even though many hashing algorithms and VQ techniques are proposed to produce a compact or short binary codes. In the real-time purposes the exhaustive search is impractical, and Hamming distance computation in the Hamming space suffers inaccurate results. The challenge of this paper is focusing on how to learn multimedia raw data or features representation to search on each media type for multimedia retrieval. So we propose a new search method that utilizes K-mean hash codes by computing the probability of a cluster in the index code. The proposed employs the index code from the K-mean cluster number that is converted to hash code. The inverted index table is constructed basing on the K-mean hash code. Then we can improve the original K-mean index accuracy and efficiency by learning a deep neural network (DNN). We performed the experiments on four benchmark multimedia datasets to retrieve each view such as 3D, image, video, text, and audio, where hash codes are produced by K-mean clustering methods. Our results show the effectiveness boost the performance on the baseline (exhaustive search).
Effect of System Utterances Conveying Understanding Results in Chat-Oriented Dialogue Systems

In dialogue systems, conveying the understanding results of user utterances is important because it enables the users to feel understood by the system. However, it is not clear which types of understanding results should be conveyed to users; some utterances may be offensive, for example, and some may be too commonsensical. In this paper, we explore the effect of conveying the understanding results of user utterances in a chat-oriented dialogue system by an experiment with human subjects. We created system utterances conveying various understanding results and then investigated which types of results were favorable and unfavorable through manual evaluation. We found that understanding results referring to a user’s internal state were unfavorable, while those related to user’s positive attributes were likely to be favorable. In addition, understanding results related to objective facts about users or general facts unrelated to users were favorable. These findings can be used as a guideline for constructing a dialogue system that conveys appropriate understanding results.
The Commemorative Internet Project as Means of Collective Identity Formation: the Development of an Optimal Content Strategy (on the Example of "The Memory Book of the Komi Republic")

The article summarizes the experience in effective search, selection, organization of scientific expertise and representation of the verbal and visual content of the Internet depository "The Memory Book of the Komi Republic". A description of the structure of a separate account is given, samples of filling in the information fields of the database are substantiated, and practical recommendations are made.
Meta Generalized Network for Few-Shot Classification

Few-shot classification aims to learn a well generalized model with very limited labeled examples. There are mainly two directions for this aim, namely, meta- and metric-learning. Meta learning trains models in a particular way to fast adapt to new tasks, but it neglects variational features of images. Metric learning considers relationships among same or different classes, however on the downside, it usually fails to achieve competitive performance on unseen boundary examples. In this paper, we propose a Meta Generalized Network (MGNet) that aims to combine advantages of both meta- and metric-learning. There are two novel components in MGNet. Specifically, we first develop a meta backbone training method that learns a flexible feature extractor and a classifier initializer efficiently, delightedly leading to fast adaption to unseen few-shot tasks without overfitting. Second, we design a trainable adaptive interval model to improve the cosine classifier, which increases the recognition accuracy of hard examples. We train the meta backbone in the training stage by all classes, and fine-tune the meta-backbone as well as train the adaptive classifier in the testing stage. We evaluate MGNet on three standard image recognition benchmarks, and experimental results validate the superiority over recent competitive methods.
Unsupervised Multi-Task Domain Adaptation

With abundant labeled data, deep convolutional neural networks have shown great success in various image recognition tasks. However, these models are often less powerful when applied to novel datasets due to a phenomenon known as domain shift. Unsupervised domain adaptation methods aim to address this problem, allowing deep models trained on the labeled source domain to be used on a different target domain (without labels). In this paper, we investigate whether the generalization ability of an unsupervised domain adaptation method can be improved through multi-task learning, with learned features required to be both domain invariant and discriminative for multiple different but relevant tasks. Experiments evaluating two fundamental recognition tasks-image recognition and segmentation-show that the generalization ability empowered by multi-task learning may not benefit recognition when the model is directly applied on the target domain, but the multi-task learning setting can boost the performance of state-of-the-art unsupervised domain adaptation methods by a non-negligible margin.
On equivalence of PFP-operator and PFP-quantifier

Abstract In this paper we consider two different logic languages. Both of them are extensions of first order logic. The first semantics is obtained by adding a partial fixed point operator. The second semantics is based on considering a partial fixed point as a non-standard quantifier. For this two semantics we demonstrate that they have an equal expressive power. For this purpose we show how to express an arbitrary formula of one logic with a formula of another.
Research on Data Analysis Model of Weapon Equipment Acquisition Information Network

The Weapon Equipment Acquisition Information Network(WEAIN) is an important platform for opening and exchange the whole society in the field of weapon and equipment construction in China. It is a model of interaction between national defense construction and social resources, and plays an important role in gathering superior resources to participate in equipment construction. In order to describe the status and function of WEAIN website objectively, and maximize the connecting value of the bridge between the military and the society, the data composition and characteristics of WEAIN website are analyzed firstly. Then the data analysis requirements are put forward. Lastly, the multidimensional data analysis models and text analysis models of WEAIN website are established, which provide means support and model basis for mastering the effect of the website and the development trend of competitive equipment acquisition systematically and accurately.
Range Guided Depth Refinement and Uncertainty-Aware Aggregation for View Synthesis

In this paper, we present a framework of view synthesis, including range guided depth refinement and uncertainty-aware aggregation based novel view synthesis. We first propose a novel depth refinement method to improve the quality and robustness of the depth map reconstruction. To that end, we use a range prior to constrain the estimated depth, which helps us to get more accurate depth information. Then we propose an uncertainty-aware aggregation method for novel view synthesis. We compute the uncertainty of the estimated depth for each pixel, and reduce the influence of pixels whose uncertainty are large when synthesizing novel views. This step helps to reduce some artifacts such as ghost and blur. We validate the performance of our algorithm experimentally, and we show that our approach achieves state-of-the-art performance.
Antenna Modeling Based on Two-Stage Gaussian Process Considering Sensitivity Information

For antenna modeling and optimization design, high-fidelity full-wave electromagnetic simulation software can generally be used to obtain training samples.However, this process takes a long time.To solve this problem, a two-stage Gaussian process (GP) considering electromagnetic sensitivity information is proposed.Based on the coarse grid and fine grid of electromagnetic simulation software, the first stage learns the mapping relationships of antenna performance and sensitivity of each parameter, simultaneously.In the second stage, the accurate surrogates are established based on the mapping relationships above obtained.Because the method in this study takes sensitivity information into consideration during the modeling process, it can better reflect the mapping relationship between antenna input and output, which can develop a more accurate surrogate model.The proposed two-stage GP surrogate model considering sensitivity information can significantly reduce the demand of high-fidelity training samples for modeling, and greatly save the simulation time of calling electromagnetic simulation software.Therefore, this method is more suitable for the problems of insufficient electromagnetic simulation samples.The proposed approach is evaluated by modeling and optimization of an inverted F antenna and an ultra-wideband planar monopole antenna.The results show that the method has high modeling accuracy with limited training samples given by high-fidelity electromagnetic simulation, which further verify its effectiveness and efficiency.
PROBLEMS OF APPLICATION OF MARKETING TECHNOLOGIES AND TOOLS IN HIGHER EDUCATION ORGANIZATIONS

The article considers marketing technologies and tools used in the educational process of higher educational institutions. The paper emphasizes specifics and determines conditions of their application. The authors indicate new realities in which educational organizations find themselves and designate the need for changes in the conduct of educational activities. The study argues the expediency of using marketing technologies and tools in educational activities due to the active development of the market of educational services. The article defines the directions of activity of the educational organization of higher education where it is necessary to use marketing technologies and tools. The paper highlights a number of problems related to the introduction and use of marketing technologies and tools in the educational process of higher educational institutions. The authors formulate conclusions on the need for the use of marketing technologies and tools in higher education organizations.
A Control Approach Based on Observers of State and Uncertainty for a Class of Takagi–Sugeno Fuzzy Models

Although the Takagi–Sugeno fuzzy model is effective for representing the dynamics of a plant to be controlled, two main questions arise when using it just as other models: 1) how to deal with the gap, which is referred to as uncertainty in this study, between the model and the concerned plant, and how to estimate the state information when it cannot be obtained directly, especially with the existence of uncertainty; 2) how to design a controller that guarantees a stable control system where only the estimated state is available and an uncertainty exists. While the existing studies cannot effectively observe the state and the resulting control systems can only be managed to be uniformly stable, this study first presents a state observer capable of precisely estimating the state regardless of the existence of uncertainty. Then, based on the state observer, an uncertainty observer is derived, which can track the trajectory of uncertainty whenever it occurs in a real system. Finally, a controller based on both observers is presented, which guarantees the asymptotic stability of the resulting control system.
The Common RNN Based Models with Sequence Tagging

Sequence tagging is a very important and basic research direction in the field of natural language processing. The performance of many top-level NLP tasks largely depends on the accuracy and reliability of sequence tagging. Correlation model has made a significant progress in this field. At present, the focus of research is how to make better use of the correlation and timing between data to sequence tagging tasks on the premise of ensuring robustness, so as to achieve further performance. In this paper, we summarize the highlights and breakthrough research results and research models in the field of sequence tagging in recent years, mainly from the models, data sets, parameters and performance. At the same time, we summarize the existing problems and look forward to the future.
CAN: Complementary Attention Network for Aspect Level Sentiment Classification in Social E-Commerce

Customer's reviews play an important role in social e-commerce. It can help merchants establish word-of-mouth and adjust marketing strategies. Because of a large number of customer's reviews, it is difficult to analyze the sentimental polarities of the reviews in time. Aspect level sentiment classification is designed to identify sentimental tendency of the aspect in the sentence. In the previous works, averaged vector was used to represent the original vector usually, but it may lose information when the aspect has multiple words. In order to solve the aforementioned issue, we propose the Complementary Attention Network(CAN), which incorporates the average pooling mechanism, circular convolution method and circular correlation method to complement the information in the averaged vector. We evaluate our model on three datasets: laptop reviews, restaurant reviews and twitter posts. Experiments show that our model is advanced and have achieved excellent performance on multiple datasets. In specifically, we compare different variants of CAN model. The CAN model we proposed achieved better performance on three datasets, especially outperforming A-CAN(attention mechanism) by 3% on laptop reviews.
Informatics and Human Creation

Creative work (as well as creativity itself) is one of the attributes of the human essence. The creation characterizes the consciousness and self-consciousness of the person to the same degree as the reflection of reality does. Art and scientific cognition are the kinds of creation that play an especially large role in the development of world civilization and culture. Information is a basis in both of them; however, intuition is of peculiar importance as well. Modern informatics gives researchers considerable methodological means for investigation of creation as a particular phenomenon of human activity.
Risk assessment of electricity transmission and distribution tariff regulation for power grid enterprises based on best-worst method and cloud model

The electricity transmission and distribution tariff policy of the second supervision cycle in China has formulated a much better electricity transmission and distribution tariff supervision system. In this context, the research on the risk related to electricity transmission and distribution tariff regulation faced by power grid enterprises is helpful for power regulatory agencies and business operators to identify and avoid risks in time and promote the sustainable development of electric power industry. Firstly, the risk evaluation criteria system is established. Secondly, a risk evaluation model based on the best and worst method (BWM) and cloud model for electricity transmission and distribution tariff regulation is proposed. Finally, the risk level of power transmission and distribution tariff regulation faced by four provincial power grid enterprises is evaluated. The validity and practicability of the proposed model in this paper are proved by the empirical analysis.
Concordance Techniques in Lossless Data Compression of Text Files

This paper aims to perform a comparative analysis, based on experimental results obtained in practical tests, of the correlation that can be identified between a transform and an encoding algorithm for lossless compression of data from text files. At the same time, the paper offers the user the opportunity to test these concordance techniques, allowing him/her to draw personal conclusions about the most appropriate combination of methods in order to obtain best performance in a specific data context. Finally, some recommendations are offered on the optimal concordance between transform-based methods and encoding algorithms for lossless text compression.
Modal Θ-valent linear codes with complementary modal Θ-valent duals

In this paper, we define the notion of modal Θ-valent linear codes with complementary modal Θ-valent duals (mΘ LCD) and give their algebraic characterization.
Mobile Robot Path Planning Optimization Based on Integration of Firefly Algorithm and Quadratic Polynomial Equation

The rapid expansion in the industrial revolution has been aroused to utilize of the robot in various high accuracy and productivity applications. Optimal path planning is an important concern in navigation of mobile robot interesting with finding an optimal path. The firefly algorithm is considered as an increasingly promising tool of Swarm Intelligence, which is used in various optimization areas. The objective of the proposed method is to find the free-collision points in mobile robot environment and generate the optimal path based on firefly algorithm and D* algorithm. D* algorithm has been applied to find the shortest path. The essential function of applying the firefly algorithm and Quadratic parametric equations is to iterative globally search and determine the accurate positions of a set of intermediate via points within the free space and generating the corresponding smooth trajectory at a specified time respectively. The collision with obstacles can be efficiently avoided by constructing the free Cartesian space, which guarantee a free collision path planning. The simulation results illustrate the efficiency of proposed methods performance in finding and determining the optimal path and trajectory even in various degrees of environmental complexity.
Sparse-Dense Subspace Clustering

Subspace clustering refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Current subspace clustering approaches are usually based on a two-stage framework. In the first stage, an affinity matrix is generated from data. In the second one, spectral clustering is applied on the affinity matrix. However, the affinity matrix produced by two-stage methods cannot fully reveal the similarity between data points from the same subspace, resulting in inaccurate clustering. Besides, most approaches fail to solve large-scale clustering problems due to poor efficiency. In this paper, we first propose a new scalable sparse method called Iterative Maximum Correlation (IMC) to learn the affinity matrix from data. Then we develop Piecewise Correlation Estimation (PCE) to densify the intra-subspace similarity produced by IMC. Finally we extend our work into a Sparse-Dense Subspace Clustering (SDSC) framework with a dense stage to optimize the affinity matrix for two-stage methods. We show that IMC is efficient for large-scale tasks, and PCE ensures better performance for IMC. We show the universality of our SDSC framework for current two-stage methods as well. Experiments on benchmark data sets demonstrate the effectiveness of our approaches.
SKILLS OF TEACHER’S QUESTIONING IN ENGLISH CLASSROOM INTERACTION AT SMA NEGERI 1 KUALA

This study was about skills of teacher’s questioning in English classroom interaction. It was aimed to investigate skills of teacher’s questioning used by the English teacher in classroom interaction. The participant of this research was English teacher of SMA Negeri 1 Kuala. The technique of data collection used was observation, video recording, field note and interview. The research finding showed that there are 9 skills that used by the English teacher i.e. structuring, phrasing, redirecting, distributing, pausing, reacting, prompting and changing the level of cognitive demand. Distributing questions were the most dominantly used by the English teacher with 39%.  Redirecting 21%, reacting 13%, pausing 8%, focusing 7%, structuring 4%, changing the level of cognitive demand 4%, phrasing 3% and prompting 1%. Moreover, there were six reasons why teacher ask question in English classroom interaction, they were stimulate and maintain student’s interest, encourage students to think and focus on the content of the lesson, enable teacher to clarify what student has said, enable teacher to elicit particular structures and vocabulary items, enable teacher to check students’ understanding, and encourage students’ participation in a lesson. Related suggestions are given to the English teacher on how to achieve better questions. The study is beneficial for both teacher and students in English classroom interaction. Keywords : Skills of Teacher’s Questioning, Teacher’s Reasons, Classroom Interaction.
GenTree: Using Decision Trees to Learn Interactions for Configurable Software

Modern software systems are increasingly designed to be highly configurable, which increases flexibility but can make programs harder to develop, test, and analyze, e.g., how configuration options are set to reach certain locations, what characterizes the configuration space of an interesting or buggy program behavior? We introduce GenTree, a new dynamic analysis that automatically learns a program's interactions - logical formulae that describe how configuration option settings map to code coverage. GenTree uses an iterative refinement approach that runs the program under a small sample of configurations to obtain coverage data; uses a custom classifying algorithm on these data to build decision trees representing interaction candidates; and then analyzes the trees to generate new configurations to further refine the trees and interactions in the next iteration. Our experiments on 17 configurable systems spanning 4 languages show that GenTree efficiently finds precise interactions using a tiny fraction of the configuration space.
The Formation of a Culture of Professional Outlook on the Basis of Forseit Technologies in Cluster Conditions

The article is theoretically based on the importance of the system of professional ideals of the student's professional outlook, the intellectual and emotional approach to professional activity in the integration of the activities of foresight centers in a pedagogical cluster environment.