Reviewerly/exHarmony huggingface dataset:
A New Component of Interactive Multi-task Network Model for Emotion-Cause Pair Extraction

Abstract The task emotional-cause pair extraction (ECPE) is proposed based on the emotion cause extraction (ECE) task. The ECPE task proposes a new two-step frame method: first step is to extract emotion and cause information separately, then the second step is to match the emotional cause pair. Compared to the ECE task, this new research no longer relies on manual annotation of documents. However, the Inter-EC model component for the ECPE task does not fully consider the relevance of sentimental clause and causal clause, and the available context is limited. In this paper, we propose a new extraction component with self-attention for the Inter-EC model, optimizing the interactive multi-task network model. Our approach achieved a good recall score on the ECPE dataset, and the F1 value is slightly better than the baseline.
Computer data analysis and processing based on character recognition and deep learning technology

In this review, we present a website we developed. Upload pictures of elementary school students' math papers to our website, and our website will automatically mark the papers for you, and you will finally get an analysis report. We aim to make teachers more effective and help them understand how well children are doing in maths. There are plenty of search apps in China's mobile app market, but websites that help teachers mark papers and analyze test scores are unheard of. Our website has greatly filled the gap in the domestic market, so we believe that our website will have a very good development prospect.
Recognition of human interactions in feature films based on infinite mixture of EDCM

In this paper, we propose a nonparametric Bayesian approach based on a mixture of the exponential-approximation to the Dirichlet Compound Multinomial (EDCM), which has been shown to be very flexible and efficient for high-dimensional sparse count data modeling. Our approach can be viewed as the first attempt to extend the finite EDCM mixture model to the infinite case, which allows the elicitation of prior belief about the parameters and the number of clusters through Markov Chain Monte Carlo sampling within Metropolis-Hastings. This extension provides a natural representation of uncertainty regarding the challenging problem of model selection, where the problems of overfitting and underfitting are prevented due to the nature of the nonparametric Bayesian approach. The resulting statistical model is applied to a challenging application, namely human interaction recognition.
Educational potential of studying recurrence relations in the preparing of prospective mathematics teachers

Abstract The substantive preparing of prospective mathematics teachers includes studying many branches of mathematics. Students encounter a very large number of various mathematical concepts and facts from different disciplines, so it is difficult for them to detect connections between these concepts and facts and to have an idea of the general system of mathematical knowledge. This makes it complicated to successfully study at the university, since many mathematical concepts and facts are assimilated by them formally. In the last three decades the section “recurrence relations” have appeared in almost all discrete mathematics textbooks. However, students encounter recurrent formulas when studying other mathematical disciplines. The experience of studying recurrence relations was analyzed in the article and it was demonstrated that studying recurrence relations has great educational potential for solving these problems and contributes to the higher level of preparing of prospective mathematics teachers. We propose to realize this potential through a system of reference tasks for practical exercises with students. The article presents both well-known problems from various branches of mathematics, and new problems, that we made by ourselves. These tasks can be used as a basis for the development of such a system of reference tasks.
Parallel Planning using a Lazy Clause Generation Solver

Reduction to a sequence of SAT problems has been a main approach for solving parallel planning problems for a long time. However, attempts at using constraint programming (CP) solvers for this purpose have witnessed relatively limited success. We show that existing CP formulations can deliver competitive results when a lazy clause generation solver (which is a hybrid of SAT and CP) is employed. This suggests promise for future cross-fertilization in SAT, CP, and AI planning.
Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia

The welfare of farmers is a strategic problem in Indonesia. The Farmer term of the trade (FTT) is one indicator to measure the welfare of farmers. FTT is a measurement of the comparison of the price index received by farmers (It) with the price index paid by farmers (Ib). Some models for FTT prediction on previous research are using ANN, SVM, MLR, Markov Chain - Predictive Probabilistic Architecture Modeling Framework (P2AMF), Singular Spectrum Analysis (SSA) - ARIMA and ANN-PSO. Previous FFT research in 2018 used three prediction methods, namely using the ANN, SVM and MLR algorithms with the best RMSE being 0.00098. Then in 2019, FTT research was followed by optimization of the ANN parameters using PSO for weighting (ANN-PSO) and obtaining the best RMSE was 0.00062. This study evaluates the robustness of the prediction models of FTTs in the Central Java region using SVM. Then proceed with increasing SVM prediction accuracy using GA (SVM-GA). SVM-GA has resulted in an increase in FTT prediction accuracy. This study has found that the SVM method has better robustness than the ANN method. The development of research related to the accuracy for the FTT prediction model in the Central Java Province has increased, starting from 2018 with RMSE 0.00098; in 2019 with RMSE 0.00062 and the results of this study resulted in the best RMSE of 0.00037.
Deep Learning for the Russian Language

Abstract Deep learning is a term used to describe artificial intelligence (AI) technologies. AI deals with how computers can be used to solve complex problems in the same way that humans do. Such technologies as computer vision (CV) and natural language processing (NLP) are distinguished as the largest AI areas. To imitate human vision and the ability to express meaning and feelings through language, deep learning exploits artificial neural networks that are trained on real life evidence. While most vision-related tasks are solved using common methods nearly irrespective of target domains, NLP methods strongly depend on the properties of a given language. Linguistic diversity complicates deep learning for NLP. This chapter focuses on deep learning applications to processing the Russian language.
Scenario Analysis for Road Following Using JetBot

Recently, the application of Unmanned Ground Vehicle (UGV) has been increasingly prevalent. It can be used in different applications both indoors and outdoors. However, for some scenarios, the Global Positioning System (GPS) may not be always precise and available due to signal attenuation and multi-path propagation. Hence, in this research, we use a novel vision-based architecture, including machine learning and edge computing, to analyze the geometric features to find the path. Furthermore, we also intend to perform a scenario analysis for obtaining the setting with better performance. According to the experimental results, the prototype demonstrates the appropriate performance and properties.
A Novel Compact Rule Extractor Based on Genetic-Fuzzy Algorithm

Extracting effective rules in medical data with two indicators of accuracy and high interpretability is essential in increasing the accuracy and speed of diagnosis by specialists. As a result, the production of decision support systems that are able to detect data-driven rules play a vital role in the early detection of disease, even in areas where there is no access to a specialist. In this paper, a novel automatic rule extractor is presented using a hybrid model consisting of fuzzy logic and evolutionary algorithm. Fuzzy systems are suitable for making diagnostic models due to the high interpretability of their rules. The genetic algorithm is used to automatically generate these rules. To evaluate the proposed method, Pima Diabetes dataset including 768 records and 9 variables was used. The accuracy of the proposed model on the PIMA dataset was 77.12%. This is achieved by 7 fuzzy rules with an average length of 2.1, using three linguistic variables that represent low, normal and high values of each of the independent variables. All membership functions are the same width. According to the three criteria of low number of rules, short rule length and symmetric membership functions with the same width, the proposed method is quite suitable for extraction of compact rule base with high accuracy and interpretability in medical data.
Physical Education as the Basis of Students' Personality Development

The article considers the process of studying the correlation between the development of personal qualities, the level of physical training of students in universities and the attitude of students to physical education classes. Physical education is one of the main ways of educating modern youth, which should harmoniously combine moral purity and spiritual wealth, physical perfection and psychological purposefulness. It has an impact on increasing the social and labor activity of students during their study at university. An active and healthy person keeps youth for a long time, continuing creative activities, not allowing “the soul to be lazy”. Methods. To achieve the research purposes, we used a specially developed questionnaire on the impact of physical education on the health and attitudes of boys and girls to the subject. The experiment was conducted on the basis of the Sevastopol State University. The sample of probationers from the 1st year of study were 205 students (100 boys and 105 girls). The results of the study showed that 42% of the 1st year students chose to conduct physical education in the educational format, 37% of them are against compulsory classes, and 21% are neutral about physical education. Statistics showed that 60% attend classes only to get a «credit», 33% are interested in studying and gaining knowledge in their sport, and 7% do not attend classes well for various reasons. The authors come to the conclusion that the requirements for graduates of educational institutions presuppose the development of new competencies by teachers that allow them to organize the educational process qualitatively and develop a positive attitude of students to physical education.
Deep learning for fast bronze inscription image retrieval

Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive accuracy.
Deep Learning-Based Facial Landmarks Localization with Loss Comparison

Facial landmarks localization has widespread use in various facial analysis applications which can solve major problems in the computer vision field. Localizing the key points such as eye corners, eye centers, nose center, jawline, etc gives the vital information needed for analysis of face status like expressions, health conditions, etc. The paper addresses a deep learning-based facial landmarks localization approach. MobileNet V3 is used to predict the facial landmarks on human faces which are mapped on the detected face in real-time. The model is evaluated with the wing and adaptive wing loss functions. The detector is examined with various head poses and occlusion conditions.
The Analysis of HTTPS Privacy Protection Based on Malicious Code Injection

HTTPS protocol is utilized to protect personal privacy in network communication by man-in-the-middle attacks. However, after the browser is attacked, this protection mechanism will be destroyed. This paper analyzes the vulnerability of https protocol under two scenarios of operating system and network data transmission. Based on the untrusted situation, the possibility of implementing HTTPS protocol injection and page injection on the browser side was analyzed. A traffic monitoring and detection scheme based on dynamic rules is proposed, which can effectively detect whether the browser is injected with malicious code. False positive is less than 10%.
Application of Fuzzy Answer set Programming in Hybrid Deep Neural-Symbolic Architecture

Hybrid deep neural-symbolic architecture for event-detection employs a deep neural network at the back-end to perform low-level reasoning and a symbolic logical module to perform high-level cognitive reasoning. The currently known hybrid architectures use classical Answer Set Programming(ASP), which is unable to perform fuzzy reasoning with uncertainty. Moreover these systems don't extract new rules from the available data. On the other hand, there are neuro-fuzzy systems that can extract fuzzy rules from data by means of Gaussian Restricted Boltzman Machines (GRBM). Both the aspects should be merged together to achieve human-like intelligent reasoning and learning from environment. But the success of such an integration depends upon the chosen logical system, that can support fuzzy reasoning with uncertainty, as well as, can support the extracted knowledge from GRBM. This work investigates the feasibility of using interval-valued fuzzy logic programming for this purpose. This work focuses on the theoretical aspects from logic programming perspective.
PAARS: Privacy Aware Access Regulation System

During pandemics, health officials usually recommend access monitoring and regulation protocols/systems in places that are major activity centers. As organizations adhere to those recommendations, they often fail to implement proper privacy requirements to prevent privacy loss of the users of those protocols or systems. This is a very timely issue as health authorities across the world are increasingly putting these regulations in place to mitigate the spread of the current pandemic. A number of solutions have been proposed to mitigate these privacy issues existing in current models of contact tracing or access regulations systems. However, a prevalent pattern among these solutions are they mainly focus on protecting users privacy from server side and involve Bluetooth based ephemeral identifier exchange between users. Another pattern is all the current solutions try to solve the problem in city-wide or nation wide level. In this paper, we propose a system, PAARS, which approaches the privacy issues in access monitoring/regulation systems from a micro level. We solve the privacy issues in access monitoring/regulation systems without any exchange of any ephemeral identifiers between users. Moreover, our proposed system provides privacy on both server side and the user side by using secure hashing and differential privacy mechanism.
Dynamic path planning for multi-handling robots in warehousing system

Aiming at the problem of robot collision in the path planning of multi-handling robots in warehousing system, an improved dynamic path planning scheme based on priority method is proposed. On the basis of static path planning, this paper proposes a scheme that dynamically plans the path according to the priority of the robot, and sets the raster to different colors by colormap to distinguish the path. The simulation results show that when the robot fails, the task completion degree of other robots remains at almost 100%, which is much higher than the conventional path planning method and has high reliability. At the same time, the MATLAB GUI is designed to make it easy to change the starting point and ending point of multiple robots and get the path results.
A tribute to Eric C. Grimm (1951–2020)

Palynologists around the world mourn the loss of Eric C. Grimm, renowned for his devising of the computer program TILIA, who died on 15 November 2020 at his home in Jefferson, South Dakota, USA at ...
Intent Classification on Myanmar Social Media Data in Telecommunication Domain Using Convolutional Neural Network and Word2Vec

Nowadays, people widely use social media and spend more time on that. Intentions behind users' generated content can be ranged from social good to feedbacks about the service or product of a company. With the help of deep learning models, users' intentions can classify more accurately. This paper focuses on the intent classification of users' generated comments on social media posted in Myanmar text. In this paper, Word2Vec is used to convert words into vector representations, which will be input for the Convolutional Neural Networks (CNN) to classify the users' comments to one of the pre-defined classes. Continuous Bag of Words (CBOW) architecture is used to train Word2Vec model. The proposed model's comparative experiment was performed on the baseline Recurrent Neural Network (RNN) model with a single recurrent layer. Facebook is a target social medial platform. Content from social media are domain-independent and makes it difficult to classify. So, in the proposed model, telecommunication is the target social media domain. Users' comments from that domain are regarded as feedbacks and collected as training and testing data for the model. According to the experimental result, the proposed model outperforms the average F-Score value of 0.94 over RNN.
A Novel Knowledge Base Question Answering Model Based on Knowledge Representation and Recurrent Convolutional Neural Network

The goal of the question-answering (QA) system is to understand the questions from users and return their accurate answers. In the medical field, the question-answering system amis to understand patients' questions and return the correct answers. The existed knowledge base question-answering (KB-QA) systems mainly rely on hand-crafted features and ignore structure information of knowledge base which accordingly lead to the answers with low accuracy. In this paper, a novel KB-QA model is put forward based on knowledge representation and recurrent convolutional neural network. This model has three parts, candidate answers generation, entity relationships extraction and knowledge representation learning based on knowledge base. In addition, an algorithm is also developed to compute the scores of linking candidate answers and knowledge base. Experimental results show that the presented model achieves better performance compared with the baseline systems.
Deep Learning-Based Channel Quality Estimation in Adaptive Shortwave Communication Systems

For a long time, poor channel quality and shortage of frequency resources often restrict its development. An adaptive shortwave communication is considered as an effective method while channel quality estimation (CQE) is essential for the shortwave adaptive communication system. Currently, deep learning (DL) based CQE methods are proposed to achieve a good identification performance. However, existing methods are hard to extract full features from baseband signals, due to the fact that their deep neural networks are trained from the limited length of signal samples. In order to avoid this problem, we consider two training models. The first one is transforming baseband signals into constellation diagrams and three kinds of DL algorithms (i.e., AlexNet, ResNet, DenseNet) are applied respectively for training. The second one is slicing IQ signals into multi-slices signals and convolutional neural network (CNN) is applied and CQE is a joint multi-slice and cooperative decision. Experimental results show that the proposed methods are robust, and joint multi-slice and cooperative detection aided DL-based CQE method achieves better performance even up to 100%.
PProCRC: Probabilistic Collaboration of Image Patches for Fine-grained Classification

We present a conditional probabilistic framework for collaborative representation of image patches. It incorporates background compensation and outlier patch suppression into the main formulation itself, thus doing away with the need for pre-processing steps to handle the same. A closed form non-iterative solution of the cost function is derived. The proposed method (PProCRC) outperforms earlier CRC formulations: patch based (PCRC, GP-CRC) as well as the state-of-the-art probabilistic (ProCRC and EProCRC) on three fine-grained species recognition datasets (Oxford Flowers, Oxford-IIIT Pets and CUB Birds) using two CNN backbones (Vgg-19 and ResNet-50).
An Adaptive BWT-HMM-based Lossless Compression System for Genomic Data

For many years, the Burrows-Wheeler Transform (BWT) had been employed in data compression. This BWT-based compression is facing inflexibility problems due to their text-dependent. To deal with this problem, we took the opportunity to combine BWT with Hidden Markov Model (HMM) as a compression system. BWT employed to produce a clustered single character structure, meanwhile, HMM employed to predict the Genomic Data through the cluster. Here we performed a learning algorithm (the Baum-Welch EM Algorithm) to improve the compression ratio by re-estimating the model to the Genomic Data. The highest single and mean compression ratio produced is 4.276 and 4.004 respectively, with the possibility of improved compression ratio as much as 2.90% before saturation. Furthermore, this compression system still interesting to be developed on these topics, i.e. developing the HMM to cope with complex patterns and performing offline re-estimation to reduce time consumption.
A Network Combining Local Features and Attention Mechanisms for Vehicle Re-Identification

Vehicle of the same manufacturer and the same color can only be distinguished by their subtle difference. If these small features, such as stickers on windows and spray paint on cars, can be better used, we can significantly improve the accuracy of vehicle reidentification. This paper aims to develop an effective network combining local features and attention mechanisms for vehicle reidentification. It divides the feature map to enable the network to capture more detailed feature information. At the same time, it uses the attention mechanism to enable the network to focus on the most important part of each branch, effectively eliminating background and other interference, and improving the network performance. Experiments show that this method improves the result of Rank-1 and mAP on two public datasets: VeRi-776 and VRIC.
AART: AI Assisted Review Tool for Marketing Creatives

We demonstrate AART (AI Assisted Review Tool) which is a novel solution for any iterative document review process like marketing creative review. AART presents multiple novel features including creating a rich structured representation for marketing documents, efficiently comparing multiple documents to identify differences, interpreting reviewer’s comments (natural language text) and transforming it into pseudo-instructions, and establishing cause-effect relation between the comments and differences. The interactive GUI of AART further provide multiple unique features that assists reviewers to perform their job more efficiently with reduced efforts.