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This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed DCM-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to very accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, DCM-ARD is shown to be able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability., 21 pages, 2 figures, 11 tables

The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).

We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differentiation and a numerical integration scheme, AutoMat reduces the implementation effort to two potential functions. By moving AutoMat to the GPU, we close the performance gap to conventional evaluation routines and demonstrate in detail that the expression level reverse mode of automatic differentiation as well as its extension to second order derivatives can be applied inside CUDA kernels. We underline the effectiveness and the applicability of AutoMat by integrating it into the FFT-based homogenization scheme of Moulinec and Suquet and discuss the benefits of using AutoMat with respect to runtime and solution accuracy for an elasto-viscoplastic example., 28 pages, 15 figures, 7 tables; new layout, more detailed proof of Theorem 1

A phoneme is the smallest perceptually distinct sound unit that can be distinguished among words in a particular language. Every language has its own set of phonemes, and all possible words can be considered as ordered sequences of phonemes.The total number of phonemes contained in a language is always very few in comparison to the size of the vocabulary supported by the language. These facts have made phoneme recognition an attractive proposition in the entire journey of the Automatic Speech Processing (ASP) till date. As a result, the classification and recognition of phonemes are considered as the primary tasks of automatic speech recognition (ASR) systems irrespective of application domain. The dynamic nature of phonemes and several sources of their variability create lots of barriers in accurate identification of phonemes from an acoustic signal. The contribution of Machine Learning (ML) based techniques in overcoming these obstructions in automatic phoneme recognition (APR) is remarkable. Nowadays with lot of data availability, ML based ASR is preferred because of its simplicity over acoustic-phonetic based methods. The ML based techniques do not follow the conventional method based on identification of acoustic properties. Rather, ML techniques build their own trained model (algorithm) using readily available data. They do so by finding out the hidden patterns in speech signals, and acquire predictive intelligence through learning. Therefore, ML techniques can be said to provide a more generalized model for phoneme classification. In this paper, we present a comprehensive survey of ML tools to build phoneme recognizers. We also highlight some applications of speech (especially phoneme) recognition which illustrate the current scope as well as future prospects of APR.

BackgroundStandardized coding of plays an important role in radiology reports’ secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research.MethodsWe present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese–English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms.ResultsThe results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup.ConclusionsThe paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding.

The article presents approaches to the automatic detection of sound repetitions and the measurement of sound coherence in Russian poetic text basing on the syllabocentric concept of the sound texture of the verse, which distinguishes the phonosyllabeme as its main operational unit and determines the main types of relations between the elements of repetition - equiphony and metaphony - and also allows describe the text in its unity of sound-segmental and rhythmic structure. The paper considers the linguistic prerequisites, main algorithms and methods, which form the basis of the PHONOTEXT computer program (web application), which automatically analyzes and visually forms the picture of the sound composition of the text presenting its quantitative characteristics. The basic operations of the program are described in detail, as well as the mathematical formulas used to identify the structure and carry out mathematical measurement of sound repetitions in Russian poetic speech. The article discusses the results of the programs use for the comparative study of poetry and prose, individual works and individual styles of Russian poets. In particular, the data obtained using the Phonotext program allows us to speak of a tendency towards syntagmatic consolidation of the phonosyllabeme and intensive granulation of the sound texture of the text in the individual style of Osip Mandelstam as the evidence of his striving for poetic morphologization of the elements of sound repetition, it also shows a tendency towards the use of the dominant sound theme as a means for cohesion of a complex syntactic and compositional whole, in contrast to Vladimir Nabokovs idiostyle, which is characterized by its orientation towards splitting up the elements of repetition, with a relatively chaotic interaction and spreading of consonances over the text and their low activity as a means of syntagmatic segmentation and consolidation of the text. The prospects for the development of the Phonotext program are described for identifying the relations of segment-sound and prosodic parallelism in speech (relations of equirhythmy and equiphony, including rhyme), which will provide an automatic presentation of a complete picture of the sound composition, the interaction of its main forms in the text and its further functional description.

To mitigate the cost of manually producing and maintaining models capturing software specifications,specification miningtechniques can be exploited to automatically derive up-to-date models that faithfully represent the behavior of software systems. So far,specification mining solutions focused on extracting information about the functional behavior of the system, especially in the form ofmodels that represent the ordering of the operations. Well-known examples are finite state models capturing the usage protocol ofsoftware interfaces and temporal rules specifying relations among system events.Although the functional behavior of a software system is a primary aspect of concern, there are several other non-functionalcharacteristics that must be typically addressed jointly with the functional behavior of a software system. Efficiency is one of the most relevant characteristics. Indeed, an application that delivers the right functionalities with an inefficient implementation may fail to satisfy the expectations of its users.Interestingly, the timing behavioris strongly dependent on the functional behavior of a software system. For instance, the timing of an operation depends on the functional complexity and size of the computation that is performed. Consequently, models that combine the functional and timing behaviors, as well as their dependencies, are extremely important to precisely reason on the behavior of software systems.In this paper, we address the challenge of generating models that capture both the functional and timing behavior of a software system from execution traces. The result is the Timed k-Tail (TkT) specification mining technique, which can mine finite state models that capture such an interplay: the functional behavior is represented by the possible order of the events accepted by the transitions, while the timing behavior is represented through clocks and clock constraints of different nature associated with transitions.Our empirical evaluation with several libraries and applications shows that TkT can generate accurate models, capable of supporting the identification of timing anomalies due to overloaded environment and performance faults. Furthermore, our study shows that TkT outperforms state-of-the-art techniques in terms of scalability and accuracy of the mined models.

Topic modeling is a probabilistic graphical model for discovering latent topics in text corpora by using multinomial distributions of topics over words. Topic labeling is used to assign meaningful labels for the discovered topics. In this paper, we present a new topic labeling method that uses automatic term recognition to discover and assign relevant labels for each topic, i.e., TLATR (Topic Labeling using Automatic Term Recognition). TLATR uses domain-specific multi-terms that appear in the set of documents belonging to a topic. The multi-term having the highest score as determined by the automatic term recognition algorithm is chosen as the label for that topic. To evaluate TLATR, we use two real, publicly available datasets that contain scientific articles’ abstracts. The topic label evaluation is done both automatically and using human annotators. For the automatic evaluation, we use Pointwise Mutual Information, Normalized Pointwise Mutual Information, and document similarity. For human evaluation, we employ the average rating method. Furthermore, we also evaluate the quality of the topic models using the Adjusted Rand Index. To prove that our novel method extracts relevant topic labels, we compare TLATR with two state-of-the-art methods, one supervised and one unsupervised, provided by the NETL Automatic Topic Labelling system. The experimental results show that our method outperforms or provides similar results with both NETL’s supervised and unsupervised approaches.

This paper presents the modeling, design, and experimental analysis of an Automatic Generation Control (AGC) for a hydropower plant using Adaptive-Neuro-Fuzzy Inference system (ANFIS). This was aimed at reducing the frequency deviations which occur during power generation. The Adaptive-Neuro-Fuzzy Inference system (ANFIS) was used to intelligently control the selection of parameters for the effective control of power in the hydropower plant. The proposed ANFIS was trained with input–output data of the fuzzy logic controller (FLC). The ANFIS model is used as a hybrid learning model which includes the Least Square Estimate (LSE) and back propagation algorithm (BPA). The conventional PID, FLC, and ANFIS controllers were investigated using MATLAB. In order to determine the best controller, the controllers were experimented and compared to determine the controller with the best performance. The results show that frequency deviations occur as a result of a continuous variation of loads, which make the deviations difficult to control when a governor is not applied. Furthermore, the response of the AGC of the hydropower plant (in the single and double area) with step load changes was studied. The simulation results show that the ANFIS controller performs better compared to the PID as well as the FLC. Further results indicate that the proposed ANFIS controller helps to speed up the performance of the AGC of the hydropower plant. The ANFIS controller not only improved the performance but also made the fuzzy inference system (FIS) less dependent on the expert system.

Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.

The massive use of multimedia technologies has enabled the exploration of information in many data such as texts, audio, videos, and images. Computational methods are being developed for several purposes such as monitoring, security, business, and even health through the automatic diagnosis of diseases by medical images. Among these diseases, we have melanoma skin cancer. Melanoma is a skin cancer that causes a large number of fatalities worldwide. Several methods for the automatic diagnosis of melanoma in dermoscopic images have been developed. For these methods to be more efficient, it is essential to isolate the lesion region. This study used a melanoma segmentation method based on U-net and LinkNet deep learning networks combined with transfer learning and fine-tuning techniques. Additionally, we evaluate the model’s ability to learn to segment the disease or just the dataset by combining datasets. The experiments were carried out in three datasets (PH2, ISIC 2018, and DermIS) and obtained promising results, emphasizing the U-net that obtained an average Dice of 0.923 in the PH2 dataset, Dice = 0.893 in ISIC 2018, and Dice = 0.879 in the DermIS dataset.

Background: In this competing era, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching- Learning Process (TLP). An assessment is one way to identify the learner’s weak spot in the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. To generate questions is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Objective: The proposed system is to generate the test questions that are mapped with bloom’s taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest bloom’s cognitive levels. Methods: Proposed an Automatic Question Generation (AQG) system which autom Proposed an Automatic Question Generation (AQG) system which, automatically generates the assessment questions from the input file Dynamically.atically generates the assessment questions dynamically from the input file. Results and Conclusion: The outputs are dynamic to create a different set of questions at each execution. Here, the input paragraph is selected from the computer science domain and their output efficiency is measured using precision and recall.

Dalam suatu sistem otomasi industri diperlukan suatu kontroler, salah satunya adalah Programmable Logic Controller (PLC). PLC dibuat untuk mengubah sistem kerja mesin yang dulunya bekerja secara konvensional menjadi sistem yang dikendalikan dengan menggunakan komputer. Penelitian ini bertujuan untuk merancang sistem kendali otomatis menggunakan PLC pada press roll di salah satu building tire machine. Fokus penelitian yang dilakukan pada sistem kontrol press roll ini meliputi pemasangan panel kontrol PLC dan program PLC dengan software GX-Works 2. PLC yang digunakan adalah PLC Mitsubishi tipe-Q dengan modul input QX41 dan QX42 dan modul output QY41P dan QY10. Metode yang digunakan dalam penelitian ini adalah metode Research and Development (R&D), yaitu metode untuk mengembangkan sistem yang sudah ada sebelumnya. Pengujian sistem dilakukan dengan mengukur tegangan kerja pada terminal input dan output PLC serta menguji kinerja sistem kontrol secara keseluruhan. Hasil yang diperoleh pada pengujian terminal input menyatakan bahwa sistem input bekerja dengan normal. Kemudian pada pengujian terminal output didapatkan nilai persentase error (% error) pada terminal output Y68 sebesar 0,08%, dan terminal output Y7F sebesar 0,04% dengan tegangan standar 24 VDC. Sedangkan terminal keluaran Y91 memiliki nilai persentase error tegangan sebesar 1,31% dengan tegangan standar 220 VAC. Dalam pengujian kinerja keseluruhan sistem bekerja normal sesuai dengan program PLC yang telah dibuat.

Context. The article proposes an approach for automated identification of the navigators motivational model in the control of water transport. Algorithms for data extraction as a result of the man-machine interaction of navigator with the electronic control systems of the vessel during performing navigation operations of increased complexity are proposed. Objective. The purpose of research is to apply formal and algorithmic approaches to extracting data on the motivational model of navigator to prevent accidents in water transport. Method. The identification of manifestation determination of navigators’ mental activity by means of the visual concept of the geometric group theory is proposed. This approach delivered the visual systematic-logical combining of diagnostic methods aimed at determining navigators motivational centers and the processes of professional activity like maneuver performing. The key indicator of identification is said to be the parameter of the navigator’s activity as “rpm_port” having an impact on the vessel speed being a marker of intensification of the navigator’s physiological activity. Such an approach is beneficial in time phase identification while maneuvering indicating explicitly at the stepping up of the navigator’s physiological motivational state. It was proven to be correct based on the results due to Ward’s dendrogram, several statistical methods and applied software. The obtained research results encourage the prediction of the navigator’ motivational states in critical situations. Results. In order to confirm the proposed formal-algorithmic approach, an experiment was carried out using the navigation simulator Navi Trainer 5000. Automated analysis of experimental ones made it possible to form a motivational map of the navigator and determine the decision-making model affecting in the processes of control vessel in difficult situations. Conclusions. The proposed research approaches made it possible to automate the processes of extracting data indicating the principles of decision-making by navigator. The effectiveness of proposed approach was substantiated by the results of experimental data automated processing and the constructed tree-like decision-making spaces.

There are many temporarily constructed dynamic words in Chinese sentences. Dynamic words are sentence building units that are not included in the general lexicon and are not suitable for further syntactic analysis. Automatic recognition and analysis of dynamic words in sentences play an important role in improving the efficiency and accuracy of Chinese automatic syntactic analysis. The existing researches on dynamic words mainly focus on the qualitative description of concepts and categories. There is no overall algorithm design and experimental exploration on automatic recognition of dynamic words. In the practice of automatic syntactic analysis, dynamic words are generally segmented, and the components are analyzed according to syntax, while the automatic recognition and analysis of dynamic words as a whole are ignored. In this study, the dynamic word is separated from syntactic analysis as the content of lexical analysis and recognized and analyzed as a whole. This paper uses the method of knowledge engineering to research and analyze dynamic words for Chinese automatic syntactic analysis based on sentence pattern structure, initially designs a knowledge representation method of dynamic words, secondly constructs the dynamic word structural mode knowledge base by annotating the dynamic words in the corpus of a certain scale of international Chinese textbooks, and finally explores the automatic recognition methods of dynamic words based on regular expressions, semantic category combinations and machine learning classification algorithms. The experimental results show that the three algorithms can cover the recognition of all types of dynamic words, and achieve relatively ideal accuracy and recall rate.

In the era of big data, information overload problems are becoming increasingly prominent. It is challengingfor machines to understand, compress and filter massive text information through the use of artificial intelligencetechnology. The emergence of automatic text summarization mainly aims at solving the problem ofinformation overload, and it can be divided into two types: extractive and abstractive. The former finds somekey sentences or phrases from the original text and combines them into a summarization; the latter needs acomputer to understand the content of the original text and then uses the readable language for the human tosummarize the key information of the original text. This paper presents a two-stage optimization method forautomatic text summarization that combines abstractive summarization and extractive summarization. First,a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initialsummarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcementlearning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2,and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset.

The automatic correction of grammar and spelling errors is important for students, second language learners, and some Natural Language Processing (NLP) tasks such as part of speech and text summarization. Recently, Neural Machine Translation (NMT) has been an out-performing and well-established model in the task of Grammar Error Correction (GEC). Arabic GEC is still growing because of some challenges, such as scarcity of training sets and the complexity of Arabic language. To overcome these issues, we introduced an unsupervised method to generate large-scale synthetic training data based on confusion function to increase the amount of training set. Furthermore, we introduced a supervised NMT model for AGEC called SCUT AGEC. SCUT AGEC is a convolutional sequence-to-sequence model consisting of nine encoder-decoder layers with attention mechanism. We applied fine-tuning to improve the performance and get more efficient results. Convolutional Neural Networks (CNN) gives our model ability to joint feature extraction and classification in one task and we proved that it is an efficient way to capture features of the local context. Moreover, it is easy to obtain long-term dependencies because of convolutional layers staking. Our proposed model becomes the first supervised AGEC system based on the convolutional sequence-to-sequence learning to outperforms the current state-of-the-art neural AGEC models.

The emergence of multi-core architectures makes it essential for optimizing compilers to automatically extract parallelism for large scientific applications composed of many subroutines residing in different files. Inlining is a well-known technique which can be used to erase procedural boundaries and enable more aggressive loop parallelization. However, conventional inlining cannot be applied to external libraries where the source code is not available, and when overly applied, it can degrade the effectiveness of compiler optimizations due to excessive code complexity. This paper highlights some obstacles we encountered while applying conventional inlining combined with automatic loop parallelization using the Polaris optimizing compiler and presents a new approach, annotation-based inlining, to effectively overcome these obstacles. Our experimental results show that the annotation-based inlining approach can eliminate negative impact of conventional inlining while enhancing the effectiveness of interprocedural parallelization for a majority of applications from the PERFECT benchmark suite.

Nowadays, students face many difficulties to practice for exams. Professors and teachers spend a lot of time and effort to make exams. Automatic Question Generation Model proposes a solution to save time, effort, and student’s learning process which helps in educational purposes. AQGM is user-friendly which is implemented as a GUI-based system that generates Wh- questions which mean” WH” (” What”,” Who”, and” Where”) and formatted into two types of templates, Question Bank template, and Exam template. Exams have different difficulty levels (Easy, Medium, and Hard). Therefore, students can measure their level and teachers will know to what extent the students understand the course. Researches have shown that this method is helpful and successful for educational purposes. AQGM generates questions automatically by using its model that generated by using sequence-to-sequence approach specially encoder-decoder technique with copy mechanism and attention decoder. AQGM model uses SQuAD as a training dataset which helps to get more accurate results.

Techniques for simulation and application of automatic digital generation control are presented. Objectives of generation control are reviewed. A digital control scheme is described and implementation. The presented directive software is capable of guiding the whole system so that control objectives are achived in a satisfactory manner.

Recently, much attention has been given to image annotation due to the massive increase in image data volume. One of the image retrieval methods which guarantees the retrieval of images in the same way as texts are automatic image annotation (AIA). Consequently, numerous studies have been conducted on AIA, particularly on the classification-based and probabilistic modeling techniques. Several image annotation techniques that performed reasonably on standard datasets have been developed over the last decade. In this paper, a review of the image annotation method was conducted, focusing more on deep learning models. Automatic image annotation (AIA) methods were also classified into five categories, including i) Convolutional Neural Network (CNN) based on AIA, ii) Recurrent Neural Network (RNN) based on AIA, iii) Deep Neural Networks (DNN) based on AIA, iv) Long-Short-Term Memory (LSTM) based on AIA, and v) Stacked auto-encoder (SAE) based on AIA. An assessment of the five varieties of AIA methods was also offered based on their principal notion, feature mining technique, explanation precision, computational density, and examined aggregated data. Moreover, the evaluation metrics used to evaluate AIA methods were reviewed and discussed. The need for careful consideration of methods throughout the improvement of novel procedures and datasets for image annotation assignment was highly demanded. From the analysis of the achievements so far, it is certain that more attention should be paid to automatic image annotation.

The automatic generation of a text summary is a task of generating a short summary for a relatively long text document by capturing its key information. In the past, supervised statistical machine learning was widely used for the Automatic Text Summarization (ATS) task, but due to its high dependence on the quality of text features, the generated summaries lack accuracy and coherence, while the computational power involved, and performance achieved, could not easily meet the current needs. This paper proposes four novel ATS models with a Sequence-to-Sequence (Seq2Seq) structure, utilizing an attention-based bidirectional Long Short-Term Memory (LSTM), with added enhancements for increasing the correlation between the generated text summary and the source text, and solving the problem of out-of-vocabulary (OOV) words, suppressing the repeated words, and preventing the spread of cumulative errors in generated text summaries. Experiments conducted on two public datasets confirmed that the proposed ATS models achieve indeed better performance than the baselines and some of the state-of-the-art models considered.

In this paper, we apply the abstractive text summarization technology to automatic generation system of reading comprehension, which is part of College English Test (CET) in China. At present, there is a growing demand of English reading examination questions, yet the manual examination question generating is time-consuming and labor-intensive. To relieve the pressure on question generating task, we put the related automatic technology into application, which aims to assist teachers in question generating, meanwhile, to provide more CET exercises for students. We combine seq2seq model and attention mechanism to generate the abstractive text summarization. The abstract generated by this method is easy to understand and in line with the question generating of long reading comprehension, the experiment showed good results of question generating.

The amount of data available in the electronic environment is increasing day by day with the development of technology. It becomes tough and time consuming for the users to access the information they desire within this increasing amount of data. Automatic text summarization systems have been developed to reach the desired information within texts in a shorter time than that of manual text summarization. In this paper, a new extractive text summarization model is proposed. In the proposed model, the inclusion of sentences of a given text in the summary is decided based on a classification approach. Also, the effectiveness of widely used features for automatic text summarization in the Turkish language is evaluated using sequential feature selection methods. The evaluations were carried out specifically for Turkish texts in the categories of economy, art, and sports. The experimental work justified the performance of the proposed text summarization method and revealed how effective the features are.

In this article, we propose a fully automatic system for generating comic books from videos without any human intervention. Given an input video along with its subtitles, our approach first extracts informative keyframes by analyzing the subtitles and stylizes keyframes into comic-style images. Then, we propose a novel automatic multi-page layout framework that can allocate the images across multiple pages and synthesize visually interesting layouts based on the rich semantics of the images (e.g., importance and inter-image relation). Finally, as opposed to using the same type of balloon as in previous works, we propose an emotion-aware balloon generation method to create different types of word balloons by analyzing the emotion of subtitles and audio. Our method is able to vary balloon shapes and word sizes in balloons in response to different emotions, leading to more enriched reading experience. Once the balloons are generated, they are placed adjacent to their corresponding speakers via speaker detection. Our results show that our method, without requiring any user inputs, can generate high-quality comic pages with visually rich layouts and balloons. Our user studies also demonstrate that users prefer our generated results over those by state-of-the-art comic generation systems.

In this article, the subject of research is the sub-process of controlling the assignment of requisites to the electronic document of the process of registration and preparation of completed electronic documents for transfer to archival storage in the electronic document management system. The aim of the research is to develop a method for automatic control of the assignment of details to a completed electronic document. In the course of the study, the requirements of regulatory legal acts of federal executive bodies in the field of office work and archiving were analyzed, in terms of the registration of electronic files and their preparation for transfer to archival storage, scientific publications covering the issues of automation of office work in electronic office systems. A functional model was built for the process of control of registration and preparation for the delivery of electronic files for archival storage using the IDEF 0 methodology in a model for performing the sub-process of assigning details to a completed electronic document of the process of controlling registration and preparation for the delivery of electronic documents using the theory of logic (predicate logic). A method has been developed for automatic control of the assignment of requisites to a completed electronic case, which can be used in the design of advanced electronic document management systems, as well as modernization of existing ones by creating software modules that implement the functions of automatic control of the registration of completed electronic cases. This method makes it possible to increase the efficiency of the process of preparing completed electronic files for transfer to archival storage.

The current article discusses the use of the dependency graph method to design and analyze automatic transmissions. Different goals may be served by modeling an automatic transmission using graphs. The most important of them are: determining the gear ratios for gears and the analysis of speed and acceleration of the rotating elements. Game tree-structure methods can be used to analyze the functional schemes of the selected gears. This paper describes the method of generating a system of kinematics equations for signal dependency graphs. This allows the generalization and extension of the algorithmic approach, and also further analyses and syntheses, such as checking the isomorphism of the proposed solutions and determining the validity of the structure and the operational parameters of the analyzed gears.

Catastrophic events cause casualties, damage property, and lead to huge social impacts. To build common standards and facilitate international communications regarding disasters, the relevant authorities in social management rank them in subjectively imposed terms such as direct economic losses and loss of life. Terrorist attacks involving uncertain human factors, which are roughly graded based on the rule of property damage, are even more difficult to interpret and assess. In this paper, we collected 114 183 open‐source records of terrorist attacks and used a machine learning method to grade them synthetically in an automatic and objective way. No subjective claims or personal preferences were involved in the grading, and each derived common factor contains the comprehensive and rich information of many variables. Our work presents a new automatic ranking approach and is suitable for a broad range of gradation problems. Furthermore, we can use this model to grade all such attacks globally and visualize them to provide new insights.

Formally known as ISO/IEC 23093, Internet of Media Things (IoMT) is an emerging paradigm ensuring interoperability among media-centric applications and services designed and deployed for the interpretation, representation or analysis of multimedia content collected by media devices such as cameras or microphones. Under this framework, the present paper establishes the proof of concept for the automation of IoMT specified Smart Contract generation. The advanced modus operandi enables media devices and their content to be seamlessly protected against payment counterfeiting, malicious control, access, interception and/or redirection. To this aim, a comprehensive architectural and experimental framework is conceived, designed, and demonstrated. While the methodological approach is blockchain agnostic, the experimental results are obtained on a 3-node, EEA (Enterprise Ethereum Alliance)-compliant private blockchain.