Reviewerly/exHarmony huggingface dataset:
Document-Improved Hierarchical Modular Attention for Event Detection

The task of event detection aims to find the event trigger for a sentence and identify the correct event type. Contextual information is crucial to event detection, which helps a model to identify the triggers better. Existing models utilizing contextual information only take document information as additional features for deep learning model, without considering the specific contribution of document information to trigger classification. In this paper, we propose a Document-Improved Hierarchical Modular Event Detection (DIHMED) model to extract hierarchical contextual information. Specifically, considering the relevance between event types, we build independent modules that combine the document-level information to express this relevance. Given the fact that events from the same document are often related, these modules can make better use of document-level information. We conduct several experiments on a Chinese political event dataset and the results show that our model can outperform the state-of-the-art models.
An Approach of Fuzzy Reasoning Based on Linguistic Ordered Pair 3-tuple

With the rapid development of artificial intelligence, it is of significance in practical application to reasoning in uncertain environment and to make judgment and reasonable decision on this basis. In order to deal with the multiple class data in uncertain environment, we standardize it and convert it into linguistic ordered pair 3-tuple which can describe the linguistic values from both positive and negative aspects, so as to obtain more reasonable reasoning results. Based on the linguistic 2-tuple, this paper constructs a standardized transformation model between the interval-valued fuzzy set and linguistic ordered pair 3-tuple by defining the transformation operator, which solves the problem of data standardization of this type. Furthermore, aiming at the problem of reasoning in uncertain linguistic environment, four operators of linguistic ordered pair 3-tuple are proposed and their properties are discussed. At the same time, in order to increase the credibility of linguistic ordered pair 3-tuple reasoning, we present a reasoning model of linguistic ordered pair 3-tuple combined with the similarity between the rules of linguistic ordered pair 3-tuple. Finally, an example which concerned the intelligent case system is given to illustrate the effectiveness and rationality of the proposed method.
Project Studies as A Transborder Didactic Tool in Higher Education (The Case of The German-Russian Study Project)

Project studies can become the most important innovative component of education programs in modern society. They allow one to not only improve the professional skills of students, but also develop competencies in the fields of project management and team building. The greatest didactic value is possessed by interdisciplinary and international projects. In the framework of their implementation, a synergistic effect occurs from a combination of methods of different sciences, as well as communications between representatives of different national scientific and educational schools. The success of a project study depends on student motivation. Their participation in setting common goals and developing methods for their achievement is the basis of high motivation. Along with this, the work of teacher-curators, oriented to help in solving emerging problems of a substantial and methodological order, acquires a fundamental role. The lack of project studies is largely due to the high requirements for the teachers involved in it. The tasks of teachers during project training are fundamentally different from those that they perform using other educational methods. This is one of the most temporarily costly and time-consuming forms of training. The study deals with the structural, managerial and didactic features of project study in higher education using as an example scientific and educational projects on labour market studies, which, since 2017, have been jointly carried out by the Moscow State University (MSU) and the University of Applied Labour Studies (UALS), Mannheim.
Matrix Transformation-based Optimized CNN

High-accuracy automatic recognition of facial expression is a challenging task because of the subtlety, complexity and diversity of facial expression. In this paper, we improve a traditional feature-based method, named the convolutional neural network (CNN). In order to increase the accuracy, we treat the tensor of the output layer as a multi-dimensional vector, and through matrix transformation, we can magnify its eigenvalues. Empirical evidence shows that this method makes the calculated difference between the probability values of each expression larger so that the computer obtains a smaller loss value when correctly recognized. Moreover, the training speed is faster than that before the optimization.
Didactic Strategy with the Application of Technologies to Contribute to the Development of Modelling Skills from Operations Research Subject

The knowledge society demands new ways to develop the learning - teaching process, where Information and Communication Technologies (ICT) plays a major role with the use of educational technology as a didactic tool for the development of skills. The University of Informatics Sciences offers a degree in Informatics Sciences Engineering, which has Operations Research subject within the Applied Mathematics discipline. This program is in charge of developing in the students the skill of modelling problems of linear programming, necessary for a professional of this area. Through the review of semester reports, pedagogical observations, interviews and surveys applied to students and teachers; several insufficiencies were evidenced in the learning teaching process of this subject; that indicates a low level of skill development of modeling linear programming problems. For this reason, this research objective is to elaborate a didactic strategy that makes use of the ICT for contributing to the development skills to model problems from Operations Research course in students of Informatics Sciences Engineering. As part of the strategy, a set of actions were defined to be executed by students and teachers in each of the four stages developed: diagnosis, planning, execution and evaluation. In this stage, a group of indicators was defined to measure the level of the development of the skill, according to a three levels scale. A collection of exercises was also made to model linear programming problems to work in classes, according to the current socioeconomic context. Finally, the results of the validation of the strategy are shown, through a pre-experiment, and the application of Iadov’s satisfaction test.
The Designer as Savage

The savage mind can be described as the thoughts of “primitive people” without writing. They desire to understand the world around them, its nature, and their society. They are thinkers and also handymen, using so-called bricolage, rather disinterested thinking and intellectual reasoning as a philosopher does, and to some extent a scientist. The term bricolage has also been used and discussed in many other fields, including anthropology, philosophy, critical theory, education, computer science and business. In this chapter, we attempt to identify design’s role neither as an economically viable tool, nor as carried out by conventional mainstream designers. We examine the significance of “design as bricolage” and explore just how bricolage can provide an alternative for new ways of approach to design and to using technology. Further, we argue for mythical thinking as an alternative creative mindset, as opposed to cultivated systematic thought that systematically proceeds from goals to means.
Use of Data Mining for Root Cause Analysis of Traffic Accidents in Colombia

The high impact of traffic accidents makes it imperative to formulate public policies to reduce their occurrence. In this task, knowing the cause of accidents is of paramount importance. The use of data mining and big data adapts to the complexity of the phenomenon under study. In order to classify some possible causes of traffic accidents, we built a data model to describe the behavior and dynamic of the participant agents in the traffic accident event in Colombia. This paper presents the application of MLP and Naïve Bayes algorithms to identify the possible immediate cause and the rules decision algorithm PART for the root cause of traffic accidents. Models have been tested aiming to obtain the goodness of fit by increasing metrics like Recall, Precision, ROC and Kappa index, and minimize the RMSE.
The Blurred Boundaries Between Budget Transparency and State Secrecy: a Survey of Three Departments Across 36 Chinese Municipalities

The tension between ensuring open government information and maintaining national security is a widespread problem around the world. This study focuses on the disclosure of budgetary information and its tension with vaguely defined state secrecy requirements in the Chinese context. Through a survey of three government departments that potentially involve state secrets across 36 Chinese municipalities, we find that there exists no consensus on whether to make budgetary information public, even for the same department across different jurisdictions. In addition, departments that chose disclosure vary considerably in the scope and depth of their transparency. Without having the boundaries clarified by law, disclosure by request, as a supplemental behavior to proactive disclosure, can rarely be successful. Our findings suggest that future legislation ought to clarify the legitimate scope of restrictions on budget transparency on the grounds of state secrecy.
Distributed Fractionalized Data Networks For Data Integrity

The world is being transformed by the onset of new high speed 5G Technologies that open the possibility of IoT networks at scale. This demands delivery guarantees and coordinated distributed communications that are resistant to damage and can self-heal under adversity. The speed of change is increasing with increased automation, artificial intelligence, information from multiple sources, integrated systems of systems and emerging quantum technologies. Current distributed consensus checking mechanisms are computationally intensive and fail to scale along with these changes because of the complexity of proof of work calculations or the unnecessary need to bind in domain specific elements such as cryptocurrencies. Furthermore, these mechanisms are brittle in that small changes in messages can cause restarts or failure of integrity checks, or they introduce domain specific elements (e.g. monetary design that has little to do with integrity). We propose distributed ledgers as a pure technology coupled with a strong proof protocol for exchanges, called "Proof of Integrity" without any need for cryptocurrencies or other domain specific elements. Proof of Integrity provides distributed data guarantees and operational continuity through adversity or breakdowns while creating a reliable and trustworthy layer for the application specificity of domain specific elements.
Malvertising in Facebook: Analysis, Quantification and Solution

Online advertising is a wealthy industry that generated more than $100B in 2018 only in the US and delivers billions of ads to Internet users every day with. These impressive numbers have also attracted the attention of malicious players that try to exploit the online advertising ecosystem for their own benefit. In particular, one of the most harmful practices refers to malicious users that act as advertisers to deliver unsafe ads. The goal of these ads is to compromise the security of the users that receive those ads. This practice is referred to as Malvertising. Some reports have estimated the economic loss caused by malvertising to the online advertising sector to $1.1B in 2017. This paper is the first work that analyses and quantifies the impact of malvertising in Facebook. To accomplish this study, we rely on a dataset that includes more than 5 M ads delivered to 3 K Facebook users from 126 K advertisers between October 2016 and May 2018. Our results reveal that although the portion of advertisers (0.68%) and ads (0.17%) associated to malvertising is very low, 1/3 of the users in our study were exposed to malvertising. Finally, we also propose a novel solution to block malvertising ads in real-time in Facebook.
Semantic Segmentation Algorithm Based on Attention Mechanism and Transfer Learning

In this paper, we propose a semantic segmentation algorithm (RoadNet) for auxiliary edge detection tasks with an attention mechanism. RoadNet improves the dispersion of the low-level features of the network model and further enhances the performance and applicability of the semantic segmentation algorithm. In RoadNet, a fully convolutional neural network is used as the basic model, an auxiliary loss in the image classification, multitask learning in machine learning, and attention mechanism in natural language processing. To improve the generalization of the model, we select and analyze a proper domain difference measure. Subsequently, the context semantic distribution module and the annotation distribution loss are designed based on the context semantic encoding structure. The domain discriminator based on the adversarial training and the adversarial training algorithm based on transfer learning are then well integrated to provide a transfer learning-based semantic segmentation algorithm (TransRoadNet). The experimental results indicate that the proposed TransRoadNet and RoadNet overperform their equivalent comparison models.
Information Retrieval in Restricted Domain for ChatterBots

This paper presents a restricted domain approach for handling information in the context of regulation search. Information Retrieval (IR) using a chatterbot as front-end is especially complex due to the interaction with Natural Language. The problem becomes more complicated when the IR is for a Restricted Domain: there is no statistical error compensation. The slang in the documentation usually does not match the common language usage, and dialogs tend to be informal, making people tend to perform incomplete questions to the system. PTAH is a chatterbot developed to interact with students, administrative employees, professors and many other people of the university community. Cultural diversity and big age differences present an interesting challenge for the Natural Language Processing area, since many approaches tend to overcome only one of the previous aspects. This paper presents the chatterbot PTAH, and specifically depicts an IR approach that intends to improve the quality of the answers. Results are evaluated with traditional precision and recall metrics, and an additional one to assess the quality of the heuristics involved in the retrieval process. Statistics indicate that it is possible to apply a specific combination of simple and traditional heuristics with good results.
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach

We present a Bayesian approach to evaluate AI decision systems using data from past decisions. Our approach addresses two challenges that are typically encountered in such settings and prevent a direct evaluation. First, the data may not have included all factors that affected past decisions. And second, past decisions may have led to unobserved outcomes. This is the case, for example, when a bank decides whether a customer should be granted a loan, and the outcome of interest is whether the customer will repay the loan. In this case, the data includes the outcome (if loan was repaid or not) only for customers who were granted the loan, but not for those who were not. To address these challenges, we formalize the decision making process with a causal model, considering also unobserved features. Based on this model, we compute counterfactuals to impute missing outcomes, which in turn allows us to produce accurate evaluations. As we demonstrate over real and synthetic data, our approach estimates the quality of decisions more accurately and robustly compared to previous methods.
Similarity of Sentences With Contradiction Using Semantic Similarity Measures

Abstract Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents an algorithm to handle negation. In sentence similarity, word pair similarity plays a significant role. Hence, this paper also discusses the similarity between word pairs. Existing semantic similarity measures do not handle antonyms accurately. Hence, this paper proposes an algorithm to handle antonyms. This paper also presents an antonym dataset with 111-word pairs and corresponding expert ratings. The existing semantic similarity measures are tested on the dataset. The results of the correlation proved that the expert ratings are in order with the correlation obtained from the semantic similarity measures. The sentence similarity is handled by proposing two algorithms. The first algorithm deals with the typical sentences, and the second algorithm deals with contradiction in the sentences. SICK dataset, which has sentences with negation, is considered for handling the sentence similarity. The algorithm helped in improving the results of sentence similarity.
BOOK COVER DESIGN IN ODISHA DURING VARIOUS PHASES OF TECHNOLOGICAL DEVELOPMENT

A Book is always recognised by its cover. Cover is a part of study of Design, which includes the aesthetics as well as the psychological part of life. Science is also involved in the designing of a subject which attracts a class of people. Books are the ultimate source of information for human being from their childhood. Even though there has been a rise in the internet media and online learning, the life of a child in the school starts from Books. Books come in all variety for catering to the needs of different kind of people. Books are read by children, youth as well as the old. Hence, there is an audience for all kind of genre of books. According to the genre of the book, the book cover designer creates the visualization, which will attract people towards the book. The study of styles of Book cover design will be a good information to the people, after which they will have a better judgment in form of design of a book cover or any other kind of design like brochure, leaflets etc. The study of evaluation of book cover design in Odisha will help the audience to know about the phases of development of book cover design and the height of achievement, which design has reached today. The future designers will get a historical brief of the style of book cover designs, which will help them as a reference to create better designs.
Contrast as a Persuasive Strategy in Social Advertising: A Case Study of 4 Advertisings

Social advertising, understood as a type of discourse, is characterized by the use of different persuasive strategies that are adopted on a regular basis for the construction of such discourse.The paper aims to present an analysis of some social advertisements where contrast is used as an expression strategy for the construction of persuasive discourse. The research was carried out from the analysis of 4 advertisements selected for their use of contrast as a persuasive strategy. In the advertisements analyzed, contrast appears in both verbal and nonverbal signs, that is, in the text and in the image. Based on such analysis, contrasting concepts are identified and placed in semiotic squares to perform the analysis. Results show that in this type of advertising there is an exhortative macro-act, consisting of two subparts, a request speech act and a commissive speech act, which is structured in a cause-consequence relationship. The structure of the commission act creates a threat and functions as an argument of the request speech act.
A Motion Blurred Image Restoration Algorithm Based on CycleGAN

An end-to-end blurred image restoration algorithm based on CycleGAN is proposed to solve the complicated problems of obtaining real paired data sets of moving blurred images and simulating real blurred images with manually generated blurred images. In CycleGAN, only using the loss of average absolute error (L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ) as the reconstruction loss wm lead to the problems of artifact, color distortion and unclear image in the restored image. The problem of image artifact and unclear image can be solved by introducing the loss of structural similarity measure (SSIM) and the color distortion can be solved by the loss of color saturation. The experimental results show that compared with several deblurring algorithms with better restoration results, the restoration results produced by the algorithm in this paper are significantly improved in qualitative and quantitative evaluation.
Critical Success Factors in Computer Aided Intercultural Business Communication Course

The paper describes the introduction of the course of intercultural business communication into the curriculum of the Faculty of Informatics and Management of the University of Hradec Kralove, Czech Republic, five years ago as an obligatory subject for foreign students visiting the university. Initially, the subject was taught using traditional methods like lectures of the tutor and the seminars. However, after a few years blended learning methodology was introduces into the course. The students’ satisfaction with the course was tested after each year and the paper compares the results before and after the introduction of blended learning into the subject. Computer aided course clearly proves to be assessed by the students more positively and the paper thus stresses the important of necessary introduction of hybrid and blended learning into traditional curricula as it shows significant improvements in students’ satisfaction, motivation and performance levels.
Neural Method for Explicit Mapping of Weighted Locally Linear Embedding in Image Retrieval

A new explicit nonlinear dimensionality reduction(DR) method, on account of neural networks, is presented for image retrieval tasks. We first propose a Weighted Locally Linear Embedding (WLLE) for training set, based on which linear relations in neighborhood of each sample are guaranteed. Then, a neural method (NM) is proposed to solve the out-of sample problem. As a combination of WLLE and NM, we provide an explicit nonlinear DR approach for efficient image retrieval. The experimental results in three benchmark datasets illustrate that our algorithm could get outstanding performance than other state-of-the-art out-of-sample methods.