Reviewerly/exHarmony huggingface dataset:
Belief base contraction by belief accrual

The problem of knowledge evolution has received considerable attention over the years. Mainly, the study of the dynamics of knowledge has been addressed in the area of Belief Revision, a field emerging as the convergence of the efforts in Philosophy, Logic, and more recently Computer Science, where research efforts usually involve "flat" knowledge bases where there is no additional information about the formulas stored in it. Even when this may be a good fit for particular applications, in many real-world scenarios different information items may be attached to formulas. For instance, when the reliability of the source of the piece of information is attached to it as a measure of some quality (e.g., strength) of the piece of information itself, or when some characteristic informs us on the desirability of the item (e.g., the potential benefit that could be obtained from it). If this type of information is available, we can use it to guide how the belief base is to be modified when new information arrives. In this work, we present a novel approach to the contraction of knowledge bases where formulas have values attached that measure some quality linked to those formulas, exploiting it to define their desirability, and uses such desirability to define which formulas need to be removed to solve conflicts. In this context, we introduce a set of properties for contraction operators by extending classic approaches. We also show how the local treatment of minimal conflicts can induce some counter-intuitive contractions, and we present a way to avoid them by considering optimal resolutions of conflicts using the additional information encoded. We show how the proposed formalization captures any contraction that is optimal under a set of features. Finally, we present a refinement based on the identification of related minimal conflicts that performs contraction in optimal ways without looking into the entire knowledge base. The approach is based on the use of the accrual of beliefs where several formulas collaboratively use their respective values to prevail in the resolution of conflicts.
Enhanced e-Learning Experience using Case based Reasoning Methodology

In recent year's improvement in innovation includes new limits for verifying data that will incite essential changes in eLearning.The user can see e-learning material subject to the reference given to them and select the best approach to see the resources.This proposed system addresses retrieval, reuse, revise and retain phases of CBR.For building personalized e-Learning, this work identifies different feature set such as learning style, learning object, knowledge level, and problem list.For constructing this model used case-based reasoning along with a k-nearest neighbour.Role of the Knearest neighbour method is to identify the perfect k factor for better analysis for calculation of accurate retrieval process.There is further addition of new cases based on the simulation of new user history limit to a certain threshold value.This model acquires dynamically incremental dataset for classification.Further, there is time and accuracy comparison on dataset done by K-nearest neighbour, decision tree and support vector machine.Eventually, eLearning spares time, upgrades the learning knowledge and gives scholarly achievement.
A Novel K-means-based Feature Reduction

The aim of feature reduction is reduction of the size of data file, elimination of irrelevant features, and discovery of the effective data features for data analysis. Irrelevant data features can skew data analysis such as data clustering. Therefore, maintaining the data structure or data clusters must be taken into consideration in feature reduction. In this article, with regard to the success of k-means-based clustering methods, a feature reduction method is presented based on weighted k-means (wk-means). More specifically, firstly, data features are weighted using wk-means method. A feature with a high weight is not a better feature for clustering than a feature with a low weight, necessarily, and the weight of a feature only change feature range for better clustering. Then, by using a novel mathematical model, a group of weighted features with the least effect on data clusters are eliminated and the remaining features are selected. Contrary to sparse k-means method, the number of selected features can be determined explicitly by the user in our proposed method. Experimental results on four real datasets show that the accuracy of clusters obtained by wk-means after feature reduction by the proposed method is better than that of sparse k-means, PCA and LLE.
Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning

Multi-agent reinforcement learning has gained lot of popularity primarily owing to the success of deep function approximation architectures. However, many real-life multi-agent applications often impose constraints on the joint action sequence that can be taken by the agents. In this work, we formulate such problems in the framework of constrained cooperative stochastic games. Under this setting, the goal of the agents is to obtain joint action sequence that minimizes a total cost objective criterion subject to total cost penalty/budget functional constraints. To this end, we utilize the Lagrangian formulation and propose actor-critic algorithms. Through experiments on a constrained multi-agent grid world task, we demonstrate that our algorithms converge to near-optimal joint action sequences satisfying the given constraints.
An outdoors obstacle detection algorithm based on disparity slicing

In this paper, we propose a new disparity-based obstacle detection method for intelligent vehicle. The disparity maps captured by the stereo vision system ZED is utilized to detect obstacles in outdoor environment. This method consists of several steps namely, histogram calculation based on the disparity map, peaks finding using Non-Maximum Suppression (NMS), disparity slicing and obstacle detection using contours. The first step is to obtain the distribution of the pixels in the disparity map. The second step is performed to get the peaks in the histogram after the binary processing on the disparity map. However, there are always many noise objects in the binary image. The real obstacle area needs to be separated from the noise obstacles. Thus, the third step is performed to filter the binary image based on a statistics by column and then segment the detected obstacle area. The last step is to calculate the obstacle including its size, distance and position on the disparity map. A series of experiments prove that the proposed method is accurate at detecting obstacles and calculating the required information based on stereovision techniques.
POWER AND SOLIDARITY REFLECTED IN THE USE OF PRONOUNS PORTRAYED IN HYBRID POLITICAL DISCOURSE

This research examined power and solidarity reflected in the use of pronouns in President Barrack Obama’s presidential speech at the University of Indonesia by applying critical discourse analysis. The speech can be called as a hybrid political discourse individual because he was a hybrid, an ex-Indonesian. Power and solidarity in hybrid political discourse can be explored by analysing language usage in speech and wordings in speech texts. Furthermore, the analysis of language usage to examine power and solidarity has a close relation to ideology. Power and solidarity are like two sides of a coin. There will be an ambiguity of determing power or solidarity used by one participant to another in delivering utterances. Power and solidarity were linguistically expressed by President Obama in his speech at University of Indonesia in the use of pronouns. He used power to show America’s hegemony toward Indonesia and solidarity to to minimize the gab between America and Indonesia. President Obama used power and solidarity in order that Indonesian people will accept him (America) as friend, to show that there is no fight between America (western people/christian) and Indonsia (moslem world). Moreover, Obama applied these expressions are to secure the America’s politics against for terrorism, America wanted that Indonesia will be its friend to fight terrorism and moslem radicals, and Indonesia will be a big new markets for the American’s goods. Thus, the phenomenon of solidarity, in this sense, cannot be meant only that President Barrack Obama was familiar and close with Indonesia people, but he has another agenda in order that the US interests in Indonesia will be accepted by The Indonesian People. Furthermore, Obama has any concessions that he should do this in order to ‘demonstrate’ that America would like to teach their moral, their ideas, their values and political leadership in the general public, particulary their central claim to political legitimacy.
Personality traits in the light of the effectiveness of transformational vocational school leadership and leaders

The Big Five Personality Traits very well known as five-factor model (FEM), which is a taxonomy for personality traits, has a an effect on Transformational School Leadership and Leaders. Personality traits are the most powerful pylons for raising educational standards and achieving excellence based on the effectual Leadership. Openness to experience, conscientiousness, extraversion, agreeableness and neuroticism constitute the FEM. Transformational School Leadership model that comprises an essential element of quality assurance is based on the constructs of Setting Directions, Developing People, Redesigning the Organisations, Reliability of Administrative Leadership. Measures of Teachersâ€™ Capacities, Measures of Teachersâ€™ Motivation, Measures of Teachersâ€™ Work Setting and Measures of Teachersâ€™ Classroom Practices. To test the research hypotheses, a survey was carried out on 215 Greek secondary education teachers teaching in vocational lyceums, senior high schools in Greece. Moreover, 215 Greek secondary Vocational education teachers evaluate the effect of Personality Traits and characteristics on Transformational Vocational School Leadership and Leaders.&#x0D; Keywords: Personality traits, leadership, vocational, education.
Application research of denoising and super pixel algorithm in image processing

with the popularization and development of science and technology, mobile phone, tablet and computer has become the necessities of people, whether work or life, the emergence of science and technology, development and rich brought a whole new world for human civilization, including electronic information in time and space communication provides convenient conditions for people, especially the image processing technology, in the life is very broad. At present, smart phones have become extremely common, and users have a huge demand for images. Every link is inseparable from the formation, acquisition, transmission and acceptance of images. However, in every link, images will be more or less polluted by noise, resulting in users' inability to obtain the desired image effect. However, if the noise is directly optimized or removed, the accuracy of the image will be affected. Therefore, the advanced noise removal technology plays a crucial role in the efficient use of the image. Image superpixel is to gather pixels with similar attributes into a region to represent the image instead of pixels, so as to reduce the order of magnitude of the image atomic structure and further reduce the complexity of the subsequent image processing algorithm, which provides the possibility for the real-time performance of the image processing algorithm.
A Distributed Algorithm for Fast Mining Frequent Patterns in Limited and Varying Network Bandwidth Environments

Data mining is a set of methods used to mine hidden information from data. It mainly includes frequent pattern mining, sequential pattern mining, classification, and clustering. Frequent pattern mining is used to discover the correlation among various sets of items within large databases. The rapid upward trend in data size slows the mining of frequent patterns. Numerous studies have attempted to develop algorithms that operate in distributed computing environments to accelerate the mining process. FLR-mining (Fast, Load balancing and Resource efficient mining algorithm) is one of the fastest methods of mining with efficient consideration of load balancing and resources. FLR-mining can automatically determine the appropriate number of computing nodes. However, FLR-mining and existing methods assume that the network bandwidth is constant. In practical distributed and many-task computing systems, this assumption fails because there are packet collisions caused by many mining tasks that run in a simultaneous manner. Therefore, a method that can consider the varying network bandwidth is necessary. In this study, we propose a method that can rapidly mine frequent patterns under the varying network bandwidth. The proposed method can also determine the appropriate number of computing nodes to efficiently utilize computing resources and achieve load balancing. Through empirical evaluation, the proposed method is shown to deliver excellent performance in terms of execution efficiency and load balancing.
Deciding the Winner of a Debate Using Bipolar Argumentation

Bipolar Argumentation Frameworks (BAFs) are an important class of argumentation frameworks useful for capturing, reasoning with, and deriving conclusions from debates. They have the potential to make solid contributions to real-world multi-agent systems and human-agent interaction in domains such as legal reasoning, healthcare and politics. Despite this fact, practical systems implementing BAFs are largely lacking. In this demonstration, we provide a software system implementing novel algorithms for calculating extensions (winning sets of arguments) of BAFs. Participants in the demonstration will be able to input their own debates into our system, and watch a graphical representation of the algorithms as they process information and decide which sets of arguments are winners of the debate.
Building Information Systems Using Collaborative-Filtering Recommendation Techniques

IoT-technologies allow for the connection of miscellaneous devices, thereby creating a platform that sustains rich data sources. Given the circumstances, it is essential to have decent machinery in order to exploit the existing infrastructure and provide users with personalized services. Among others, recommender systems have been widely used to suggest users additional items that best match their needs and expectation. The use of recommender systems has gained considerable momentum in recent years. Nevertheless, the selection of a proper recommendation technique depends much on the input data as well as the domain of applications. In this work, we present an evaluation of two well-known collaborative-filtering (CF) techniques to build an information system for managing and recommending books in the IoT context. To validate the performance, we conduct a series of experiments on two considerably large datasets. The experimental results lead us to some interesting conclusions. In contrast to many existing studies which state that the item-based CF technique outperforms the user-based CF technique, we found out that there is no distinct winner between them. Furthermore, we confirm that the performance of a CF recommender system may be good with regards to some quality metrics, but not to some others.
METHODOLOGICAL APPROACHES TO FORMATION PROFESSIONAL AND ETHICAL CULTURE OF GRAPHIC DESIGNERS

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HIV-TB Treatment Pictogram Tool Designed from Semiotic Analysis for Community Pharmacists in India

McDonald et al.: HIV-TB Pictograms to Support Pharmacy Counselling A pictogram-based counselling pamphlet for pharmacists in India for HIV-TB co-infection and treatment was developed.Pictograms have been shown to improve health literacy and adherence by increasing retention and comprehension of health information, especially in persons with lower levels of literacy.The development of the draft pictogram pamphlet for HIV-TB treatment was based on semiology and adaptation of existing pictograms and incorporation of key counselling points identified in a literature review.A total of 103 articles were reviewed and 21 (n=9 for HIV; n=12 for TB) were identified as applicable to pharmacist counselling and were used in the development of key counselling points.Of the 22 pictograms in the draft pamphlet, n=5 were redesigned based on existing pictograms, n=11 were based on semiotic analysis of existing pictograms, n=5 were based on semiology of a web-based image search, and n=1 was developed in consultation with Indian pharmacists.The future goal is to validate the pictogram pamphlet as there is a scarcity and oversimplification of validated pictograms targeted to counselling patients with HIV-TB.Although validation of more complicated pictograms in a low-literacy population may be challenging, the aim of pictograms is to enhance recall and deliver the pictogram information in combination with verbal instruction in order to improve adherence and patient outcomes.
Exploration in the Face of Parametric and Intrinsic Uncertainties

In distributional reinforcement learning (RL), the estimated distribution of the value functions model both the parametric and intrinsic uncertainties. We propose a novel, efficient exploration method for Deep RL that has two components. The first is a decaying schedule to suppress the intrinsic uncertainty. The second is an exploration bonus calculated from the upper quantiles of the learned distribution. In Atari 2600 games, our method achieves 483% average gain in cumulative rewards over QR-DQN.
Contemporary Trends in Engineering Entrepreneurship Education

This study investigated the contemporary dimensions of engineering entrepreneurship education aiming at creating alandscape of the most important theories and trends found in the literature, mapping the most important authors,countries, and journals and the often-used research methods. The research methodology included a systematic literaturereview, combining bibliometrics, networks, and contents analysis. The sampling process was conducted in the Web ofScience and Scopus databases, with two filters: research areas (engineering field) and document types (articles and reviews).The sample of 324 articles published from 2001 to 2017 was analyzed in-depth. The results show an exponential growth ofinterest in the study of engineering entrepreneurship education, with 74% of the samples published in the last 3 years. Thereis a trend of transition towards a more confirmatory research perspective. There is an increasing focus of the literature onthe development of educational programs and methodologies and, consequently, on the evaluation of the results. Twodimensions of contemporary engineering entrepreneurship education stood out: the influence of entrepreneurshipeducation on students’ entrepreneurial intention and the definitions of entrepreneurship education. Finally, three mainclusters of the literature were identified: entrepreneurial behavior and entrepreneurial intention; entrepreneurshipeducation; and entrepreneurship education challenges, results, and best practices.
Power equipment status information parallel fault diagnosis of based on MapReduce

A diagnosis algorithm against status information parallel faults occured on power equipment is analyzed and explored based on MapReduce. Here comes the parallelized Naive Bayes algorithm and transformer DGA test which help understand this algorithm by 4 MapReduce processes, coupled with the dissolv ed gas parameters of transformers given here. It turns out that the transformers can be diagnosed by Hadoop clusters when they reach a certain scale, as shown in the experiment.
PENGARUH POLA ASUH ORANG TUA TERHADAP KEMANDIRIAN BELAJAR SISWA

This type of research is quantitative with a causal approach consisting of independent variables (parenting parents) and dependent variables (learning independence). The purpose of this study is to describe the effect of parenting on the independence of student learning. Collecting research data using a five-scale questionnaire instrument. To find the magnitude of the effect of parenting parents using the statistical calculation model Carl Person Corelation Product Moment. For reliability calculated using the Cronbach Alpha formula. A sample of 49 students was obtained using the Taro Yamane formula. Analysis prerequisite testers are Liliefors normality test. After the normality test, the homogeneity test is done by Fisher's test. Instruments that are valid and reliable are used to collect data on the results that show there is an influence of parenting parents on student learning independence. Simple regression analysis and correlation techniques produce a model of influence expressed in the form of a regression equation that is Y ? = (87.22+ 0.06X), with the F value of 0.281 greater than F table with a level of 0.05 of 4.06 and its contribution is 49%. The conclusion of the study is that there is a positive effect of parenting on the learning independence of students in fifth grade of Cibubur Public Elementary School 03 Odd Semester Cibubur 03 in the odd semester of the 2018/2019 academic year.
Domain Specific Code Smells in Smart Contracts.

Smart contracts are programs running on a blockchain. They are immutable topatch for bugs once deployed -- it is critical to ensure they are bug-free andwell-designed before deploying. Code smells are symptoms in source code thatpossibly indicate deeper problems. The detection of code smells is a method toavoid potential bugs and improve the design of existing code. However,traditional code smell patterns are designed for centralized OO programs, e.g.,Java or C++; while smart contracts are decentralized and contain numerousdistinctive features, such as the gas system. To fill this gap, we collectedsmart-contract-related posts from Stack Exchange, as well as real-world smartcontracts. We manually analyzed these posts and defined 20 kinds of \emph{codesmells for smart contracts. We categorized these into security, architecture,and usability problems. To validate if practitioners consider these contractsmells as harmful, we created an online survey and received 96 responses from24 different countries. Feedback showed these code smells are harmful andremoving them would improve quality and robustness of smart contracts. Wemanually identified our defined code smells in 587 contract accounts andpublicly released our dataset. Finally, we summarized 5 impacts caused bycontract code smells. These help developers better understand the symptoms ofthe smells and removal priority.
Teorías pedagógicas que sustentan el aprendizaje con el uso de las Tecnologías de la Información y las Comunicaciones

Introduction: information and communication technologies are present in all spheres of social, labor and educational life. Objective: analyze models that are used in learning processes with the use of information and communications. Method: a pedagogical study was carried out where some of the models that are used recently in the learning processes were analyzed and emphasis is placed on the need to implement training actions to eliminate the digital and cognitive gaps that exist in many countries. Discussion: one of the most important characteristics of collaborative work through social networks is that the media makes it easier for users to be connected and to be able to share the content they generate on a global scale. Conclusions: information and communication technologies foster a significant change in the methodological and didactic order within any organization or educational institution.