Reviewerly/exHarmony huggingface dataset:
Importance-Aware Learning for Neural Headline Editing

Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more readers. We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers. To train such a neural headline editing model, we collected a dataset which contains articles with original headlines and professionally edited headlines. However, it is expensive to collect a large number of professionally edited headlines. To solve this low-resource problem, we design an encoder-decoder model which leverages large scale pre-trained language models. We further improve the pre-trained model's quality by introducing a headline generation task as an intermediate task before the headline editing task. Also, we propose Self Importance-Aware (SIA) loss to address the different levels of editing in the dataset by down-weighting the importance of easily classified tokens and sentences. With the help of Pre-training, Adaptation, and SIA, the model learns to generate headlines in the professional editor's style. Experimental results show that our method significantly improves the quality of headline editing comparing against previous methods.
Sharing data in Ecology: how to add more value to data

Pérez-Luque, A.J., Ros-Candeira, A. 2019. Sharing data in Ecology: how to add more value to data. Ecosistemas 28(3):150-159. Doi.: 10.7818/ECOS.1838 Data is one of the valuable products of science. Their preservation, accessibility and reuse in ecology is crucial, given the importance of data sets for understanding complex ecological questions and/or solving emerging environmental problems. Although most researchers recognize the importance of sharing data, there are different social and technological barriers that limit the value of data, slowing down possible advances in ecological research. In this paper we review the different practices of data publication in ecology and present a series of ideas to add "more" value to the data through their documentation and publication. We also present a case study showing different approaches to data sharing. Throughout the review we included different resources and tools to help us maximize the value of the data we generate and facilitate access to other users.
Performance analysis of DCT and successive division based digital image watermarking scheme

In this article, a combined Discrete Cosine Transformation (DCT) and Successive Division based image watermarking scheme is proposed. In many spatial domain approaches, the watermark information is embedded into Least Significant Bits (LSBs) of host image. These LSBs are more vulnerable to noise and other unwanted information contents in the channel, in few cases these are subjected for modifications also. Many frequency domain approaches withstands LSB interference problem but utilizes more execution time. The proposed technique is a frequency domain approach which can withstand LSB attack and utilizes very less execution time than other existing approaches. Performance analysis is done based on robustness, imperceptibility, data embedding capacity and time of execution. The experimental results are better compared to other existing techniques.
An Operational Semantics for True Concurrency in BDI Agent Systems

Agent programming languages have proved useful for formally modelling implemented systems such as PRS and JACK, and for reasoning about their behaviour. Over the past decades, many agent programming languages and extensions have been developed. A key feature in some of them is their support for the specification of ‘concurrent’ actions and programs. However, their notion of concurrency is still limited, as it amounts to a nondeterministic choice between (sequential) action interleavings. Thus, the notion does not represent ‘true concurrency’, which can more naturally exploit multi-core computers and multi-robot manufacturing cells. This paper provides a true concurrency operational semantics for a BDI agent programming language, allowing actions to overlap in execution. We prove key properties of the semantics, relating to true concurrency and to its link with interleaving.
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks

Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algorithm uses each transition to learn Q-values towards all goals simultaneously and off-policy. However the expensive numerous updates in parallel limited the approach to small tabular cases so far. To tackle this problem we propose to use convolutional network architectures to generate Q-values and updates for a large number of goals at once. We demonstrate the accuracy and generalization qualities of the proposed method on randomly generated mazes and Sokoban puzzles. In the case of on-screen goal coordinates the resulting mapping from frames to distance-maps directly informs the agent about which places are reachable and in how many steps. As an example of application we show that replacing the random actions in ε-greedy exploration by several actions towards feasible goals generates better exploratory trajectories on Montezuma's Revenge and Super Mario All-Stars games.
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization

Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence. This is especially more crucial in dynamic optimization in which diversity can ensure that the population keeps track of the global optimum by adapting to the changing environment. Dynamic constrained optimization problems (DCOPs) have been the target for many researchers in recent years as they comprehend many of the current real-world problems. Regardless of the importance of diversity in dynamic optimization, there is not an extensive study investigating the effects of diversity promotion techniques in DCOPs so far. To address this gap, this paper aims to investigate how the use of different diversity mechanisms may influence the behavior of algorithms in DCOPs. To achieve this goal, we apply and adapt the most common diversity promotion mechanisms for dynamic environments using differential evolution (DE) as our base algorithm. The results show that applying diversity techniques to solve DCOPs in most test cases lead to significant enhancement in the baseline algorithm in terms of modified offline error values.
Implementing Technology: Measuring Student Cognitive Engagement

In an effort provide access to devices and prepare students for the future, a small rural high school committed to becoming a high-tech school. However, data collected using the IPI-T process suggested teachers were typically the users of the technology, students were often disengaged, and teachers were asking students to participate in lower-order surface activities. Missing from the process was the implementation of the faculty collaborative sessions. The year after the initial rollout of the devices, IPI-T data was collected three times. Additionally, faculty collaborative sessions were planned and facilitated within one week of collection data. Participating in each faculty collaborative session, teachers (a) became familiar with the IPI-T Rubric and Protocols, (b) analyzed and discussed the data, (c) identified high-quality examples of student learning that foster student engagement with technology, (d) designed high-quality lessons that foster student engagement with technology, (e) compared longitudinal data and set goals for future data collection using the IPI-T tool. An analysis of the data revealed when implementing the IPI-T process with fidelity teacher and student technology use increased as did student cognitive engagement when using technology. In addition, it was found that students use technology for information searches the majority of the time rather than media development or to collaborate among peers for example, which is associated with higher-levels of cognitive engagement.
Query Answering with Guarded Existential Rules under Stable Model Semantics

In this paper, we study the problem of query answering with guarded existential rules (also called GNTGDs) under stable model semantics. Our goal is to use existing answer set programming (ASP) solvers. However, ASP solvers handle only finitely-ground logic programs while the program translated from GNTGDs by Skolemization is not in general. To address this challenge, we introduce two novel notions of (1) guarded instantiation forest to describe the instantiation of GNTGDs and (2) prime block to characterize the repeated infinitely-ground program translated from GNTGDs. Using these notions, we prove that the ground termination problem for GNTGDs is decidable. We also devise an algorithm for query answering with GNTGDs using ASP solvers. We have implemented our approach in a prototype system. The evaluation over a set of benchmarks shows encouraging results.
Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

Privacy has become a major concern in data mining as it is utilized in many important applications. Distributed privacy-preserving data mining (DPPDM) is one of the techniques to address this concern, which focuses on protecting private information of members in distributed systems during data mining. As DPPDM is widely discussed in recent works, the semi-supervised manner of learning still draws less attention in this field. In this paper, a mixture-model-based semi-supervised DPPDM method is proposed. By introducing our method, a site in a distributed system is able to initiate a learning process using labeled data of its own and unlabeled data from all the sites. During the process, no individual data of any site is revealed to others, no information about data can be traced back to any specific site, and only the initiating site learns the result. We propose a parameter-masking privacy-preserving Expectation-Maximization (EM) algorithm and a mixture-model-based semi-supervised learning algorithm as the two main steps of our method. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method.
Gas Source Declaration With Tetrahedral Sensing Geometries and Median Value Filtering Extreme Learning Machine

Gas source localization (including gas source declaration) is critical for environmental monitoring, pollution control and chemical safety. In this paper we approach the gas source declaration problem by constructing a tetrahedron, each vertex of which consists of a gas sensor and a three-dimensional (3D) anemometer. With this setup, the space sampled around a gas source can be divided into two categories, i.e. inside (“source in”) and outside (“source out”) the tetrahedron, posing gas source declaration as a classification problem. For the declaration of the “source in” or “source out” cases, we propose to directly take raw gas concentration and wind measurement data as features, and apply a median value filtering based extreme learning machine (M-ELM) method. Our experimental results show the efficacy of the proposed method, yielding accuracies of 93.2% and 100% for gas source declaration in the regular and irregular tetrahedron experiments, respectively. These results are better than that of the ELM-MFC (mass flux criterion) and other variants of ELM algorithms.
Exploratory Factor Analysis of a Digital Competency Questionnaire for Research

Currently, citizens find themselves in a technological environment that demands a series of digital skills that must be developed to perform adequately, either in society or in any type of profession. It is of our interest that these competences allow students to perform satisfactorily in the academic field. Thus, the need to establish the level of digital skills that students require in order to carry out scientific research in a satisfactory manner. For this reason, an instrument of self-perception was adapted that gives a diagnosis of digital competences for research conducted by new university students. In order to carry out the analysis of reliability and validity of the instrument, a pilot test was implemented with a representative sample of 71 new students, belonging to a military engineering university in Veracruz, Mexico. As a result of the analysis, the instrument obtained a remarkable reliability, yielding a Cronbach’s Alpha of 0.890 and its construct validity was average, obtaining a global Kaiser Meyer Olkin index of 0.718. Regarding the level of competence detected, a better perception of development was obtained in the students coming from private schools and technological baccalaureates, while the lowest levels were found in those coming from CONALEP, COBAEV and tele-baccalaureates. In light of the results, it is important to visualize the factors that can affect the academic development of students in order to allow them to enter universities with a better expectation of success.
IMPROVING CLASSIFICATION PERFORMANCE OF NEURO-FUZZY CLASSIFIER BY IMPUTING MISSING DATA

In medical data classification, if the size of data sets is small and if it contains multiple missing attribute values, in such cases improving classification performance is an important issue. The foremost objective of machine learning research is to improve the classification performance of the classifiers. The number of training instances provided for training must be sufficient in size. In the proposed algorithm, we substitute missing attribute values with attribute available domain values and generate additional training tuples that are in addition to original training tuples. These additional, plus original training samples provide sufficient data samples for learning. The neuro-fuzzy classifier trained on this dataset. The classification performance on test data for the neuro-fuzzy classifier is obtained using the k-fold cross-validation method. The proposed method attains around 2.8% and 3.61% improvement in classification accuracy for this classifier.
The Analysis of Teacher Talk and The Characteristic of Classroom Interaction in English for Young Learner

ABSTRACT&#x0D; This study was aimed to find out the type of teacher talk and the characteristic of classroom interaction take place in the English language classroom of Semarang Multinational School.This study applied descriptive qualitative research. There were two activities in gathering the data of this study: observation and audio recording. In analyzing this study, the researcher used interactive theory proposed by Flander (1989). The findings showed that based on Flanders Interaction Analisys Categories (FIAV), the teacher indirectly influenced the students in teaching and learning process by relying hard on asking questions. Besides asking questions to students, teacher also used (1) accepting students’ feeling, (2) praising or encouraging students, and (3) accepting or even using students’ ideas. In addition the pattern of content cross interaction tended to be more on asking questions than lecturing behavior. It indicated that teacher often relied on asking questions to students in the teaching and learning process rather than to introduce new learning material and help conveying information to students. In conclusion, the teacher indirectly influenced the students in the teaching and learning process. This kind of interaction indicated that teacher often relied on asking rather than lecturing the students.&#x0D; Keywords: Teacher Talk, Classroom Interaction, Flanders Interaction Analysis Categories
Augmented Reading Through Emerging Technologies: The Living Book Approach to Teachers’ Professional Development

Recognizing the importance of reading, the EU Education and training 2020 (ET 2020) benchmarks set in 2009, included having less than 15% of 15-year-olds being under-skilled in reading by 2020 as one of its main targets. Unfortunately, while some EU countries have since made significant progress toward improving their students’ performance in reading skills, other countries are still lagging behind. In an attempt to respond to this challenge and to address the under-achievement of European students in reading, the EU-funded Living Book—Augmenting Reading for Life project has developed an innovative approach that combines off-line activities promoting reading literacy with online experiences of books’ “virtual augmentation.” A professional development program targeting teachers of upper primary and lower secondary school (ages 9–15), which aims at strengthening teachers’ profile and competences in adopting the Living Book approach and in dealing with diversified groups of learners, and particularly with pupils from disadvantaged backgrounds has been designed within the project, and is currently being pilot tested in five partner countries. The current chapter describes the content and structure of the “Augmented Teacher” professional development course, and reports on some of the initial insights gained from the pilot delivery taking place in one of the partner countries, Cyprus.
Application of Ontology and Multivariate Decision Diagram in Cloud Monitor Systems

Cloud computing is different from distributed computing and grid computing and has its own characteristics.The existing cloud system is not sufficient in the unified identification of cloud resources and the dynamic joining management of new resources.According to the characteristics of cloud computing, this paper introduces the idea of ontology on cloud monitor system (CMS) based on bionic autonomic nervous system (BANS) and uses ontology web language (OWL) language to describe the resources of the system.It also establishes a reusable extended resource expression model.At the same time, the use of the third-party tool Jena for OWL semantic query also gives the monitoring system the characteristics of a rapid semantic query, which further enhances the convenience of cloud resource management.In addition, based on the application of ontology, we also introduce multivariate decision diagram (MDD) multi-valued decision graph technology, which allows B-CMS to self-diagnose complex system faults.The combination of ontology and MDD greatly simplifies the monitoring and management of large-scale systems, providing a fast and standardized means for the intelligent diagnosis of systems.
The Use of Prezi with KWL Strategy to Enhance Students Reading Comprehension

The objectives of this research were to find out whether the use of Prezi with KWL Strategy enhances the reading comprehension and the students’ interest. This research employed quasi experimental design. The population of this research was the first grade students of SMA 2 Enrekang in academic 2017/2018. This research employed cluster random sampling technique. The sample consisted of 72 students which belong to two classes; 36 students in experimental class and also 36 students in control class. Research instruments were used to collect the data in this research namely reading comprehension test and questionnaire. The result of the students’ reading score in this research showed that the experimental class improved significantly from 69.69 up to 86.61. While, in control class the result improved from 66.22 up to 75.89. Therefore, the significant values of Independent t-test 0.000 &lt; α = 0.05. It means that the alternative hypothesis (H1) was accepted. Moreover, based on the students’ response on questionnaire showed that the mean score of interest was 80.61 and it was categorized as interested.
Improving the Capability of Prospective Primary School Teachers in Making Science-Based Science Teaching Materials Based on ICT Media Assisted Literacy

In general, this study aims to improve the ability of prospective elementary school teachers to make science teaching materials based on ICT literacy based on ICT media.Specifically, to develop the ability of prospective elementary school teachers to make teaching materials based on scientific literacy.The method used in this study is a quasi-experimental research design using Nonrandomized pretest-posttest control group design.The population in this study were all PGSD IKIP Siliwangi semester 4 students.The sample in this study was 2 regular classes with 60 students.The instruments used include tests and observation sheets.The results showed that the value of learning using teaching resources based on ICT literacy based on ICT media is better than conventional learning.
Difficulties in diagnosing the emotional-evaluative component of primary schoolchildren’s value-based attitudes

The article is devoted to the description of the approach to assessing the dynamics of personal achievements in primary school in accordance with the requirements of the Federal State Educational Standard of Primary General Education. The new goal of spiritual and moral development of schoolchildren has required us to review the old technocratic paradigm of diagnosing educational results. The central aim of education today is to support the formation of a child’s value attitudes system, therefore, the teacher must be able to qualitatively assess the process of child’s moral and spiritual development. The importance of such work at primary school age is highlighted by the author, who describes the evaluation system designed to measure the development of value attitude components. Emphasis is placed on the assessment of the emotional-evaluative component being the most complex. The key difficulties of administering assessment and interpreting the obtained diagnostic results are revealed. They are instability in component demonstration, the influence of reference adults, constant change of the studied component and the whole value-semantic structure of the child’s personality during this particularly sensitive period. The author suggests possible ways of overcoming the difficulties by means of several techniques, repetition to establish the stability of the emotional reaction, discussion of the results with the children in order to create the atmosphere of trust and openness, comparison between the manifestation of the studied component with others and the manifestation of the value attitude as a whole. The paper refers to the titles, authors, and descriptions of the methods, and presents the data obtained as a result of research conducted over several years. The author describes the qualitative characteristics of the levels of emotional-evaluative component of value attitudes in accordance with the proposed criteria and indicators, and provides specific examples of the qualitative characteristics of the emotional-evaluative component of the value attitude to family, nature, and homeland. The article emphasises the importance of correct identification and interpretation of single components component and the value attitude as a whole in the organisation of an effective education process.