Reviewerly/exHarmony huggingface dataset:
Deverbal zero-nominalization and verb classes: Insights from a database

We investigate deverbal zero-derived nominals in English (e.g., to walk &gt; a walk) from the perspective of the lexical semantics of their base verbs and the interpretations they may receive (e.g., event, result state, product, agent). By acknowledging that, in the absence of an overt affix, the meaning of zero-nominals is highly dependent on that of the base, the ultimate goal of this study is to identify possible meaning regularities that these nominals may display in relation to the different semantic verb classes. We report on a newly created database of 1,000 zero-derived nominals, which have been collected for various semantic verb classes. We test previous generalizations made in the literature in comparison with suffix-based nominals and in relation to the ontological type of the base verb. While these generalizations may intuitively hold, we find intriguing challenges that bring zero-derived nominals closer to suffix-based nominals than previously claimed. Dieser Beitrag ist ursprünglich im Peter-Lang-Verlag erschienen (https://www.ingentaconnect.com/contentone/plg/jwf/2020/00000004/00000002/art00007)
A Formalization of Emotional Planning for Strong-Story Systems

Characters that are capable of expressing emotions seem more lifelike to a human audience. Interacting with emotional characters may cause a human user to feel empathy and to care for their fate. In turn, the audience may feel more engaged in the story. We take a step to address the lack of computational models of emotions for strong-story systems and propose to extend strong-story state-space narrative planners, already equipped with intentionality and belief, to reason about character emotions. Our proposed system is multi-agent, highly domain-independent, and focused on reasoning and decision making. In this paper, we evaluate to what extent our system can accurately model a set of emotions and in doing so, improve the believability of story characters.
Community‐based data integration of course and job data in support of personalized career‐education recommendations

Abstract How does your education impact your professional career? Ideally, the courses you take help you identify, get hired for, and perform the job you always wanted. However, not all courses provide skills that transfer to existing and future jobs; skill terms used in course descriptions might be different from those listed in job advertisements; and there might exist a considerable skill gap between what is taught in courses and what is needed for a job. In this study, we propose a novel method to integrate extensive course description and job advertisement data by leveraging heterogeneous data integration and community detection. The innovative heterogeneous graph approach along with identified skill communities enables cross‐domain information recommendation, for example, given an educational profile, job recommendations can be provided together with suggestions on education opportunities for re‐ and upskilling in support of lifelong learning. Note: This work was partially supported by the National Science Foundation under award 1,936,656. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
University Students' Ability in Evaluating Fake News on Social Media

Background of the study: Social media has become a traffic information exchange, both true and false information. Therefore, social media users should not simply believe the information they received. This paper investigates the process of evaluating news on social media carried out by students in assessing the news they find on social media, and their ability to distinguish factual and false news.Purpose: It is to find out more about the process of students evaluating news on social media by assessing the news they found on social media, and how can they know which is the factual news and which is the fake news.Method: This research uses qualitative research methods with a descriptive approach. This research was conducted at Gadjah Mada University. The purposive sampling technique was chosen to be used in determining participants.Findings: Overall, participants were able to identify almost all news articles. Participants are able to identify almost all factual news articles correctly and most fake news articles correctly. Only a small portion of all news articles cannot be correctly identified by participants. Participants are better to identify factual news than fake news.Conclusion: Although participants already have experience in finding fake news on social media and have self-taught knowledge about how to distinguish fake news from reliable news, this is no guarantee that they can tell the news article they got fake news or factual news. The researcher wants to give advice to the academic library to provide training on the characteristics of reliable referral sources and to think critically in assessing information as part of student information literacy training.
Multiple Bipolar Fuzzy Measures: An Application to Community Detection Problems for Networks with Additional Information

In this paper we introduce the concept of multiple bipolar fuzzy measures as a generalization of a bipolar fuzzy measure.We also propose a new definition of a group, which is based on the multidimensional bipolar fuzzy relations of its elements.Taking into account this information, we provide a novel procedure (based on the well-known Louvain algorithm) to deal with community detection problems.This new method considers the multidimensional bipolar information provided by multiple bipolar fuzzy measures, as well as the information provided by a graph.We also give some detailed computational tests, obtained from the application of this algorithm in several benchmark models.
A Study of Sonar Image Stabilization of Unmanned Surface Vehicle Based on Motion Sensor for Inspection of Underwater Infrastructure

In order to detect damage to underwater infrastructure for inspection, an expensive survey by a diver is generally conducted, but it carries the risk of accidents. Accordingly, the development of an effective unmanned underwater survey system is an important priority. The unmanned underwater survey system used in this study is equipped with sonar towed by an- Unmanned Surface Vehicle (USV) to conduct the survey, but the USV is more affected by the waves and swells than a common boat. As a result, distorted sonar data causes errors in the information regarding the damage of underwater infrastructure. This study proposes the method of sonar image stabilization to minimize the errors of the distortion of sonar data by using a motion sensor. The change in the amount of the roll was calculated from the motion sensor, and the sonar data was corrected in the sonar ping unit. The sonar image stabilization algorithm was verified through field tests, and the error rate before and after correction was reduced by 15%. It is expected that, in the future, the proposed approach will be used as a standard data-gathering system for securing the reliability of sonar data when performing an unmanned underwater survey.
Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling

Doudizhu poker is a very popular and interesting national poker game in China, and now it has become a national competition in China. As this game is a typical example of incomplete information game problem, it has received more and more attention from artificial intelligence experts. This paper proposes a multirole modeling-based card-playing framework. This framework includes three parts: role modeling, cards carrying, and decision-making strategies. Role modeling learns different roles and behaviors by using a convolutional neural network. Cards carrying can calculate reasonable rules especially for “triplet” by using an evaluation algorithm. Decision making is for implementing different card strategies for different player roles. Experimental results showed that this card-playing framework makes playing decisions like human beings, and it can to some extent learn, collaborate, and reason when facing an incomplete information game problem. This framework won the runner-up in the 2018 China Computer Game Competition.
The Development Of Web-based Outcome Based Education Information System

The development of Outcome Based Education (OBE) Information System is done as a solution to academic problems related to the achievement of graduate learning for students. In addition, the OBE system is a form of effort to improve academic quality in order to be able to monitor and measure the development of student learning outcomes in a college study program. The Outcome Based Education system includes all grades processing based on Course Learning Outcomes which is related to Graduates Learning Outcomes so that the outcomes of each student can be measured. This information system is developed by the Rapid Application Development method, and uses the Yii2 Framework with the concept of MVC (Model, View, Controller) where system programming is separated based on application components, such as: manipulating data, controllers, and user interfaces. The use of the RAD method and the Yii2 Framework in making applications can be done quickly and in a structured manner making it easier for future developments. The developed system has been successfully tested and applied in the Electrical Engineering Study Program, Civil Engineering Study Program and Mechanical Engineering Study Program, and planned to be implemented in all study programs of the Faculty of Engineering, Sebelas Maret Universit&lt;em&gt;y.&lt;/em&gt; This information system can be applied not only in the engineering faculty of Sebelas Maret University but also outside the university which requires an information system to measure the learning outcomes of graduates.
SpecMiner: Heuristic-based mining of service behavioral models from interaction traces

Software behavioral models have proven useful for emulating and testing software systems. Many techniques have been proposed to infer behavioral models of software systems from their interaction traces. The quality of the inferred/mined model is critical to their successful use. While generalization is necessary to deduce concise behavioral models, existing techniques of inferring models, in general, overgeneralize what behavior is valid. Imprecise models include many spurious behaviors, and thus compromise the effectiveness of their use. In this paper, we propose a novel approach, named SpecMiner, that increases the precision of the behavioral model inferred from interaction traces. The essence of our approach is a heuristic-based generalization and truthful minimization. The set of heuristics include patterns to match input traces and generalize them towards concise model representations. Furthermore, we adopt a truthful minimization technique to merge these generalized traces. The key insight of our approach is to infer a concise behavioral model without compromising its precision. We present an empirical evaluation of how our approach improves upon the state-of-the-art specification inference techniques. The results show that our approach mines model with 100% precision and recall with a limited computation overhead.
Password Managers—It’s All about Trust and Transparency

A password is considered to be the first line of defence in protecting online accounts, but there are problems when people handle their own passwords, for example, password reuse and difficult to memorize. Password managers appear to be a promising solution to help people handle their passwords. However, there is low adoption of password managers, even though they are widely available, and there are fewer studies on users of password managers. Therefore, the issues that cause people not to use password managers must be investigated and, more generally, what users think about them and the user interfaces of password managers. In this paper, we report three studies that we conducted: on user interfaces and the functions of three password managers; a usability test and an interview study; and an online questionnaire study about users and non-users of password managers, which also compares experts and non-experts regarding their use (or non-use) of password managers. Our findings show that usability is not a major problem, rather lack of trust and transparency are the main reasons for the low adoption of password managers. Users of password managers have trust and security concerns, while there are a few issues with the user interfaces and functions of password managers.
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets

Machine learning models nowadays play a crucial role for many applications in business and industry.However, models only start adding value as soon as they are deployed into production.One challenge of deployed models is the effect of changing data over time, which is often described with the term concept drift.Due to their nature, concept drifts can severely affect the prediction performance of a machine learning system.In this work, we analyze the effects of concept drift in the context of a real-world data set.For efficient concept drift handling, we introduce the switching scheme which combines the two principles of retraining and updating of a machine learning model.Furthermore, we systematically analyze existing regular adaptation as well as triggered adaptation strategies.The switching scheme is instantiated on New York City taxi data, which is heavily influenced by changing demand patterns over time.We can show that the switching scheme outperforms all other baselines and delivers promising prediction results.
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference and Application

We formalize concepts around geometric occlusion in 2D images (i.e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation. The former provides a way to generate large-scale accurate occlusion datasets while, based on the latter, we propose a novel method for task-independent pixel-level occlusion relationship estimation from single images. Experiments on a variety of datasets demonstrate that our method outperforms existing ones on this task. To further illustrate the value of our formulation, we also propose a new depth map refinement method that consistently improve the performance of state-of-the-art monocular depth estimation methods.
Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution

Anomaly detection methods exploiting autoencoders (AE) have shown good performances. Unfortunately, deep non-linear architectures are able to perform high dimensionality reduction while keeping reconstruction error low, thus worsening outlier detecting performances of AEs. To alleviate the above problem, recently some authors have proposed to exploit Variational autoencoders (VAE), which arise as a variant of standard AEs designed for generative purposes. The key idea of VAEs is take into account a regularization term constraining the organization of the latent space. However, VAEs share with standard AEs the problem that they generalize so well that they can also well reconstruct anomalies. In this work we argue that the approach of selecting the worst reconstructed examples as anomalies is too simplistic if a VAE architecture is employed. We show that outliers tend to lie in the sparsest regions of the combined latent/error space and propose a novel unsupervised anomaly detection algorithm, called VAEOut, that identifies outliers by performing density estimation in this augmented feature space. The proposed approach shows sensible improvements in terms of detection performances over the standard approach based on the reconstruction error.
Use of mobile applications in collocation teaching

In this study, the importance of vocabulary knowledge in language education and the use of mobile applications in the teaching of collocations are emphasized. The application of collocation was realized through a mobile instant messaging application. It has been determined that students use a mobile application in a word learning process to provide more meaningful learning than in classroom learning. In this study, which was applied to 54 people using experimental and controls grouped research design, the experimental group was given mobile cards and the control group was given word cards in the class. In quantitative aspect, the study aimed to see the difference between the academic achievements of both group. At the end of the process, an academic achievement test was applied to the students, and a significant difference was found between the groups in favor of the experimental group. In qualitative aspect, the students opinions was given on use of mobile applications in teaching vocabulary. Individual interviews were conducted with the students at the end of the process accordingly and these interviews were used in the evaluation of the process.&#x0D; &#x0D; Keywords: Educational technology; language learning, mobile learning; teaching collocations.
A Preprocessing Approach for Class-Imbalanced Data Using SMOTE and Belief Function Theory

Dealing with imbalanced datasets at the preprocessing level is an efficient strategy used by many methods to re-balance the data and improve classification performance. Specifically, SMOTE is a popular oversampling technique which modifies the training data by adding artificial minority samples. However, SMOTE may create instances in noisy and overlapping areas, far from safe regions. To tackle this issue, we propose SMOTE-BFT, in which we use the belief function theory to remove generated minority instances that are not in safe regions. After applying SMOTE, each generated minority instance is represented by an evidential membership structure, which provides detailed information about class memberships. Rules based on the belief function theory are then enforced to detect and remove generated instances that are in noisy and overlapping regions. Experiments on noisy artificial datasets show that our proposal significantly outperforms other popular oversampling methods.
Robot Creativity: An Incentive-Based Neighboring Rights Approach

Today texts, paintings and songs need no longer be the result of human creativity. Advanced artificial intelligence (AI) systems are capable of generating creations that can hardly be distinguished from those of authors of flesh and blood. This development raises the question whether AI-generated works could be eligible for copyright protection. In the following analysis, we explore this question. After a discussion of the traditional copyright requirement of human creativity, the rationales underlying copyright protection – in particular the utilitarian incentive theory – will serve as a compass to decide on the grant of protection and delineate the scope of exclusive rights. In addition, the analysis will address the question who the owner of protected AI creations should be. Finally, the discussion of pros and cons of protection will be placed in the broader context of competing policy goals and legal obligations, such as the prospect of enriching the public domain and the question of liability for AI creations that infringe the rights of third parties.
Development of a toolkit for analyzing software debugging processes using the constructive approach

Constructive-synthesizing modeling and the Process Mining methods in a toolkit to monitor and analyze the software debugging process were applied. Methods for monitoring the development and debugging processes are the basis for improving the level of practical training of students, reducing the time that is used irrationally in the process of software development by a student, and in monitoring the processes of performance of tasks by a teacher. The process of software debugging is seen as a sequence of actions when dealing with relevant tools. Using the methodology of constructive-synthesizing modeling, a constructor for forming a debugging actions log was developed. Based on the constructive model, the extension to the integrated development environment (IDE) Microsoft Visual Studio, in which all debugging actions are recorded in an event log, was designed. During debugging in the IDE, event logs are collected and then a conformance checking of these logs with regard to the reference model is performed. To do this, the ProM (Eindhoven Technical University, Netherlands), a platform for Process Mining methods, is used. By checking compliance, it is possible to compare different debugging processes and recognize behavioral similarities and differences. The main purpose of the developed toolkit is to collect debugging actions from the developer's IDE. By better understanding how students grasp and deal with errors, one can help novices learn to program. Knowing how programmers debug can encourage researchers to develop more practically directed methods, enable teachers to improve their debugging curricula and allow tool developers to adapt the debugger to the actual needs of users. It is practically suggested to use the prepared tools in the software engineering course
The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease

In the health sector, data mining can be used as a recommendation to predict a disease from the&nbsp; collection of patient medical record data or health data. One of the techniques can be applied is&nbsp; classification with the C4.5 algorithm. The increasing accuracy can be conducted in data&nbsp; transformation using zscore normalization method. In addition, the implementation of the&nbsp; ensemble method can also improve accuracy of C4.5 algorithm, namely boosting or adaboost.&nbsp; The purpose of this study was determinin the implementation of zscore normalization in the&nbsp; pre-processing and adaboost stages of the C4.5 algorithm and determing the accuracy of the&nbsp; C4.5 algorithm after applying zscore and adaboost normalization in diagnosing chronic kidney&nbsp; disease. In this study, the mining process used k-fold cross validation with the default value k =&nbsp; 10. The implementation of the C4.5 algorithm obtained an accuracy of 96% while the accuracy&nbsp; of the C4.5 algorithm with the zscore normalization method obtained an accuracy of 96.75%.&nbsp; The highest accuracy was obtained from the addition of the boosting method to the C4.5&nbsp; algorithm and zscore normalization obtained the accuracy of 97.25%. The increasing accuracy&nbsp; was obtained of 1.25% which compared to the accuracy C4.5 algorithm.
The Impact of Graduate Student's Perceptions towards Usage of Cloud Computing in Higher Education Sectors

Learning is a continuous process.Nowadays, students learn through digital technologies.The important factors that make education a distant dream are unavailability of educational resources and the high cost that comes with it.Cloud computing can solve both the issues.Cloud computing in education is a rapidly emerging technology.The individuals and the educational organizations are ready to adapt to new technologies.The usage of cloud computing technologies has modified the work flow of teachers, educators in higher education sectors.The adoption rate is gradually increasing due to its cost efficiency and collaborative nature of the educational cloud.The foremost objective of this paper is to determine the impact of graduate student's perceptions towards cloud technologies usage in higher education sectors and to examine its benefits to students and teachers.Powerful solutions provided by cloud applications have been proposed which will be beneficial to the higher education institutions.This empirical study analyzed and discussed the student's learning experience by using the cloud applications and services.The outcome of this study implies that the intention of respondents has endurance in using cloud computing services.