Reviewerly/exHarmony huggingface dataset:
Interactive Information Extraction by Semantic Information Graph

Information extraction (IE) mainly focuses on three highly correlated subtasks, i.e., entity extraction, relation extraction and event extraction. Recently, there are studies using Abstract Meaning Representation (AMR) to utilize the intrinsic correlations among these three subtasks. AMR based models are capable of building the relationship of arguments. However, they are hard to deal with relations. In addition, the noises of AMR (i.e., tags unrelated to IE tasks, nodes with unconcerned conception, and edge types with complicated hierarchical structures) disturb the decoding processing of IE. As a result, the decoding processing limited by the AMR cannot be worked effectively. To overcome the shortages, we propose an Interactive Information Extraction (InterIE) model based on a novel Semantic Information Graph (SIG). SIG can guide our InterIE model to tackle the three subtasks jointly. Furthermore, the well-designed SIG without noise is capable of enriching entity and event trigger representation, and capturing the edge connection between the information types. Experimental results show that our InterIE achieves state-of-the-art performance on all IE subtasks on the benchmark dataset (i.e., ACE05-E+ and ACE05-E). More importantly, the proposed model is not sensitive to the decoding order, which goes beyond the limitations of AMR based methods.
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture

Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data.
Teaching LTLf Satisfiability Checking to Neural Networks

Linear temporal logic over finite traces (LTLf) satisfiability checking is a fundamental and hard (PSPACE-complete) problem in the artificial intelligence community. We explore teaching end-to-end neural networks to check satisfiability in polynomial time. It is a challenge to characterize the syntactic and semantic features of LTLf via neural networks. To tackle this challenge, we propose LTLfNet, a recursive neural network that captures syntactic features of LTLf by recursively combining the embeddings of sub-formulae. LTLfNet models permutation invariance and sequentiality in the semantics of LTLf through different aggregation mechanisms of sub-formulae. Experimental results demonstrate that LTLfNet achieves good performance in synthetic datasets and generalizes across large-scale datasets. They also show that LTLfNet is competitive with state-of-the-art symbolic approaches such as nuXmv and CDLSC.
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning

We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space. Deep generative models suffer from catastrophic forgetting in the same way as other neural structures. Recent generative continual learning works approach this problem and try to learn from new data without forgetting previous knowledge. However, those methods usually focus on artificial scenarios where examples share almost no similarity between subsequent portions of data - an assumption not realistic in the real-life applications of continual learning. In this work, we identify this limitation and posit the goal of generative continual learning as a knowledge accumulation task. We solve it by continuously aligning latent representations of new data that we call bands in additional latent space where examples are encoded independently of their source task. In addition, we introduce a method for controlled forgetting of past data that simplifies this process. On top of the standard continual learning benchmarks, we propose a novel challenging knowledge consolidation scenario and show that the proposed approach outperforms state-of-the-art by up to twofold across all experiments and additional real-life evaluation. To our knowledge, Multiband VAE is the first method to show forward and backward knowledge transfer in generative continual learning.
Tessellation-Filtering ReLU Neural Networks

We identify tessellation-filtering ReLU neural networks that, when composed with another ReLU network, keep its non-redundant tessellation unchanged or reduce it.The additional network complexity modifies the shape of the decision surface without increasing the number of linear regions. We provide a mathematical understanding of the related additional expressiveness by means of a novel measure of shape complexity by counting deviations from convexity which results in a Boolean algebraic characterization of this special class. A local representation theorem gives rise to novel approaches for pruning and decision surface analysis.
Integration of school and university resources in the training of future defectologists

Subject of research: the article contains theoretical and practical material about one of the acute problems of higher defectological education – the quality of training of young specialists coming to a modern school to work with students with disabilities. Method, methodology of research: the paper considers a practice-oriented approach to the university training of students-defectologists as one of the ways to improve the quality of professional training, which involves expanding the scope of cooperation of higher education institutions with general education organizations, integrating the resources available to them for the formation of professional competencies of future defectologists. The joint work of higher education organizations and general education institutions ensures an increase in the level of training of students-defectologists according to modern practice requirements, and the school gives the opportunity in the future to get a young specialist, motivated, involved in solving the problems of the modern education system. The novelty of the research: the experience of interaction between a higher education institution and an educational organization carrying out educational activities according to programs adapted for students with disabilities in the field of training future defectologists is analyzed and summarized. The potential and forms of such interaction, its influence on the formation of professional competencies of special pedagogical personnel are revealed. Conclusions: the constructive interaction between the school and the higher educational institution, the joint solution of tasks for the training of future defectologists within the framework of a practice-oriented approach should be carried out in different directions, such as: organization of different types of practice; involvement in research activities of both students and school staff; analysis and generalization of scientific, practical and scientific-methodological experience of educational organizations.
A Study on Approximate Matching for Similarity Search: Techniques, Limitations and Improvements for Digital Forensic Investigations

With the rapid increase in data storage capacity, the use of automated procedures to handle the massive volume of data available nowadays is required by digital forensic practitioners. The Known File Filtering is one of the techniques employed to reduce/separate data (based on their hash values) for analysis, using a list of interest objects. However, due to the limitation of hashes in this scenario (inability to detect similar objects), new methods have been designed. These functions, called Approximate Matching (AM), are possible candidates to perform such a process because they can identify similarity in a very efficient way by creating and comparing compact representations of objects. In this work, we show how to perform the similarity search task (using AM) more efficiently using the Similarity Digest Search Strategies and perform a detailed analysis. Given the limitations found, we also propose a new strategy. Furthermore, we address some limitations of AM tools regarding the similarity detection, where many matches pointed out as similar, were indeed false positives. Another improvement made was in the comparison function of one of the most known AM tools after a theoretical assessment of it. We identified and mitigated some limitations, proposing a new and more precise similarity measurement. New applications of AM are also presented and analyzed: One for fast file identification (using sampling) and another for efficient fingerprint identification. This text is a summary of a Doctor of Science thesis presented and approved in Feb./2020 at the School of Electrical and Computer Engineering, University of Campinas, S˜ao Paulo, Brazil, and submitted to the Thesis and Dissertations Competition of SBSeg 2020.
Pengembangan Bahan Ajar E-Book Berbasis Kontekstual Pada Mata Pelajaran Praktikum Akuntansi Lembaga Kelas XI SMK

The increasingly rapid and modern development of science and technology brings a new paradigm in human life in all aspects. The educional world does not desert from the use of technology in the learning process. Along with curriculum charges occurs in Indonesia, which are in line with the development of learning tools, namely one of the teaching materials. The aim of this development research is to make the available e-books more perfect by developing electronic teaching materials based on a contextual approach for class XI SMK second semester accounting practicum subjects/government agencies. The 4D model was chosen by the researchers because this model was systematically arranged to solve problems in learning and adapted to the needs of students. The researcher only carried out the stages of determining, designing, and developing because the researcher focused on producing contextual-based learning materials, testing the feasibility of the book to be used in the learning process. To be carried out, a study and trial was carried out by three experts from various majors and a trial was carried out on 20 students. Based on these results, the criteria were "very feasible" to be used and students were able to understand the material presented.
LEXICAL FEATURES OF ENGLISH ADVERTISEMENT TAGLINES DURING PRIME TIME

Advertisement language employs certain characteristics which make its usage unique so that it is interesting to explore. There are 2 research problems addressed in this study. The first is to classify and analyze lexical features which are contained in English advertisement taglines during prime time moment. The second is to figure out what triggers the dominant lexical features used in English advertisement taglines during prime time. The data were taken from English advertisement taglines during the period of prime time in any local television stations in Indonesia. This study used document analysis method and was categorized as a qualitative research. Six lexical features were proposed to classify and analyze the data, namely colloquial words, coinages, loanwords, use of verbs, use of adjectives, and frequent use of compounds. The results of the study were there are six lexical features used in English advertisement taglines during prime time and the most three dominant features are colloquial words with 40%, use of verbs with 28.57%, and use of adjectives with 18.57%. There is a similarity between three of them that all of them employ monosyllabic words so it makes easier to catch by the audiences.
Blockchain technology and its impact on audit activities

Purpose- Within the scope of this study, it is to conduct a comprehensive literature review on the conceptualisation, functioning, historical process, types, basic features, areas of use of blockchain technology and finally the relationship of blockchain technology in terms of an audit. Methodology- A comprehensive literature review was conducted on the concept relationship of blockchain technology in terms of auditing. Findings- The biggest feature of blockchain technology is that it has a decentralized verification system. In this respect, it is shown as one of the most effective areas where digital transformation is experienced. The usage areas of blockchain technology can be summarized as shown: Finance, public service, health, supply chain, education and auditing. The main impact of blockchain technology on the audit profession is to reduce the cost of monitoring and control as blockchain technology becomes more common in organizations thanks to the reliability, transparency and timeliness of the data used in auditing. Blockchain creates a more robust audit trail by using multiple sibling databases instead of a single and central database. The most important part that distinguishes blockchain technology from other technologies is that this technology is far from a centralized structure. Conclusion- With the blockchain technology, which emerged to question the need for intermediary institutions that provide trust and to show that there is no need for trust in intermediary institutions, it is expected that many sectors from the banking and finance sector, logistics and supply chain to the health sector will be affected, especially cost and time savings. With the increasing number of blockchain technology, the existing risks both continue and new technology brings new risks. This technology inevitably affects audit activities as it does all sectors. Businesses should identify these risks and take the necessary precautions. Internal audit departments should develop themselves on blockchain, and businesses should allocate an additional budget for those working in this department and ensure that they receive training on this subject. Keywords: Blockchain, audit, database security, distributed ledger technology, digital transformation. JEL Codes: G10, M10, O10
Early Evidence of the Pareto Principle in Grammatical Distribution: Causative Situations in Chinese Conversational Discourse

This study is an initial report on Pareto distribution (the 80/20 rule) of grammatical constructions; namely, about 20% of the types of grammatical constructions for causative situations account for about 80% of the uses in conversation. I use a data-driven approach to investigate the grammatical constructions that Chinese L1 speakers choose in spontaneous talk show conversations to describe causative situations. I identify two specific Pareto distributional patterns. 1) The distribution of all 22 constructions for causative situations constitutes a Pareto ABC diagram with the A-class (ba-; unmarked passive; rang-; bei-; resultative; gei-) containing 27.3% of the types but accounting for 88.8% of all the 1,497 uses. 2) Most uses of a grammatical construction come from a small set of subtypes: The full ba-accounts for 87.9% of all ba-uses; the reduced bei-accounts for 86.8%; 37.5% of rang-subtypes account for 84.2%. These patterns can be explained by the Lens concept. I conclude that a few constructions account for most grammatical choices of L1 Chinese speakers in conversation. Understanding these grammatical distributions in natural discourse can improve the efficiency and efficacy of language teaching and Natural Language Processing (NLP).
Beyond Design Freedom - Providing a Set-up for Material Modelling Within Kangaroo Physics

Kangaroo Physics, a physical simulation engine, is amongst the most used form-finding tool with nearly 500 000 downloads.Mostly resorted to by users with moderate computation skills, it provides a simplified interface for an advanced simulation tool.It is a Particle Spring System relying on the Dynamic Relaxation method and offering a wide design space.Thanks to the visual scripting interface provided by Grasshopper, the user has access to a fixed set of physical ``goals'' and unitless variables, without having to work with more complex aspects of the Kangaroo physical model.This setup induces a disconnection between the user and the physical model with its variables.The goal of this research is to introduce, within the Grasshopper environment, a tensile parameter, the Young Modulus, into the Kangaroo model.Thus, while preserving the design freedom of the plug-in, a better understanding of the physical behaviour modelled in Kangaroo is offered to neophytes, as well as better control of material properties.
A mathematical assessment of the isolation random forest method for anomaly detection in big data

We present the mathematical analysis of the Isolation Random Forest Method (IRF Method) for anomaly detection, proposed by Liu F.T., Ting K.M. and Zhou Z. H. in their seminal work as a heuristic method for anomaly detection in Big Data. We prove that the IRF space can be endowed with a probability induced by the Isolation Tree algorithm (iTree). In this setting, the convergence of the IRF method is proved, using the Law of Large Numbers. A couple of counterexamples are presented to show that the method is inconclusive and no certificate of quality can be given, when using it as a means to detect anomalies. Hence, an alternative version of the method is proposed whose mathematical foundation is fully justified. Furthermore, a criterion for choosing the number of sampled trees needed to guarantee confidence intervals of the numerical results is presented. Finally, numerical experiments are presented to compare the performance of the classic method with the proposed one.
Local ranking explanation for genetic programming evolved routing policies for uncertain capacitated Arc routing problems

The Uncertain Capacitated Arc Routing Problem (UCARP) is a well-known combinatorial optimisation problem that has many real-world applications. Genetic Programming is usually utilised to handle UCARP by evolving effective routing policies, which can respond to the uncertain environment in real-time. Previous studies mainly focus on the effectiveness of the routing policies but ignore the interpretability. In this paper, we focus on post-hoc interpretability, which explains a pre-trained complex routing policy. Unlike the existing explanation methods for classification/regression models, the behaviour of a routing policy is characterised as a ranking process rather than predicting a single output. To address this issue, this paper proposes a Local Ranking Explanation (LRE) method for GP-evolved routing policies for UCARP. Given a UCARP decision situation, LRE trains a linear model that gives the same ranks of the candidate tasks as those of the explained routing policy. The experimental results demonstrate that LRE can obtain more interpretable linear models that have highly correlated and consistent behaviours with the original routing policy in most decision situations. By analysing coefficients and attribute importance of the linear model, we managed to provide a local explanation of the original routing policy in a decision situation.
The innovation of sustainable business model in eco-edutourism: a way for creating society 5.0

As a country with a huge population of more than 270 million people, Indonesia needs to reach society 5.0 to create a society-centred digital transformation. This study aims to formulate a business model for eco-edutourism in a vilage as a suburb with great potential in terms of natural resources and human resources. This research uses a qualitative approach with a type of case study. The primary and secondary data were obtained from semi-structured interviews, village databases, and related articles. The business model is formulated using the business model canvas with the identification of each element. The results show that the potential villages with natural resources and human resources can be developed into an education-based tourism village through its potential rich culture, cow farming, and proximity to another tourism destination. The business model canvas has been formulated from these potentials, which includes nine main elements. The main activities in this business are coffee processing educational tours, milk extracting educational tours, local cultural tours, nature tourism, and outbound tours. The value proposition formulated in the business model canvas in eco-edutourism is a social value that will focus on providing qualified education for visitors. The eco-edutourism initiation is expected to be an alternative to community-based digital transformation, especially in rural areas. Achieving digital transformation in the periphery areas on an ongoing basis can be a real step towards realising society 5.0 in Indonesia.
FlowUI: Combining Directly-Interactive Design Modeling with Design Analytics

In a systems building experiment, we explored how directly manipulating non-parametric geometries can be used together with a real-time parametric performance analytics for informed design decision-making in the early phases of design.This combination gives rise to a design process where considerations that would traditionally take place in the late phases of design can become part of the early phases.The paper presents FlowUI, a prototype tool for performance-driven design that is developed in a collaboration with our industry partner as part of our design analytics research program.The tool works with and responds to changes in the design modeling environment, processes the design data and presents the results in design (data) analytics interfaces.We discuss the system's design intent and its overall architecture, followed by a set of suggestions on the comparative analysis of design solutions and design reports generation as integral parts of design exploration tasks.
AN INVESTIGATION OF STUDENTS’ PERCEPTION TOWARD LISTENING COMPREHENSION DIFFICULTIES AND TOP-DOWN LISTENING STRATEGY

The main purpose of conducting this research is to investigate students’ perception toward listening comprehension difficulties and the use of Top-down listening strategy in listening class of the ninth grade students of SMPN 13 Pontianak in the year 2018-2019. It also investigated the correlation between students’ listening difficulties and strategy usage. The method of this research is a cross-sectional survey. The data were collected by using a questionnaire, continuous Likert-scale. Then, the data collected from the questionnaire was analyzed by using descriptive statistical analysis to present the items frequency, mean, and standard deviation. The population of this research is all of the ninth grade students at SMPN 13 Pontianak. The total population is 333 students from 9 classes. While the participants are 168 selected through representative stratified random. The findings showed that accent, pronunciation, the speed of speech, lack of vocabulary, the complex grammatical structure, basic background knowledge, lack of concentration, bad quality of recording were the major difficulties encountered by the ninth grade students in SMPN 13 Pontianak. The development of listening skill to the learners must be intensified in language learning because listening is critical and important not only as a receptive skill but also as a tool needed for the development of spoken language ability.