Reviewerly/exHarmony huggingface dataset:
UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised Annotations

This work describes a self-supervised data augmentation approach used to improve learning models’ performances when only a moderate amount of labeled data is available. Multiple copies of the original model are initially trained on the downstream task. Their predictions are then used to annotate a large set of unlabeled examples. Finally, multi-task training is performed on the parallel annotations of the resulting training set, and final scores are obtained by averaging annotator-specific head predictions. Neural language models are fine-tuned using this procedure in the context of the AcCompl-it shared task at EVALITA 2020, obtaining considerable improvements in prediction quality.
Urban green economic planning based on improved genetic algorithm and machine learning

There are many factors that need to be considered when planning a city’s green economy, so it is difficult to simulate the planning effect through manual models. In order to improve the effect of urban green economic planning, this paper improves the traditional algorithm and combines the principle of machine learning algorithm to build a model that can be used in urban green economic planning. Moreover, this paper considers the measurement of green economic efficiency from the perspective of input, expected output and undesired output. In addition, this paper compares and analyzes the green efficiency calculated by the SE-SBM model, including horizontal comparison analysis and vertical comparison analysis, and conducts model simulation analysis in combination with data simulation research. Finally, this paper sets the simulation area, combines the data to perform model performance analysis, summarizes the data with statistical analysis methods, and draws charts. The research results show that the model constructed in this paper has a certain effect and can be applied to the design stage of urban green planning.
Transnational power‐transaction model considering transaction costs under the support of consortium blockchain

Transnational power trading can promote the development of global energy Internet, which can enable various countries and regions to optimize the allocation of resources on a larger scale. However, transnational electricity trading still faces many challenges. First, the issues of trust, member management, and huge transaction volume hinder the development of transnational power trading. Second, the issues of considering the differences among countries and establishing a reasonable and efficient transnational power market trading mechanism are also particularly important. The emergence of a consortium-blockchain technology provides a new idea of solving these problems. The distributed-ledger technology and member-management services of a consortium blockchain can solve the problems of trust in the transnational power transaction and access the review of market subjects. Simultaneously, the chain-code technology can be used to improve the efficiency of transactions. Therefore, according to the actual needs of transnational power transactions, the applicability of the consortium-blockchain technology was analysed. Then, a mathematical model of transnational power transactions that considered the transaction costs was designed, and simulation tests were conducted on Hyperledger Fabric. The calculation example shows that the proposed transaction model plays a positive role and significance in promoting transnational power transactions and stabilizing the market.
A Study on the Strategies of Classroom Construction of English E-Teaching in Higher Vocational Colleges

In recent years, information technology has made rapid progress and its application has become more and more extensive. Under the background of information technology, it is very important to study the classroom teaching of higher vocational English. This paper mainly summarizes the positive role of informationization in higher vocational English classroom teaching, and then focuses on the construction of vocational English e-teaching classroom strategies, hoping to provide reference for the future reform of vocational English.
A practical mode system for recursive definitions

In call-by-value languages, some mutually-recursive value definitions can be safely evaluated to build recursive functions or cyclic data structures, but some definitions (let rec x = x + 1) contain vicious circles and their evaluation fails at runtime. We propose a new static analysis to check the absence of such runtime failures. We present a set of declarative inference rules, prove its soundness with respect to the reference source-level semantics of Nordlander, Carlsson, and Gill (2008), and show that it can be (right-to-left) directed into an algorithmic check in a surprisingly simple way. Our implementation of this new check replaced the existing check used by the OCaml programming language, a fragile syntactic/grammatical criterion which let several subtle bugs slip through as the language kept evolving. We document some issues that arise when advanced features of a real-world functional language (exceptions in first-class modules, GADTs, etc.) interact with safety checking for recursive definitions.
Path planning Methods for Mobile Robots: A systematic and Bibliometric Review

Robots are currently replacing humans in different tasks in various sectors. Among the vital features desirable in autonomous robots is the capability of navigating safely through a given environment. Robot navigation is a process designed with the ability of avoiding any hitches or obstacles while aiming at a specific predefined position. Many studies have been proposed to find solutions to robot path-planning problems. This paper presents a survey of the heuristic and classical path-planning approaches. Focal strengths, together with the weaknesses of these approaches, were also identified to provide deep insight for future studies. As several literature studies have recommended, classical methods might not be effective in real-time applications as a result of their failure to confront the unpredictable nature of the real-world. They require a considerable amount of computation and space, while heuristic-based methods can overcome real-world problems with some modifications. To summarize the research progress and also suggest future directions of path-planning research, this study performs a bibliometric analysis of the relevant publications published from 2000 to 2020. The results show that 5385 articles were published in 1128 journals, hence indicating publication diversity. There is a steady rise in the yearly publication output, reflecting an increase in global research interest in the topic. In general, this research provides useful insight into path-planning research so that researchers in this area can better recognize the relevant research study topics and search for the appropriate research partners.
Efektivitas Pembelajaran Cooperative Script, Artikulasi dan Cooperative Integrated Reading and Composition terhadap Pemahaman Konsep Siswa Sekolah Dasar

This study aims to determine the effectiveness of Cooperative Script (CS), Artikulasi, and Cooperative Integrated Reading and Composition (CIRC) learning model on understanding the concepts of elementary school students. This study used a quasi-experimental research method with Counterbalance design. This study used sample of 5th grade students in three elementary schools with total 75 students. The schools are SDN Pakembangan, SDN Kutakembaran 1, and SDN Kutakembaran 2. The instruments used included a concept understanding test, observation sheets, and documentation. Data analysis used was statistical testing with independent t-test which was previously tested for normality, homogeneity, and two mean difference at a significant level of 0.05. The results showed that the understanding of the concept of students who took learning with CS was better than students who took Artikulasi and CIRC. It was reviewed based on the student average score in the study I was 75.0, study II was 75.2 and 76.8 in the study III. The results of the two-difference test mean in study I, II, and III of the CS model obtained a significant value (2-tailed) 0.000 0.05. It means that there is a significant difference between the use of CS, Artikulasi, and CIRC models. Most of the students showed a positive attitude towards learning with CS model. Based on the findings, social studies learning with CS model can be used as an alternative learning model to improve the concept understanding of elementary students .
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too

Intermediate-task training—fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task—often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tasks and moderate improvements on question-answering target tasks. MNLI, SQuAD and HellaSwag achieve the best overall results as intermediate tasks, while multi-task intermediate offers small additional improvements. Using our best intermediate-task models for each target task, we obtain a 5.4 point improvement over XLM-R Large on the XTREME benchmark, setting the state of the art as of June 2020. We also investigate continuing multilingual MLM during intermediate-task training and using machine-translated intermediate-task data, but neither consistently outperforms simply performing English intermediate-task training.
Verification of the IBOS Browser Security Properties in Reachability Logic

This paper presents a rewriting logic specification of the Illinois Browser Operating System (IBOS) and defines several security properties, including the same-origin policy (SOP) in reachability logic. It shows how these properties can be deductively verified using our constructor-based reachability logic theorem prover. This paper also highlights the reasoning techniques used in the proof and three modularity principles that have been crucial to scale up and complete the verification effort.
Commonsense Knowledge in Wikidata.

Wikidata and Wikipedia have been proven useful for reason-ing in natural language applications, like question answering or entitylinking. Yet, no existing work has studied the potential of Wikidata for commonsense reasoning. This paper investigates whether Wikidata con-tains commonsense knowledge which is complementary to existing commonsense sources. Starting from a definition of common sense, we devise three guiding principles, and apply them to generate a commonsense subgraph of Wikidata (Wikidata-CS). Within our approach, we map the relations of Wikidata to ConceptNet, which we also leverage to integrate Wikidata-CS into an existing consolidated commonsense graph. Our experiments reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it is an indicator that Wikidata contains relevant commonsense knowledge, which can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS and other commonsense sources is low, motivating the value of knowledge integration; 3) Wikidata-CS has been evolving over time at a slightly slower rate compared to the overall Wikidata, indicating a possible lack of focus on commonsense knowledge. Based on these findings, we propose three recommended actions to improve the coverage and quality of Wikidata-CS further.
Reasoning about Exceptions in Ontologies: from the Lexicographic Closure to the Skeptical Closure

Reasoning about exceptions in ontologies is nowadays one of the challenges the description logics community is facing. The paper describes a preferential approach for dealing with exceptions in Description Logics, based on the rational closure. The rational closure has the merit of providing a simple and efficient approach for reasoning with exceptions, but it does not allow independent handling of the inheritance of different defeasible properties of concepts. In this work we outline a possible solution to this problem by introducing a weaker variant of the lexicographical closure, that we call skeptical closure, which requires to construct a single base. We develop a bi-preference semantics for defining a characterization of the skeptical closure.
Human Activity Recognition by Utilizing Local Ternary Pattern and Histogram of Oriented Gradients

Due to increasing demand of automated monitoring systems, human activity recognition (HAR) has become one of the most active research areas. The goal of the proposed work is to develop a view invariant algorithm to recognize human activities from realistic unconstrained videos. In this study, we proposed a robust and discriminative feature vector for human activity recognition based on integration of local ternary pattern (LTP) and histogram of oriented gradients (HOG). The LTP descriptor is used to grab texture features from the sequence of frames followed by computation of HOG to represent the extracted texture feature by local oriented histogram. Since LTP is an efficient feature descriptor to grab structural arrangement of pixels in frame sequences, HOG is useful for measuring shape of an object locally. Therefore, by utilizing the properties of these two descriptors, an efficient feature descriptor has been constructed for multi-view and realistic environment. The classifier used for the classification of activities is multiclass support vector machine (SVM). The proposed work has been tested on three publically available human activity recognition datasets that are—UT interaction, CASIA, and UCF101. Performance of the proposed method is compared with several existing methods. The experimental results have shown the usefulness of the present work.
Exploiting GPU-based Parallelism for Quantum Computer Simulation: A Survey

As the advent of quantum computers comes closer, research on quantum algorithms is actively being conducted in various fields. Quantum computer simulation is mainly employed for the study of quantum algorithms due to the imperfections of quantum computers. Quantum computer simulation using classical computers has encountered significant challenges in emulating quantum algorithms, where computational time and memory usage increase exponentially due to superposition and entanglement. To resolve these problems, there have been many studies to exploit GPU-based parallelism in quantum computer simulation. In this review paper, we present various studies on implementing quantum computer simulations using single or multiple GPUs. We also analyze the performance of these studies in terms of speedup and summarize the simulation methods.
Systematic Generalization on gSCAN with Language Conditioned Embedding.

Systematic Generalization refers to a learning algorithm’s ability to extrapolate learned behavior to unseen situations that are distinct but semantically similar to its training data. As shown in recent work, state-of-the-art deep learning models fail dramatically even on tasks for which they are designed when the test set is systematically different from the training data. We hypothesize that explicitly modeling the relations between objects in their contexts while learning their representations will help achieve systematic generalization. Therefore, we propose a novel method that learns objects’ contextualized embeddings with dynamic message passing conditioned on the input natural language and end-to-end trainable with other downstream deep learning modules. To our knowledge, this model is the first one that significantly outperforms the provided baseline and reaches state-of-the-art performance on grounded SCAN (gSCAN), a grounded natural language navigation dataset designed to require systematic generalization in its test splits.
Transfinite step-indexing for termination

Step-indexed logical relations are an extremely useful technique for building operational-semantics-based models and program logics for realistic, richly-typed programming languages. They have proven to be indispensable for modeling features like higher-order state , which many languages support but which were difficult to accommodate using traditional denotational models. However, the conventional wisdom is that, because they only support reasoning about finite traces of computation, (unary) step-indexed models are only good for proving safety properties like “well-typed programs don’t go wrong”. There has consequently been very little work on using step-indexing to establish liveness properties, in particular termination. In this paper, we show that step-indexing can in fact be used to prove termination of well-typed programs—even in the presence of dynamically-allocated, shared, mutable, higher-order state—so long as one’s type system enforces disciplined use of such state. Specifically, we consider a language with asynchronous channels, inspired by promises in JavaScript, in which higher-order state is used to implement communication, and linearity is used to ensure termination. The key to our approach is to generalize from natural number step-indexing to transfinite step-indexing , which enables us to compute termination bounds for program expressions in a compositional way. Although transfinite step-indexing has been proposed previously, we are the first to apply this technique to reasoning about termination in the presence of higher-order state.
Deimos: A Model-Based NoSQL Data Generation Language

Synthetic data generation is of great interest when testing applications using databases. Some research and tools have been developed for relational systems. However there has been little attention to this problem for NoSQL systems. This work introduces Deimos, a prototype of a model-based language developed to generate synthetic data from NoSQL schemas represented as models conforming the NoSQLSchema metamodel. Requirements for the language–that become its design forces–are stated. The language is described, the generation process is analyzed, and future lines of work are outlined.
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling

Understanding neural models is a major topic of interest in the deep learning community. In this paper, we propose to interpret a general neural model comparatively. Specifically, we study the sequence-to-sequence (Seq2Seq) model in the contexts of two mainstream NLP tasks–machine translation and dialogue response generation–as they both use the seq2seq model. We investigate how the two tasks are different and how their task difference results in major differences in the behaviors of the resulting translation and dialogue generation systems. This study allows us to make several interesting observations and gain valuable insights, which can be used to help develop better translation and dialogue generation models. To our knowledge, no such comparative study has been done so far.
Eye tracking analysis of airport flight information display system (FIDS) to improve the information search efficiency

Abstract Air transportation is one mode of transport with a large economic contribution to Indonesia (38,12%). The airport must ensure to provide the best service, especially the efficiency of information given. Flight Information Display System (FIDS) is one substantial information system in the airport and aims to increase communication efficiency about the flight information. Based on the preliminary research, the airport’s FIDS showed different visual design one to another. Therefore, this research aims to analyze it to improve information search efficiency. The research used full factorial design as the experimental design method, eye tracker as the data gathering, and mixed-design ANOVA with post hoc test as the data analysis. We involved 3 factors that have a strong correlation with FIDS searching time efficiency: color combination of background and text, row arrangement, and column arrangement. As the measure, time to first fixation and total fixation used to indicate the information search efficiency. Data analysis generated 2 variables that improved information search efficiency on both data, except the row arrangement (only on Total Fixation data). The intended variable levels are dark color background and light color text, row arrangement based on the alphabetic order of the “ Flight ” column, and column arrangement “ Flight-Time-Destination ”.
Violent scenes detection based on connected component analysis

Violent action represents a threat to public security, thus intelligent violence detection became one of the important and challenging topics in video surveillance scenarios for this reason there is a growing appeal of video-surveillance systems. Hence, it’s mandatory for the detection of violent or abnormal activities to avert any casualties which could cause any damages. Distinctly, in this paper, it is possible to create a network to learn spatial-temporal information on all subjects of violence rather than going through each concept separately. In order to construct a new concept for violence detection system, we rely on a strategy of a dynamic frame skipping to reduce the complexity of calculation. However, following the regions of interest in the frame, the overall complexity of the calculation is decreased. Withal, the History of Binary Motion Image for n successive images is used for features extraction to facilitate to model the human behaviors. Then, the biggest regions of interest are extracted in order to find the maximum component represented violence action. Finally, deep neural networks involve three stacked Autoencoders and a Softmax are adopted as an exterior layer for classification.
O contexto da formação docente para o atendimento educacional especializado

This study aimed to analyze the training process for acting in Specialized Educational Care (SEC) through a systematic review of the literature, in order to identify the problems that still permeate this field of action, and to reflect on the inclusive practices and policies that govern and direct SEC in the Multifunction Resource Rooms. The research results showed that the teacher education for the SEC, is not enough to fulfill all the demands attributed to the function. In addition, it was found that the training for the SEC is much more in service than in previous training, which does not prepare the professional to act in face of the reality found in schools. In view of this, it was concluded that training for SEC, initial or continuous, must accompany the advances in terms of inclusive policies, as well as in relation to the practices required of the professional who works in schools.