Reviewerly/exHarmony huggingface dataset:
Aquisição de pronomes clíticos de português europeu por falantes de chinês: dados sobre a colocação

This paper presents a pilot-study of a research which aims to investigate the acquisition of the clitic pronouns in European Portuguese (EP) by Chinese learners. Based on an acceptability judgement task, conducted with 20 Chinese students and a control group of 26 native speakers of EP, we would like, on the one hand, to make a better characterization of the development of placement properties of clitic pronouns in Chinese learners’ interlanguage grammars; on the other hand, to contribute to a better understanding of the theoretical proposals on the learnability problems regarding uninterpretable features not available in the first language (L1). Our preliminary results suggest that, as shown in previous works on L1 EP and L2 EP, the conditions which determine proclisis seem to be more difficult to acquire than those for enclisis and the different contexts of proclisis seem to develop gradually, following a similar path to that previously described for L1 EP.
SPECIAL ISSUE EDITORIAL: Virtual, Augmented and Mixed: New Realities in Construction

This special edition of Information Technology in Construction is focuses on new realities in construction. The editors have curated a series of papers intended to provoke speculation on the potential future of construction in the 21st Century and beyond. This edition grew from ConVR 2018: Evolving Construction, a conference held in Auckland, New Zealand. Where researchers reported on ideas, innovations and applications for virtual and augmented reality for construction. Authors of excellent papers were invited to extend their submission for publication in this special edition. Thus, this issue presents a glimpse into the state of research focused on construction and specifically the topic of ‘virtual and augmented reality’ in its broadest sense. The Architecture, Engineering and Construction (AEC) sector is age-old, comprises a variety of stakeholders and professions and is worth trillions of dollars globally. It is also a fiercely complex and risky business, which is highly regulated, because of this, change and innovation can occur slowly. However, we are now in the 21st century, a century that has come to be defined by fast changing technology. What are the implications for the AEC industry?
Methods to simulate fermions on quantum computers with hardware limitations

This thesis is a collection of theoretical works aiming at adjusting quantum algorithms to the hardware of quantum computers. The overarching topic of these efforts is to enable digital quantum simulation, the process of approximating the ground state of an arbitrary physical system with elementary operations of a quantum computer. For fermionic systems, a class including molecules and materials, the impact of quantum computing would be undoubtedly high, and algorithms exist for their simulation. However, there is a certain gap between the requirements of those algorithms and what actual quantum devices can provide: it seems that our expectations of a fully-fledged quantum computer still exceed our capabilities to build it. To make quantum simulation feasible, we seek to adapt quantum algorithms to three different types of device limitations within this thesis.
Incremental analysis in machine learning

Supervised learning requires data to be labeled. However, labels may not always be available, or creating a labeled dataset may be costly. Even when the data is labeled, labeling is often inconsistent, incomplete and inaccurate. If the data changes over time, a model also needs to be retrained periodically. A machine learning model, therefore, needs to learn from data the wild, not just from an initial training dataset. This problem can be addressed by techniques that combine clustering and classification with user feedback. The paper describes one such technique in the form of a pattern: Incremental Analysis. The target audience includes developers who do not have much experience with using machine learning in dynamic environments. This is the first of a number of planned papers on patterns for machine learning.
Patent Protection for Artificial Intelligence in China

To stimulate the incentives for AI-related innovations, China revised its Examination Guidelines for Patents, which came into force from 2017. The updated guidelines stipulate that if a invention relating to computer program or business model includes a technical feature, it should not be excluded from being patent eligible. Four types of claims, which are method, program module, computer system and storage media, are provided to protect the invention patent application on the computer program processing flow based on the amendment. Since artificial intelligence related inventions include methods for implementing mental steps using computers and devices designed to automate mental activities, the implicit requirements for judging patent eligibility may be inconsistent with the nature of artificial intelligence technology. In order to enhance the possibility of patent entitled of artificial intelligence related inventions, patent applicants should concentrate on their construction of the claims to indicate what technical problems the invention are solving or identify the specific improvements made by the claims.
AlphaZero to Alpha Hero : A pre-study on Additional Tree Sampling within Self-Play Reinforcement Learning

In self-play reinforcement learning an agent plays games against itself and with the help of hindsight and retrospection improves its policy over time. Using this premise, AlphaZero famously manage ...
Impacts of Different Types of Scaffolding on Academic Performance, Cognitive Load and Satisfaction in Scientific Inquiry Activities Based on Augmented Reality

The emergence of augmented reality technology has injected new vitality into teaching, especially when the visualization and interactivity have increased students' interest in scientific inquiry. Although augmented reality technology has brought freshness to students, it is necessary to design suitable teaching support to promote student learning. Therefore, knowing how to design a teaching support system has become an important research problem. The researchers have designed open scaffolding and convergent scaffold based on augmented reality technology as a teaching support. Twenty-one elementary school students were randomly assigned to use either the convergent scaffolding or the open scaffolding for a learning task. Using a mixed research method, this study examined the effects of different scaffoldings on knowledge retention, knowledge transfer, learning satisfaction, and cognitive load. The results revealed that open scaffolding could improve students' knowledge retention, knowledge transfer, and reduce cognitive load.
Performance Analysis and Benchmarking of Clustering Algorithms with gene datasets

Clustering is the identification of similar data from a rough or scaled or transformed data and grouping into clusters. Cluster shows symmetry and asymmetry of data and its relations. In this paper, comparisons of three fuzzy clustering algorithms and two conventional clustering algorithms are represented. The analysis is conducted on four datasets which include three gene expression datasets. Here, clustering performance is evaluated using both internal and external validation measurements and an attempt for searching the optimum number of the cluster has taken. This analysis provides an effective way of selecting a suitable algorithm for a particular dataset among different hardcore and soft-core clustering approaches.
An Active Learning Approach

In order for software project management courses to be effective, they should be appropriately aligned to the learning outcomes. Common misalignments are caused by using an incorrect degree of real ...
METODE DRILL UNTUK MENINGKATKAN KEMAMPUAN MENULIS PERMULAAN BERDASARKAN PUEBIDI SEKOLAH DASAR

This research is motivated by the low ability to write the beginning of students and the ability to use spelling in writing. This is because there is no emphasis on language learning, especially writing skills. Therefore, this research is done to overcome the problem, by applying Drill method in learning process. The purpose of this study, is to describe the planning, implementation, and improvement of writing skills beginning of students based on PUEBI after the implementation of Drill method. Participants or subjects of this study are the third grade students of elementary school in the city of Bandung. This type of research is PTK through three cycles with qualitative and quantitative data analysis techniques. Then the design of this research using Kemmis and Taggart research design consists of four stages, namely planning, implementation, observation, and reflection. The instrument of this research consists of observation of the application of Drill method, field notes, test, and documentation. The results of the test write the beginning of the students, showing the average percentage of students' initial writing ability on the first cycle is 60%, cycle II has increased, ie to 80%, 92% cycle III. Based on the data, it can be concluded that, the application of Drill method can improve students writing skill based on PUEBI in third grade of elementary school.
Development of a model for the formation of the ecological worldview of students of secondary professional education

The article is devoted to the search for methodological, theoretical and didactic basic for a modern model of the formation of the ecological worldview of college students. The author made a substantiation of the main components of the model. A two-level concept of the formation of the ecological worldview of students of secondary professional education (SPE) and a set of pedagogical conditions for its implementation are proposed. The proposed model for the formation of the ecological worldview of students of secondary vocational education on the basis of a system-activity approach in the educational process can be pedagogically effective by including in the training and education processes such forms and methods that will put students in the position of researchers and discoverers. In the modern professional educational environment, the training of competitively capable graduates who are ready to withstand the socio-economic changes that arise in society and are able to take responsibility for decisions made becomes one of the main directions of the educational process in institutions of higher and secondary professional education.
A Noble Approach Toward Security Implementation in Cloud Virtualization Framework

Virtual machine infrastructure has been one of the most powerful technologies over the years. In the mid of its growing popularity in the market, there lies a fear in using this technology. Various survey papers discussed the security issues concerning this technology, for instance, the attack at the Virtualization level, man-in-the-middle attacks, denial of service (DoS). In our paper, we discussed various security issues which can occur when we use an application in a virtualized environment. Those issues can be related to the physical machine, in which the virtualized environment is being set up. Attackers can also gain access to the hypervisor which acts as an interface between the virtual machines and the host machine. Security threats can also come up when we login into an application in a virtual machine. Those issues can be related to the login credentials, the data which is to be transferred to the destination system and last but by no means the least, the vulnerabilities of the virtual machines, through which an attacker can gain access to the hypervisor, which is the heart of the virtualized environment. Hence, it is imperative that we do some survey on how we can implement the measures by which we can minimize these security threats and counter the same.
Shape Adaptation of the Inspection Guide Frame in Tunnels to Avoid Obstacles Detected by a Laser Range Finder

To advance the automated inspection and maintenance of the inner wall of tunnels, an advanced inspection system aimed at regulating traffic was developed. In this inspection system, a guide frame was installed along the tunnel ceiling wall that is above the protection frame built over the road and resembles a gantry crane. The inspection device was fitted with an inspection guide frame (IGF), which stabilized its movement and improved its accuracy. However, as this protection frame moves along the tunnel, the guide frame should have the capacity to avoid convex obstacles such as the duct fan, the lamp and road traffic signs within the tunnel. Therefore, the entire inspection guide frame is made of variable geometry truss (VGT), which makes it possible to suitably alter the shape of the guide frame whenever necessary and pass it through the tunnel. To enable the guide frame adapt to any shape, the inverse analysis method and mathematical interpolation method were applied. The orientation of each frame was reversely analyzed according to the shape of the obstacle measured using the laser range finder (LRF), and the frame’s actuator was controlled simultaneously. We investigated the construction of a system that can perform a series of tasks such as searching for obstacles and positioning, frame shape simulation, frame shape change, inspection of the device and movement. By applying spline interpolation, we managed to practically determine the shape of the guide frame that would avoid obstacles.
Editorial IMA IAI - Information and Inference special issue on optimal transport in data sciences

Optimal transport (OT) offers a unifying theory to model and solve many problems that involve probability distributions. Because of the ubiquity of probability distributions in data sciences, this theory has recently attracted the interest of practitioners, motivating the proposal of faster algorithms and calling for more research into its statistical underpinnings. The theory and its algorithms are currently used to handle problems that involve distributions supported on low dimensional spaces (such as shape registration problems in medical imaging or regression problems in neuroscience), but also in high dimensional spaces (such as supervised cost-sensitive classification and training of generative models in machine learning, comparison of word embeddings in language processing and cell populations in single-cell genomics). This positive feedback loop, from theory, optimization to modeling is opening new application perspectives and raises naturally the many open challenges that remain to turn OT in a scalable tool, able to cope with huge datasets, yet also able to offer meaningful results, derived from sharp theoretical analyses of its limitations. This special issue on optimal transport in data science presents a varied set of contributions aiming at covering this very active research field, ranging from theoretical advances in probability and statistics to exciting applications in imaging and learning.
THE ATTITUDE OF SELECTED EUROPEAN COUNTRIES TO MOBILE TECHNOLOGIES IN EDUCATION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 3074-3078ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0778Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Remotely Gated InGaAs Single-Photon Detector at 1550 nm

Single-photon detectors (SPDs) are important devices in the development of quantum technologies, like quantum metrology, quantum computers and quantum communication. The simplest, cheapest and most commonly used SPD employs avalanche photodiode. After the photon absorption, a photocurrent is produced and it must be quenched before to damage the device. In the present work we show a new strategy for quenching the photocurrent of avalanche photodiodes remotely. Our scheme makes easier the synchronism between sender and receiver in a quantum communication setup. Some experimental results are presented.
Contributing Features-Based Schemes for Software Defect Prediction

Automated defect prediction of large and complex software systems is a challenging task. However, by utilising correlated quality metrics, a defect prediction model can be devised to automatically predict the defects in a software system. The robustness and accuracy of a prediction model is highly dependent on the selection of contributing and non-contributing features. Hence, in this regard, the contribution of this paper is twofold, first it separates those features which are contributing towards the development of a defect in a software component from those which are non-contributing features. Secondly, a logistic regression and Ensemble Bagged Trees-based prediction model are applied on the contributing features for accurately predicting a defect in a software component. The proposed models are compared with the most recent scheme in the literature in terms of accuracy and area under the curve (AUC). It is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.
Design Pattern Evaluation on A RESTful API Wrapper: A Case Study of Software Integration with An Internet Payment Gateway using Model-Driven Architecture

A proper use of design patterns has proven to be very useful in the development of robust applications over time. In this paper, the design patterns are introduced in the early stage of the software development where model-driven architecture is used as the engineering approach. A RESTful internet payment gateway API (Application Programming Interface) wrapper is selected as the case study. At the beginning, Platform Independent Model (PIM) is created as the domain model. After that, the PIM is transformed into the Platform Specific Model (PSM). Before converting the PSM into the source code, three design patterns such as builder, observer, and factory pattern are added into the model. To evaluate the impacts of implementation, static analysis is used to examine the generated code before and after adding the design patterns. The result shows that the design decision increases cohesion, complexity, coupling, inheritance, and size metrics of the source codes.
Back-translation Approach for Code-switching Machine Translation: A Case Study.

This publication has emanated from research supported in part by a researchgrant from Science Foundation Ireland (SFI) under grant agreement numberSFI/12/RC/2289_P2, co-funded by the European Regional Development Fund,and the Enterprise Ireland (EI) Innovation Partnership Programme under grantnumber IP20180729, NURS – Neural Machine Translation for Under-ResourcedScenarios.
A Logical Approach to the Analysis of Aerospace Images

The paper proposes algorithms and software tools for the automatic interpretation and classification of objects and situations on aerospace images by structural-spatial analysis and iterative reasoning based on fuzzy logic and expert rules of inference. During iterations, the decision tree is built, the transition to local rules and additional features is carried out, and the ranges of acceptable values are adjusted. Particular attention is paid to geometric features of objects. Quantitative attributes are converted to qualitative ones for ease of perception of results and forming decision rules. The results of the experiment on the automatic identification of objects in the aerial image of an urban area are given. The system is useful for automating the process of labeling images for supervised learning and testing programs that recognize objects in aerospace images.
A Spatial-temporal Attention Module for 3D Convolution Network in Action Recognition

Action recognition is a significant but challenging task in the field of computer vision. 3D convolutional neural network is one of the mainstream methods for action recognition because it can process three-dimensional information effectively. However, at present, the performance of 3D convolutional neural networks is not particularly prominent. The main reason is that the information of the video is mainly contained in the key areas of key frames in the video, yet the 3D convolutional neural network usually cannot extract the most critical information in the video effectively. Therefore, we propose a temporal attention and a spatial attention respectively, and combine them into a module called STAM to let models focus more on the key information. We introduced the STAM module into 3D ResNet, and conducted experiments on the UCF101 and HMDB51 datasets. The results demonstrate that our proposed attention module can improve the performance of 3D convolutional neural networks effectively.
Nap: Network-Aware Data Partitions for Efficient Distributed Processing

In order to support emerging data-intensive applications, many clever frameworks have been developed over the last years to efficiently and distributedly process big data sets, such as MapReduce. However, these frameworks are often optimized for relatively homogeneous environments, and accounting, e.g., for the varying connectivity of wide-area network infrastructure, may require complex placement algorithms. In this paper, we present Nap, which allows optimizing distributed data processing frameworks such as MapReduce for heterogeneous environments. Nap allows adapting resources dynamically, without requiring complex placement or migration algorithms, or modifications to the logic of the mappers and reducers. Rather, Nap simply changes the data partition, by spawning virtual nodes (e.g., reducers) depending on the demand. To this end, Nap leverages a connection to integer partition problems and employs Young lattices to guarantee minimal completion times (i.e., the makespan). In fact, Nap comes with provable performance guarantees and also supports applications that leverage redundancy to speed up executions further. In particular, to demonstrate our framework, as a case study, we show how to execute multiway joins across wide-area networks with limited bandwidth efficiently. Our experiments, based on a proof-of-concept prototype implementation, confirm the potential of Nap to reduce completion times.
Recognition of Traffic Signs Applying Neural Network

Abstract Artificial intelligence is one of the most dynamically developing areas of science today. Although it is not yet an integral part of our lives to use artificial intelligence solutions, it can be seen in terms of development, that it will become available to everyone in the coming decades, and not be exclusive for the richest. An important part of artificial intelligence research are the so-called soft calculation methods, the most important of which are fuzzy logic, genetic algorithms and neural networks. In this article, the authors present a method of identifying certain traffic signs with the help of the neural network.
Walking Human Detection Using Stereo Camera Based on Feature Classification Algorithm of Second Re-projection Error

This paper presents a feature classification method based on vision sensor in dynamic environment. Aiming at the detected targets, a double-projection error based on orb and surf is proposed, which combines texture constraints and region constraints to achieve accurate feature classification in four different environments. For dynamic targets with different velocities, the proposed classification framework can effectively reduce the impact of large-area moving targets. The algorithm can classify static and dynamic feature objects and optimize the conversion relationship between frames only through visual sensors. The experimental results show that the proposed algorithm is superior to other algorithms in both static and dynamic environments.