Reviewerly/exHarmony huggingface dataset:
Impacto da qualidade de textos de partida criados por utilizadores e agentes e a propagação de erros em sistemas de Tradução Automática

This paper proposes a typology concerning errors and linguistic structures found in the source text that have an impact on Machine Translation (MT). The main objectives of this project were firstly, to make a comparison between error typologies and analyze them according to their suitability; analyze annotated data and build a data-driven typology while adapting the previous existing typologies; make a distinction between the errors produced by users and agents in the online Customer Support domain; test the proposed typology with three case studies; methodize patterns in the errors found and verify their impact in MT systems; finally, create a typology ready for production for its particular field. At first, it was made a comparison between different typologies, whether they consider a bilingual or monolingual level (e.g. Unbabel Error Typology, MQM Typology (Lommel et al., 2014b) and SCATE MT Error Taxonomy (Tezcan et al., 2017). This comparison allowed us to verify the differences and similarities between them and, also, which issue types have been previously used. In order to build a data-driven typology, both sides of Customer Support were analyzed — user and agent — as they present different writing structures and are influenced by different factors. The results of that analysis were assessed through the annotation process with a bilingual error typology and were calculated with one of the most highly used manual evaluation metrics in translation quality evaluation — Multidimensional Quality Metrics (MQM), proposed in the QTLaunchPad project (2014), funded by the European Union. Through this analysis, it was then possible to build a data-driven typology — Source Typology. In order to aid future annotators of this typology, we provided guidelines concerning the annotation process and elaborate on the new additions of the typology. In the interest of confirming the reliability of this typology, three case studies were conducted in an internal pilot, with a total of 26,855 words, 2802 errors and 239 linguistic structures (represented in the ‘Neutral’ severity — associated with conversational markers, segmentation, emoticons, etc., characteristics of oral speech) annotated, with different purposes and taking into account several language pairs. In these studies, we verified the effectiveness of the new additions, as well as the transfer of source text errors to the target text. Besides that, it was also analyzed whether the linguistic structures annotated with the ‘Neutral’ severity had in fact any impact on the MT systems. This testing allowed us to confirm the effectiveness and reliability of the Source Typology, including what needs improvement.
Sobre a aquisição da Fonologia do Português Europeu como L1: caminhos por trilhar

This text presents a set of reflections on the state of the art regarding phonological acquisition in European Portuguese as L1. Taking the existing research accomplishments as its starting point, the discussion presented here will focus on what still needs to be done and known in different domains: regarding the construction, validation and availability of corpora and tools for phonological assessment; in the study of typical and atypical development; in the relationships between research and clinical and pedagogical intervention. Among the paths to be followed, emphasis will be placed on the need to instill multidisciplinary and multimodal approaches in phonological acquisition research, combining data from production, perception and metaphonological processing
Implementasi Gerakan Literasi Sekolah pada Sekolah Menengah Pertama di Kecamatan Binjai Utara, Kota Binjai, Sumatera Utara

The School Literacy Movement (GLS) which is implemented in Indonesia in accordance with the direction of the Regulation of the Minister of Education and Culture (Permendikbud) Number 23 of 2015, is not only the ability to read and write, but includes thinking and behavior skills, namely as the outcome of a comprehensive effort involving all school members, including teachers, students, parents/guardians, and the community, which are components of the educational ecosystem that relies on the role and completeness of school library facilities and infrastructure. The purpose of this study, to describe the application of GLS in Junior High Schools in North Binjai District, Binjai City, North Sumatra. Data collection was carried out following the paradigm of qualitative research, namely observation, in-depth interviews, which were complemented by a study of documentation. The data is sourced from all stakeholders including the Principal, Teachers, School Committees, Students, and Librarians. The results of the study illustrate that the implementation of GLS in junior high schools in North Binjai District still does not meet the National Education Standards (SNP), both public and private schools, so that there needs to be firmness in local government policies and stakeholder commitment to implementing GLS which results in outstanding students, have good behavior and character, have insight into knowledge and excellent learning achievements.
The Next Frontier in Intelligent Augmentation: Human-Machine Collaboration in Strategic Marketing Decision-Making

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The Applications of Graph Algorithms to Modeling of Integrated Urban Water Management System

The application of methods using graphs to model a variety of engineering issues has been known for several decades, but the application of graph algorithms to model the urban water management issues is a completely new approach.The article reviews the scientific literature on integrated urban water management systems in terms of the use of graph theory algorithms in this topic.Such a review has not been done before and constitutes a completely novel study.Some of the algorithms presented are directly derived from graph theory, while others were developed from other sciences, including environmental engineering or genetics, to solve specific engineering problems.The paper presents a general scheme and a brief description of the most important components of an integrated urban water management system.The necessary concepts of graphs were defined, the origin and the principle of graph algorithms used in modeling water management issues (Loop-By-Loop Cutting Algorithm, Hanging Gardens Algorithm, Tree Growth Algorithm, Dijkstra's Algorithm, Genetic Algorithm, and Bayesian Networks Algorithm) were described.Their use in modeling the issues in stormwater, sanitary sewage and water distribution system was described.A complete list of scientific literature in this field was provided.
Utilizing Smartphone-Based Pinterest Applications in Developing EFL Students Speaking Skills in Indonesia

This study raises the issue of how to improve speaking skills using the smartphone-based Pinterest application for second-semester students. The purpose of this study was to determine the improvement of speaking skills using the smartphone-based Pinterest application for second-semester students. The type of research used in this research is Classroom Action Research (CAR) which is carried out in 2 cycles. The research subjects are second-semester students. The research instruments are an observation sheet and a performance test sheet. Data collection techniques are used in the form of tests and non-tests. The data analysis technique used is the descriptive statistical technique. The results of the study on second-semester students, experienced a good increase from the learning outcomes of the first cycle to the second cycle, with the standard value of student learning mastery of 85 classical standards. The average value in the first cycle is 48.00 with the level of completeness of learning outcomes in the first cycle of 10% and 90% incomplete. While the learning outcomes in the second cycle with an average value of 85.46 with the completeness level of learning outcomes in the second cycle of 96%, this figure indicates that the average value of the first cycle and the second cycle has increased, so that the smartphone-based Pinterest application in improving speaking skills can improve student learning outcomes.
Statistics of partial permutations via Catalan matrices

A generalized Catalan matrix (an,k)n,k≥0 is generated by two seed sequences s=(s0,s1,…) and t=(t1,t2,…) together with a recurrence relation. By taking sℓ=2ℓ+1 and tℓ=ℓ2 we can interpret an,k as the number of partial permutations, which are n×n 0,1-matrices of k zero rows with at most one 1 in each row or column. In this paper we prove that most of fundamental statistics and some set-valued statistics on permutations can also be defined on partial permutations and be encoded in the seed sequences. Results on two interesting permutation families, namely the connected permutations and cycle-up-down permutations, are also given.
Sample Complexity of Robust Reinforcement Learning with a Generative Model

The Robust Markov Decision Process (RMDP) framework focuses on designing control policies that are robust against the parameter uncertainties due to the mismatches between the simulator model and real-world settings. An RMDP problem is typically formulated as a max-min problem, where the objective is to find the policy that maximizes the value function for the worst possible model that lies in an uncertainty set around a nominal model. The standard robust dynamic programming approach requires the knowledge of the nominal model for computing the optimal robust policy. In this work, we propose a model-based reinforcement learning (RL) algorithm for learning an $\epsilon$-optimal robust policy when the nominal model is unknown. We consider three different forms of uncertainty sets, characterized by the total variation distance, chi-square divergence, and KL divergence. For each of these uncertainty sets, we give a precise characterization of the sample complexity of our proposed algorithm. In addition to the sample complexity results, we also present a formal analytical argument on the benefit of using robust policies. Finally, we demonstrate the performance of our algorithm on two benchmark problems.
Quantum Receiver Enhanced by Adaptive Learning

Quantum receivers aim to effectively navigate the vast quantum-state space to endow quantum information processing capabilities unmatched by classical receivers. To date, only a handful of quantum receivers have been constructed to tackle the problem of discriminating coherent states. Quantum receivers designed by analytical approaches, however, are incapable of effectively adapting to diverse environment conditions, resulting in their quickly diminishing performance as the operational complexities increase. Here, we present a general architecture, dubbed the quantum receiver enhanced by adaptive learning (QREAL), to adapt quantum receiver structures to diverse operational conditions. QREAL is experimentally implemented in a hardware platform with record-high efficiency. Combining the QREAL architecture and the experimental advances, the error rate is reduced up to 40% over the standard quantum limit in two coherent-state encoding schemes.
Learning Concept Lengths Accelerates Concept Learning in ALC

Concept learning approaches based on refinement operators explore partially ordered solution spaces to compute concepts, which are used as binary classification models for individuals. However, the number of concepts explored by these approaches can grow to the millions for complex learning problems. This often leads to impractical runtimes. We propose to alleviate this problem by predicting the length of target concepts before the exploration of the solution space. By these means, we can prune the search space during concept learning. To achieve this goal, we compare four neural architectures and evaluate them on four benchmarks. Our evaluation results suggest that recurrent neural network architectures perform best at concept length prediction with a macro F-measure ranging from 38% to 92%. We then extend the CELOE algorithm, which learns ALC concepts, with our concept length predictor. Our extension yields the algorithm CLIP. In our experiments, CLIP is at least 7.5 times faster than other state-of-the-art concept learning algorithms for ALC -- including CELOE -- and achieves significant improvements in the F-measure of the concepts learned on 3 out of 4 datasets. For reproducibility, we provide our implementation in the public GitHub repository at https://github.com/dice-group/LearnALCLengths.
Learning to Transfer Prompts for Text Generation

Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in a data-scarce situation. Therefore, it is non-trivial to develop a general and lightweight model that can adapt to various text generation tasks based on PLMs. To fulfill this purpose, the recent prompt-based learning offers a potential solution. In this paper, we improve this technique and propose a novel prompt-based method (PTG) for text generation in a transferable setting. First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks. To consider both task- and instance-level information, we design an adaptive attention mechanism to derive the target prompts. For each data instance, PTG learns a specific target prompt by attending to highly relevant source prompts. In extensive experiments, PTG yields competitive or better results than fine-tuning methods. We release our source prompts as an open resource, where users can add or reuse them to improve new text generation tasks for future research. Code and data can be available at https://github.com/RUCAIBox/Transfer-Prompts-for-Text-Generation.
Robust Design of Federated Learning for Edge-Intelligent Networks

Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users' privacy, model sharing-based federated learning (FL) that migrates model parameters but not private data from edge devices to a central cloud is particularly attractive for edge-intelligent networks. Due to multiple rounds of iterative updating of high-dimensional model parameters between base station (BS) and edge devices, the communication reliability is a critical issue of FL for edge-intelligent networks. We reveal the impacts of the errors generated during model broadcast and model aggregation via wireless channels caused by channel fading, interference and noise on the accuracy of FL, especially when there exists channel uncertainty. To alleviate the impacts, we propose a robust FL algorithm for edge-intelligent networks with channel uncertainty, which is formulated as a worst-case optimization problem with joint device selection and transceiver design. Finally, simulation results validate the robustness and effectiveness of the proposed algorithm.
IMPLEMENTATION OF EDTECH TOOLS IN THE EDUCATIONAL PROCESS OF THE DIGITAL ERA

The article provides an overview of popular Edtech tools utilized in the educational process of the digital era.Analysis and comparison of educational software helps to gain insights into digital game-based learning opportunities by measuring effectiveness, efficiency and user satisfaction according to different parameters.Edtech solutions give the opportunity for students to complete their educational program remotely with the use of synchronous and asynchronous communication.Learning with edutainment and gamification makes the learning process more attractive and effective.E-learning technologies are multifunctional and involve multiple forms of interaction.Modern EdTech platforms and web or mobile applications for improving language proficiency with rich imagery and visualization proved to be extremely useful.Digital game-based learning enjoys great popularity among language learners of all ages in the age of information and communication technology.This article provides an overview of the most effective Edtech tools utilized in the educational process at higher educational establishments namely Google Classroom, Duolingo, Kahoot, Teachable, Moodle, Obsidian Learning, Quizlet, Wordup, Grammarly, Сoursera.Each app is characterized by some special technological features: layout, sound and visual options for touch-based activities.The goal of the study is to demonstrate the effectiveness
Building a Random Forest predictive modeling of mineral perspectivity and Mapping gold mineral prospects in Tam Ky - Phuoc Son, Quang Nam

Tam Ky - Phuoc Son area has great potential for gold mineral with 98 gold occurrences, but the evaluation of the entire gold-mineralization potential of the area is still very limited, while this is considered as a basis for planning, exploration, and mining. The paper uses an Artificial Intelligence model which has a name Random Forest to build predictive modeling of mineral perspectivity and to map the gold mineral prospect of the study area. 12 influencing factors are selected to build the dataset for model training and mapping gold minerals prospect, including Geology, fault systems (NE-SW faults, NW-SE faults, sub meridian faults, sub-latitude faults), Bouguer geophysical anomaly, a geochemical anomaly of silver (Ag), gold ( Au), lead (Pb), zinc (Zn), copper (Cu) and distance to the geologic boundary of complexes related to gold mineralization. The data which are generated from these factors are 12 fuzzy maps. This data combines with 98 occurrences’ locations to create a dataset that is used to train a model of mineral perspectivity using the Random Forest algorithm. After training the model is evaluated by validation. The results of the Random Forest predictive modeling of mineral prospects are well trained with an accuracy of 95.99% on the training set and 83.05 on the validation set, the performance of the model is excellent on both datasets with AUC of 0.993 and 0.95, respectively. Finally, a mineral perspectivity map is built using the trained model. The study area is divided into 3 types of areas: high, medium, and low prospects. The area of high prospect is 982.8 km2, covering 71% of the gold occurrences.
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting

Self-augmentation has received increasing research interest recently to improve named entity recognition (NER) performance in low-resource scenarios. Token substitution and mixup are two feasible heterogeneous self-augmentation techniques for NER that can achieve effective performance with certain specialized efforts. Noticeably, self-augmentation may introduce potentially noisy augmented data. Prior research has mainly resorted to heuristic rule-based constraints to reduce the noise for specific self-augmentation methods individually. In this paper, we revisit these two typical self-augmentation methods for NER, and propose a unified meta-reweighting strategy for them to achieve a natural integration. Our method is easily extensible, imposing little effort on a specific self-augmentation method. Experiments on different Chinese and English NER benchmarks show that our token substitution and mixup method, as well as their integration, can achieve effective performance improvement. Based on the meta-reweighting mechanism, we can enhance the advantages of the self-augmentation techniques without much extra effort.
Computer Vision Based Smart CCTV Solution

Abstract With the assessment of the latest technology safeguarding humanity for their safety and security round the clock is an immense challenge. Deep learning algorithms are being developed to provide greater performance, accurate classification, and the extraction of useful information. Numerous deep learning architecture techniques have been put forth in recent years. Massive security camera deployments have been made, together with additional sensors, to monitor important infrastructure, including Oil &amp; Gas industries, military bases, airports, power plants, banks, etc. As human resources are expensive and have a finite skill set, manual monitoring by human operators is an ineffective solution in modern world. The goal of AI technology used in CCTV system is to automatically monitor infrastructure or the environment with little to no human involvement. These monitoring activities involve automatically locating and tracking objects (such as people or vehicles), followed by additional analysis and action. Techniques for artificial intelligence (machine learning), signal processing, and image processing are key components in the development of such intelligent systems. The most popular modality (device) for a surveillance system is a visible camera like a CCTV. It has been used for a very long time to keep an eye on people and events. To automatically evaluate data (image or video) from surveillance cameras, extensive research has been done. Numerous specialized review papers on background-foreground segmentation, objects detection and classification, tracking, and behavioral analysis have covered a large portion of these investigations. Other than visible cameras, many sensor modalities have been investigated for surveillance systems, including thermal and infrared cameras, radar, and audio sensor. This paper will present the case study of a comprehensive overview of computer vision base CCTV surveillance system and to review the current approaches to each of its processing steps.
A Study on the Physical Input Method of Future Display

With the rapidly developing technology, displays are being found in various places in our lives, and the area of use is gradually expanding with the advent of flexible displays that are thin, light, and can be deformed without breaking.In this study, the purpose of this study is to derive a specific input method for future displays, which are the generation after flexible displays, and to expand use examples for future display research.Through the literature research method, based on flexibility, the first generation Flat, second generation Curved, third generation Bendable, fourth generation Foldable and Rollable, and fifth generation Stretchable were classified into Five generations.Later, out of the nine characteristics of Flattening, Bending, Folding, Rolling, Extension, Twisting, Crumpling, Pop-up, and Clear of each display, the input method was visualized based on eight characteristics excluding Clear.This will help users understand and manipulate future displays that may be somewhat unfamiliar to them, and in subsequent studies, research will be conducted on which product line can be incorporated based on input methods.