Reviewerly/exHarmony huggingface dataset:
Trap Spoofing Algorithm for Target Using GNSS/INS-Integrated Navigation System Based on Prior Error Compensation

Black flights of unauthorized unmanned aerial vehicle (UAV) have occurred frequently in recent years, threatening public safety. UAV mainly uses GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation for positioning. Due to the weakness of GNSS signal, spoofing UAV is possible. Some scholars have conducted researches on the spoofing of targets with integrated navigation, but these studies all use radar to obtain the target’s position, speed and acceleration in real time. This method has high technical complexity and expensive cost. To solve this problem, this research proposes a trap spoofing algorithm for target using GNSS/INS-integrated navigation system based on prior error compensation. By setting a spoofing trap in a certain area of the target path, when the target moves within the trap range, it will be trapped in designated areas by spoofing signals. Finally, this paper verifies the feasibility of the trap spoofing algorithm through simulation experiments.
Ps-Crdts: Crdts in Highly Volatile Environments

The implementation of collaborative applications in highly volatile environments, such as the ones composed of mobile devices, requires low coordination mechanisms. The replication without coordination semantics of CRDTs makes them a natural solution for these execution contexts. However, the current CRDT models require each replica to know all other replicas beforehand or to discover them o-the-fly. Such solutions are not compatible with the dynamic ingress and egress of nodes in volatile environments. To cope with this limitation, we propose the PS-CRDT model that combines CRDTs with the publish/subscribe interaction model, and, with that, enable the spatial and temporal decoupling of update propagation. We implemented PS-CRDTs in Thyme , a reactive storage system for mobile edge computing. Our experimental results show that PS-CRDTs require less communication than other CRDT-based solutions in volatile environments.
THE SYSTEM FOR ASSESSMENT OF DEVELOPMENT TEAMS’ COHESION INDEX

Cohesive software development teams are the most valuable engineering units in IT. However, the search of these teams is complicated by the need to analyze development artifacts, assess the found teams, and order them via the rating. The article describes the newly proposed rating system which allows estimating teams’ quality. The rating is built upon the system of fuzzy inference due to the fact that usually the teams’ efficiency may be estimated only by experts. The article contains the reference to the previous research when the search algorithm was designed and implemented, the joint section between the previous and current study, the description of the rating system, and why the fuzzy logic is applicable, the tests of a rating system.
Students’ Problems in Learning English-Speaking Skills at Fastco English Course Blora

This research aims to find students' problems in English-speaking and their efforts to solve their problems.The research design was case study.The technique for analyzing data was qualitative.The subjects were 8 students at major class.They were 6 female students and two male students.The instruments were observation and interview.The results showed that there were 2 aspects of students' speaking problems, namely linguistics aspects and psychological aspects.The researchers found 9 data caused by linguistics aspects which related to the lack of vocabulary, grammar and pronunciation.Besides, they found 6 data caused by psychological aspects concerned with the students; they are not being confident, passive and nervous.They felt that linguistics aspects were more difficult than psychological aspects.Other results showed that students only had 10 efforts to solve their speaking problems and only 3 efforts out of 14 characteristics of successful language learners.Therefore, their efforts cannot maximally improve their speaking skills.
Privacy-Preserving User Modeling for Digital Marketing Campaigns: The Case of a Data Monetization Platform

This work proposes using a collection of Deep Learning approaches to design the privacy-preserving data monetization platform to improve the digital marketplace where users have control over their data and marketers can identify the right users for their marketing campaigns through several steps. First, representation learning on hyperbolic space is performed to learn latent embeddings of user interests across multiple data sources with hierarchical structures. Second, Generative Adversarial Networks is performed to generate synthetic user interests from the embeddings. Third, the system adopts a Federated Learning technique to ensure the user modeling is trained in a decentralized manner on the user's own devices while keeping data localized without sharing with marketers. Last, a recommender system is built upon the learned user interests to identify the right users for digital marketing campaigns. Overall, this work provides a holistic solution for privacy-preserving user modeling for digital marketing campaigns.
How the COVID-19 pandemic fueled educational innovation in higher education.

The COVID-19 pandemic was the largest disruption to education systems in the world. Universities faced vulnerabilities such as low levels of digitization, deficits in their structures, low levels of digital literacy, and lack of financial resources. The students faced problems because they had low digital skills, less internet access and inadequate computers for a good virtual education. But the COVID-19 pandemic had another side. On the one hand, it meant the possibility of stimulating innovation in education. And on the other hand, it transformed several of the contexts for the implementation of the curriculum, so that various learning and skills became more relevant. In the present investigative work, the objective is to make visible the case of the Indoamerican University of Ecuador, which has a student population of 9,000 students and about 400 professors. The different actors were open to learning about educational technological tools, which helped to deal with complexity and uncertainty. In the present investigation it is established that the training in virtual education and the elaboration of digital material, the change of new educational platforms, the generation of MOOCs (Massive Online Open Courses), the radio programs and the production of academic ebooks, constituted educational innovations that supported the educational processes. at that university.
Improving Machine Translation Formality Control with Weakly-Labelled Data Augmentation and Post Editing Strategies

This paper describes Amazon Alexa AI’s implementation for the IWSLT 2022 shared task on formality control. We focus on the unconstrained and supervised task for en→hi (Hindi) and en→ja (Japanese) pairs where very limited formality annotated data is available. We propose three simple yet effective post editing strategies namely, T-V conversion, utilizing a verb conjugator and seq2seq models in order to rewrite the translated phrases into formal or informal language. Considering nuances for formality and informality in different languages, our analysis shows that a language-specific post editing strategy achieves the best performance. To address the unique challenge of limited formality annotations, we further develop a formality classifier to perform weakly labelled data augmentation which automatically generates synthetic formality labels from large parallel corpus. Empirical results on the IWSLT formality testset have shown that proposed system achieved significant improvements in terms of formality accuracy while retaining BLEU score on-par with baseline.
Recovering Text from Endangered Languages Corrupted PDF documents

In this paper we present an approach to efficiently recover texts from corrupted documents of endangered languages.Textual resources for such languages are scarce, and sometimes the few available resources are corrupted PDF documents.Endangered languages are not supported by standard tools and present even the additional difficulties of not possessing any corpus availabel over which to train language models to assist with the recovery.The approach presented is able to fully recover born digital PDF documents with minimal effort, thereby helping the preservation effort of endangered languages, by extending the range of documents usable for corpus building.
Informatics Olympiads in Kazakhstan: Team Selection and National Olympiads in Informatics

Improving education is one of the main reasons for modern society. Therefore, subject Olympiads make it possible to identify talented schoolchildren and students to improve the quality of education. The National Olympiads of schoolchildren in informatics, which are held annually, allowing gifted students to demonstrate a high level of training in subjects and contribute to the development of mental and creative abilities. The main goals and objectives of the Olympiad in Kazakhstan are the promotion of scientific knowledge and the development of students’ interest in scientific activity, the creation of the necessary conditions for identifying gifted children, their further intellectual development, the selection and preparation of students to participate in international Olympiads, support with the choice of specialty when entering universities, increasing the prestige of education in the Republic of Kazakhstan. This article describes the current model of organizing Informatics Olympiads in Kazakhstan: steps, team selection (national and international olympiads), and evaluation. This paper also includes the comparison with Informa- evaluation evaluation. This paper also includes the comparison with Informa- . This paper also . This paper also includes the comparison with Informa- includes the comparison with Informa- includes the comparison with includes the comparison with Informa- Informa Informatics Olympiad organization procedure of Slovakia and Hungary.
Two-Stage Robust Reversible Image Watermarking Based on Deep Neural Network

AbstractThis paper proposes a two-stage lossless robust watermarking scheme based on deep neural network. In the first stage, the encoder, noise layer and decoder network are combined to design an end-to-end network framework, which is used to train the input watermark information and cover image. In this framework, the encoder outputs a robust watermarked image whose visual quality is similar to the cover image, and the decoder can extract the embedded watermark information to evaluate robustness. In the second stage, by using reversible information hiding algorithm with extended prediction error, the auxiliary information that can recover the cover image is embedded into the robust watermarked image to ensure reversibility. If the watermarked image does not undergo attack processing, the hidden watermark information can not only be extracted, but also the cover image can be completely recovered. Even if the watermarked image does undergo attack processing, the embedded watermark information can also be extracted correctly because of the strong robustness in the first stage. Experimental results demonstrate the robustness of proposed scheme in terms of JPEG compression, gaussian filtering noise, rotation and shearing operations.KeywordsReversible information hidingRobust watermarkingDeep neural networkTwo-stage embeddingNoise simulation
An Information Management System for the COVID-19 Pandemic Using Blockchain

During the 1st Semester of 2020, 25 students from the Aeronautics Institute of Technology (Instituto Tecnológico de Aeronáutica – ITA) in São José dos Campos, SP, Brazil developed the academic project “Specific Technological Solutions for Special Patients with Big Data”, in Portuguese Projeto STEPES-BD: Soluções Tecnológicas Específicas para Pacientes Especiais e Sistemas em Bancos de Dados. They have accepted the challenge of using a technological approach to help manage and combat the Sars-CoV-2 Virus Pandemic (COVID-19). At that time, the lack of shared data between public and private agencies and the need for faster information flow were considered the main information for combating the spread of the disease to guide the development of an Information Management System for the COVID-19 Pandemic, involving the essential data from Electronic Health Records (EHRs). The combination of some emerging technologies like Big Data and Blockchain, together with the Scrum Framework (SF) and the Interdisciplinary Problem-Based Learning (IPBL) enabled those students from three different academic courses to develop a computer system prototype based on the pressing needs caused by this disease. This article describes the development of the main deliverables made by those graduate students in just 17 academic weeks right from the beginning of the COVID-19 Pandemic crisis in the 1st Semester of 2020.
The correlation between students’ activity in playing English video games and student’s vocabulary mastery at SMAN 1 Bandar Lampung

The primary goals of this reasearch are to find out the correlation between students’ activity in playing English video games and students’ vocabulary mastery. This research was conducted in SMAN 1 Bandar Lampung. The subject were 36 students of XI MIPA 2 as the subject of the research. Questionnaires and vocabulary test were used to collect the data. This research was using correlation through the application of quantitative methods by collecting the data from the test. The result of questionnaires showed that there were 20 students who had high level of playing English video games, 13 students had medium and 3 students had low. Meanwhile, the result of the vocabulary test showed that there were 2 students got outstanding level of vocabulary mastery. 18 students got good, 10 students got satisfactory, and 6 students got very weak. According to the result of the analysis and statistical calculation it is found that rxy is 0.604, which means there is significant correlation between students’ activity in playing English video games and students’ vocabulary mastery.
Crowdsourcing Syntactically Diverse Paraphrases with Diversity-Aware Prompts and Workflows

Task-oriented bots (or simply bots) enable humans to perform tasks in natural language. For example, to book a restaurant or check the weather. Crowdsourcing has become a prominent approach to build datasets for training and evaluating task-oriented bots, where the crowd grows an initial seed of utterances through paraphrasing, i.e., reformulating a given seed into semantically equivalent sentences. In this context, the resulting diversity is a relevant dimension of high-quality datasets, as diverse paraphrases capture the many ways users may express an intent. Current techniques, however, are either based on the assumption that crowd-powered paraphrases are naturally diverse or focus only on lexical diversity. In this paper, we address an overlooked aspect of diversity and introduce an approach for guiding the crowdsourcing process towards paraphrases that are syntactically diverse. We introduce a workflow and novel prompts that are informed by syntax patterns to elicit paraphrases avoiding or incorporating desired syntax. Our empirical analysis indicates that our approach yields higher syntactic diversity, syntactic novelty and more uniform pattern distribution than state-of-the-art baselines, albeit incurring on higher task effort.
On the Locality of Attention in Direct Speech Translation

Transformers have achieved state-of-the-art results across multiple NLP tasks.However, the self-attention mechanism complexity scales quadratically with the sequence length, creating an obstacle for tasks involving long sequences, like in the speech domain.In this paper, we discuss the usefulness of self-attention for Direct Speech Translation.First, we analyze the layerwise token contributions in the self-attention of the encoder, unveiling local diagonal patterns.To prove that some attention weights are avoidable, we propose to substitute the standard self-attention with a local efficient one, setting the amount of context used based on the results of the analysis.With this approach, our model matches the baseline performance, and improves the efficiency by skipping the computation of those weights that standard attention discards.
An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX

Numerous graph algorithms have been developed to address a variety of problems in the industry, ranging from fraud detection to scheduling or even recommendation systems. Graph-processing frameworks are hence created to simplify the implementation of graph-based solutions. Nonetheless, the number of such frameworks has grown significantly over the past decades with varying benefits and drawbacks. Understanding the requirements and characteristics of each framework plays a vital role in the selection of a suitable solution to a given problem. In this work, we evaluate the performance and usability of 2 popular graph-processing frameworks Neo4j and Apache Spark GraphX by implementing a PageRank solution to solve a practical business problem derived from the Yelp dataset.
Modular Reinforcement Learning for Playing the Game of Tron

Tron is a simultaneous move two-player game where a wall is created along the path where two agents move and the agent that crash with the wall first is defeated. Due to the fact that the same action may result in different outcomes (non-stationarity), it is difficult to utilize the basic approach of reinforcement learning. In this paper, we present a modular reinforcement learning approach to tackling the game of Tron by decomposing the game into two phases where the first phase is non-stationary and the second phase is stationary. We evaluate the performance of our algorithm by comparing with previous algorithms including the state-of-the-art algorithm for the game of Tron (called a1k0n) in different grid sizes.
Layout Creation and Setup

Layout Template is a standard layout defined in central library that can be referenced by layout design. The information contained in layout template mainly includes Layer Stackup, material parameters, substrate shape, mounting holes, fixed position connectors, etc. For Layer Stackups or substrate shapes that are often used, we can define specific layout templates in Central Library beforehand and referenced directly when designing.
Impact of Different Levels of Information Presentation on User Experience: A Case Study in a Virtual World

User experience can be affected by the amount and intensity of information presented. Four scenarios were developed to assess the insertion of information elements (chronometer and hint system) and tested with 37 users to find out if they affected the user's sense of presence and symptoms of cybersickness. In order to instruct users and using virtual reality using the Unity 3D game engine, we created a virtual world where the user has the role of exploring the environment and looking for mushrooms, and can consult a description about it. For tests with users, the IPQp and SSQ questionnaires were applied. The results indicate that it is possible to create a virtual world with the addition of informational components without significantly disturbing the user experience.
Exploring semantic connections through a thesaurus in the earth observation domain

This paper presents the creation of a thesaurus for the Earth Observation (EO) systems domain which also includes terms representing the potential clinical effects caused by the pollutants.The goal is to provide a terminological means of support for professionals and public audiences in understanding specialized terms and their semantic connections with other representatives of this area of study (Bernier-Colborne 2012).The paper presents, in the first instance, the steps carried out in compiling the source corpus from which the specialized information about the contaminant substances has been retrieved; subsequently, the work will address the extraction of the specific terminology with the aid of pre-trained semantic software as well as the identification of a sub-methodological procedure to improve term configuration, i.e., semantic relation configuration by isolating the domain-specific connective verbal constructions (Auger 2008) and exploiting the textual contextualization to broaden the conceptual system and enhance the semantic accuracy.1
Extending a Genetic-Based Visualization: Going Beyond the Radial Layout?

We study in this work the properties of a new method called Gen-POIViz for data projection and visualization. It extends a radial visualization with a genetic-based optimization procedure so as to find the best possible projections. It uses as a basis a visualization called POIViz that uses Points of Interest (POIs) to display a large dataset. This visualization selected the POIs with a simple heuristic. In Gen-POIViz we have replaced this heuristic with a Genetic Algorithm (GA) which selects the best set of POIs so as to maximize an evaluation function based on the Kruskal stress. We continue in this chapter the study of Gen-POIViz by providing additional explanations and analysis of its properties. We study several possibilities in the use of a GA: we tested other layouts for the POIs (grid, any) as well as a different evaluation function. Finally, we consequently extend the experimental results to evaluate those possibilities. We found that alternative POI layout were not more efficient that the circle layout. As a conclusion, POIViz can deal with datasets that are often too large for other methods.
Construction of the Educational Model of Vocational College Students' Career Planning Based on Design Thinking

Design thinking is an integrated thinking method with mature concepts, methods and tools, which is widely used in the field of education. Career planning education includes five aspects: Self-analysis, Environmental Analysis, Goal Establishment, Strategy Development, Evaluation and Modification, which fits well with the five steps of Empathy, Define, Ideate, Prototype and Test of design thinking. This study introduces the design thinking method and structure into the curriculum, puts forward the teaching activity model and strategy based on design thinking, and makes an empirical study on the strategy. The study mainly adopts the experimental research method and evaluates the effect of the teaching mode by comparing the improvement of the career planning ability of learners between the experimental group and the control group. The results show that the teaching mode based on design thinking has a positive effect on promoting students' career planning ability.
A Self-supervised Strategy for the Robustness of VQA Models

In visual question answering (VQA), most existing models suffer from language biases which make models not robust. Recently, many approaches have been proposed to alleviate language biases by generating samples for the VQA task. These methods require the model to distinguish original samples from synthetic samples, to ensure that the model fully understands two modalities of both visual and linguistic information rather than just predicts answers based on language biases. However, these models are still not sensitive enough to changes of key information in questions. To make full use of the key information in questions, we design a self-supervised strategy to make the nouns of questions be focused for enhancing the robustness of VQA models. Its auxiliary training process, predicting answers for synthetic samples generated by masking the last noun in questions, alleviates the negative influence of language biases. Experiments conducted on VQA-CP v2 and VQA v2 datasets show that our method achieves better results than other VQA models.