Reviewerly/exHarmony huggingface dataset:
IT club educational technologies for creative individual development in digital age

This article is aimed at identifying the influence of IT clubs on the development of students’ creative abilities. As a research method, a questionnaire survey was used to identify the role of modern IT forms of artistic activity in the development of students' creative abilities. The study was conducted on the basis of Russian universities among 265 students. The article describes the development of modern IT forms of artistic activity in the digital age. It is revealed that the main motivations for students to visit IT clubs were: learning new things, continuing their self-improvement, transforming the fantastic and creative into real, in-depth study of previously acquired knowledge and their improvement, obtaining skills that will be useful in the future, gaining experience in creative activities, attending classes together with friends, new communication, useful pastime. The study determines that the goals of teachers coincide with the mission of creative IT clubs: to train creative engineers who could invent and implement new technologies that would have no analogues in the world, to teach and prepare a child for his/her future occupation.&#x0D; &#x0D; Keywords: creativity, learners, motivations for developing abilities, creative space.
DEVELOPMENT OF EFFICIENCY ASSESSMENT TOOLS FOR EDUCATION MANAGERS, AND REGIONAL EDUCATION DEVELOPMENT STRATEGY AS ITS BASIS

DEVELOPMENT OF EFFICIENCY ASSESSMENT TOOLS FOR EDUCATION MANAGERS, AND REGIONAL EDUCATION DEVELOPMENT STRATEGY AS ITS BASIS
Python Computational Web Apps for STEM Engineering Education

The article discusses STEM technologies and tools used in engineering education for scientific and technical calculations in Python, which allow to make classes visual and fun for students. An integrated environment that supports all the stages of solving computational scientific problems from their formulation, solution to their sharing is considered. Dash, Panel, Voilà and Streamlit technologies for publishing computational web applications for multivariate calculations are discussed, a comparison of these technologies for use in the educational process is made. Web applications allow computational experiments, but prevent changes to the source code, eliminating the appearance of errors. The integrated environment includes a generator of static sites for publishing smart tutorials with embedded web applications. This allows publishing student-developed applications along with learning materials with a minimum of effort. The integrated environment is convenient for both face-to-face and distant learning.
Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction

Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
Visualization of Salient Object With Saliency Maps Using Residual Neural Networks

Visual saliency techniques based on Convolutional Neural Networks (CNNs) exhibit an excessive performance for saliency fixation in a scene, but it is harder to train a network in view of their complexity.The imparting Residual Network Model (ResNet) that is more capable to optimize features for predicting salient area in the form of saliency maps within the images.To get saliency maps, an amalgamated framework is presented that contains two streams of Residual Network Model (ResNet-50).Each stream of Reset-50 that is used to enhance the low-level and high-level semantics features and build a network of 99 layers at two different image scales for generating the normal saliency attention.This model is trained with transfer learning for initialization that is pretrained on ImageNet for object detection, and with some modifications to minimize prediction error.At the end, the two streams integrate the features by fusion at low and high scale dimensions of images.This model is fine-tuned on four commonly used datasets and examines both qualitative and quantitative evaluation metrics for state-of-the-art deep saliency model outcomes.
On the differences between BERT and MT encoder spaces and how to address them in translation tasks

Various studies show that pretrained language models such as BERT cannot straightforwardly replace encoders in neural machine translation despite their enormous success in other tasks.This is even more astonishing considering the similarities between the architectures.This paper sheds some light on the embedding spaces they create, using average cosine similarity, contextuality metrics and measures for representational similarity for comparison, revealing that BERT and NMT encoder representations look significantly different from one another.In order to address this issue, we propose a supervised transformation from one into the other using explicit alignment and fine-tuning.Our results demonstrate the need for such a transformation to improve the applicability of BERT in MT.
Product Programs in the Wild: Retrofitting Program Verifiers to Check Information Flow Security

Abstract Most existing program verifiers check trace properties such as functional correctness, but do not support the verification of hyperproperties, in particular, information flow security. In principle, product programs allow one to reduce the verification of hyperproperties to trace properties and, thus, apply standard verifiers to check them; in practice, product constructions are usually defined only for simple programming languages without features like dynamic method binding or concurrency and, consequently, cannot be directly applied to verify information flow security in a full-fledged language. However, many existing verifiers encode programs from source languages into simple intermediate verification languages, which opens up the possibility of constructing a product program on the intermediate language level, reusing the existing encoding and drastically reducing the effort required to develop new verification tools for information flow security. In this paper, we explore the potential of this approach along three dimensions: (1) Soundness: We show that the combination of an encoding and a product construction that are individually sound can still be unsound, and identify a novel condition on the encoding that ensures overall soundness. (2) Concurrency: We show how sequential product programs on the intermediate language level can be used to verify information flow security of concurrent source programs. (3) Performance: We implement a product construction in Nagini, a Python verifier built upon the Viper intermediate language, and evaluate it on a number of challenging examples. We show that the resulting tool offers acceptable performance, while matching or surpassing existing tools in its combination of language feature support and expressiveness.
“I’ve Seen Things You People Wouldn’t Believe”: Hallucinating Entities in GuessWhat?!

Natural language generation systems have witnessed important progress in the last years, but they are shown to generate tokens that are unrelated to the source input.This problem affects computational models in many NLP tasks, and it is particularly unpleasant in multimodal systems.In this work, we assess the rate of object hallucination in multimodal conversational agents playing the GuessWhat?! referential game.Better visual processing has been shown to mitigate this issue in image captioning; hence, we adapt to the GuessWhat?! task the best visual processing models at disposal, and propose two new models to play the Questioner agent.We show that the new models generate few hallucinations compared to other renowned models available in the literature.Moreover, their hallucinations are less severe (affect task-accuracy less) and are more human-like.We also analyse where hallucinations tend to occur more often through the dialogue: hallucinations are less frequent in earlier turns, cause a cascade hallucination effect, and are often preceded by negative answers, which have been shown to be harder to ground.
THE USE OF BLOCKCHAIN TECHNOLOGIES TO ISSUE AND VERIFY MICRO-CREDENTIALS FOR CUSTOMISED EDUCATIONAL JOURNEYS: PRESENTATION OF A DEMONSTRATOR

Information about the paper titled "THE USE OF BLOCKCHAIN TECHNOLOGIES TO ISSUE AND VERIFY MICRO-CREDENTIALS FOR CUSTOMISED EDUCATIONAL JOURNEYS: PRESENTATION OF A DEMONSTRATOR" at IATED Digital Library
Web-Based Rice Disease Diagnosis Expert System Using Fuzzy Tsukamoto Method and K-Nearest Neighbor Algorithm

Technology today is growing rapidly from year to year, not least started to spread to the agricultural sector. With the information technology making society more easily in search of information via the internet from your smart device. The goal of this study was made to facilitate the community, especially farmers in helping to diagnose diseases and pests in rice plants. Rice plants can be attacked by a wide variety of diseases and pests with a wide variety of symptoms experienced in rice plants. To know the kind of disease on rice plants in the era of technology, it takes an expert system that can help detect the disease in rice plants. In this study, Expert System-Based Website using Tsukamoto Fuzzy method and the Algorithm of K-Nearest Neighbor whose purpose is to help people, especially farmers in diagnosing diseases and pests in rice plants by looking at the symptoms of the attack on the rice plant. Data was obtained from the Research and the Ministry of Agriculture then taken some sample data for testing done. The results of the testing data of this expert system is the result of late diagnosis in diseases of the rice with the symptoms that already exist based on the data that have been obtained with an accuracy rate of 92,88%.
A Study on Semantic Features of Antonymous Verbs Based on Lexical Knowledge System

Extraction of lexical semantic features is a key issue in lexical knowledge research. Based on the connectionism theory, this paper adopts the corpus and statistical approach to analyze the semantic features of antonymous verbs and construct their lexical knowledge systems. We take the antonymous verbs ‘‘买(mai3,buy)’’ and ‘‘卖(mai4,sell)’’ as examples in our analysis. We also construct lexical knowledge systems of collocation, semantic features of collocation and information about syntactic constructions. On this basis, this paper focuses on the effects of the types and frequencies of word collocations and the syntactic usage of the words, hoping to provide some insights for the studies on lexical semantic features of antonymous verbs.
Health status identification of catenary based on VMD and FA-ELM

Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the catenary based on firefly algorithm optimized extreme learning machine combined with variational mode decomposition is proposed in this paper. Variational mode decomposition is used to decompose the original detection curve of catenary into a series of intrinsic mode function components, and the intrinsic mode function components filtered by the correlation coefficient method after decomposing each detection curve are input into the firefly algorithm optimized extreme learning machine model to realize health status identification. Compared with some other models, the results show that the proposed model has better health status identification effect.
Pola Materi Kebahasaan, Monitoring, dan Evaluasi Pembelajaran Bahasa pada Buku Tematik Sekolah Dasar Kelas 1

This research has two aims. 1) to describe the pattern of lan-guage learning in primary school thematic text-books used in grade 1; and 2) to describe how to monitor and evaluate lan-guage learning in school thematic text-books used in the basic class in grade 1. By using the method of listening and tapping as well as note-taking techniques, we collected our data sourced from the 2013 thematic integrated curriculum book for elementary school students grade one. The data that have been obtained are then analyzed and presented formally in the narrative form. The results of this study indicate that of the 8 themes that are represented in the 8 books are able to pre-sent aspects and language competencies that are rather sys-tematic. In addition, the training and evaluation monitoring sheet in the form of "Now I Can" can make it easier for teach-ers to measure the achievement and success of each student's learning comprehensively and validly.
Face Morphing of Newborns Can Be Threatening Too : Preliminary Study on Vulnerability and Detection

Face morphing attacks are evolving as a significant threat to the Face Recognition Systems (FRS) operating in border control and passport issuance. As newborn face has very limited discriminative facial characteristics, it is challenging for both human and machines to verify the newborns based on the facial biometrics accurately. Further, the introduction of face morphing elevates the problem of baby trafficking as it can challenge both human and machine-based facial verification. In this paper, we pose a question if the morphed images of newborns can threaten FRS and present first systematic study on the vulnerability analysis of FRS towards morphed faces of newborns. To effectively benchmark threat of newborns’ facial morphing attacks, we introduce a new face morphing dataset constructed based on 42 unique newborns with 852 bona fide and 2451 morphing images. Extensive experiments are carried out on the newly constructed dataset to benchmark the vulnerability against both Commercial-Off-The-Shelf (COTS) FRS (Cognitec FaceVACS-SDK Version 9.4.2) and deep learning based FRS (Arcface) for three different morphing factors. Further, we also evaluate the performance of Morphing Attack Detection (MAD) in detecting such morphing attacks of newborn faces. We conduct experiments on four different Off-The-Shelf MAD techniques to benchmark the detection performance on newborn morph attacks.
A Statistical Extension of Byte-Pair Encoding

Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks. The goal is to split words (both in the source and target languages) into smaller units which then constitute the input and output vocabularies of the MT system. The aim of reducing the size of the input and output vocabularies is to increase the generalization capabilities of the translation model, enabling the system to translate and generate infrequent and new (unseen) words at inference time by combining previously seen sub-word units. Ideally, we would expect the created units to have some linguistic meaning, so that words are created in a compositional way. However, the most popular word-splitting method, Byte-Pair Encoding (BPE), which originates from the data compression literature, does not include explicit criteria to favor linguistic splittings nor to find the optimal sub-word granularity for the given training data. In this paper, we propose a statistically motivated extension of the BPE algorithm and an effective convergence criterion that avoids the costly experimentation cycle needed to select the best sub-word vocabulary size. Experimental results with morphologically rich languages show that our model achieves nearly-optimal BLEU scores and produces morphologically better word segmentations, which allows to outperform BPE’s generalization in the translation of sentences containing new words, as shown via human evaluation.
TRANSFORMING GLOBAL LEARNING WITH DIGITAL TECHNOLOGIES: A QUALITATIVE EXPLORATION OF THE USE OF EDUCATIONAL TECHNOLOGY WITH MARGINALISED, OUT-OF-SCHOOL CHILDREN LIVING IN REMOTE SETTINGS

TRANSFORMING GLOBAL LEARNING WITH DIGITAL TECHNOLOGIES: A QUALITATIVE EXPLORATION OF THE USE OF EDUCATIONAL TECHNOLOGY WITH MARGINALISED, OUT-OF-SCHOOL CHILDREN LIVING IN REMOTE SETTINGS
Estimation of Earthquake Early Warning Parameters for Eastern Gulf of Corinth and Western Attica Region (Greece). First Results

The main goal of an Earthquake Early Warning System (EEWS) is to alert before the arrival of damaging waves using the first seismic arrival as a proxy, thus becoming an important operational tool for real-time seismic risk management on a short timescale. EEWSs are based on the use of scaling relations between parameters measured on the initial portion of the seismic signal after the arrival of the first wave. To explore the plausibility of EEWSs around the Eastern Gulf of Corinth and Western Attica, amplitude and frequency-based parameters, such as peak displacement (Pd), the integral of squared velocity (IV 2) and the characteristic period (τc), were analyzed. All parameters were estimated directly from the initial 3 s, 4 s, and 5 s signal windows (tw) after the P arrival. While further study is required on the behavior of the proxy quantities, we propose that the IV 2 parameter and the peak amplitudes of the first seconds of the P waves present significant stability and introduce the possibility of a future on-site EEWS for areas affected by earthquakes located in the Eastern Gulf of Corinth and Western Attica. Parameters related to regional-based EEWS need to be further evaluated.
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning

This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning.We propose to use pre-trained ELECTRA discriminator to choose the best abstract word from five candidates.An upper attention and auto denoising mechanism is introduced to process the long sequences.The experiment results demonstrate that this contribution greatly facilitates the contextual language modeling in reading comprehension task.The ablation study is also conducted to show the validity of our proposed methods.
The quantum annealing gap and quench dynamics in the exact cover problem

Quenching and annealing are extreme opposites in the time evolution of a quantum system: Annealing explores equilibrium phases of a Hamiltonian with slowly changing parameters and can be exploited as a tool for solving complex optimization problems. In contrast, quenches are sudden changes of the Hamiltonian, producing a non-equilibrium situation. Here, we investigate the relation between the two cases. Specifically, we show that the minimum of the annealing gap, which is an important bottleneck of quantum annealing algorithms, can be revealed from a dynamical quench parameter which describes the dynamical quantum state after the quench. Combined with statistical tools including the training of a neural network, the relation between quench and annealing dynamics can be exploited to reproduce the full functional behavior of the annealing gap from the quench data. We show that the partial or full knowledge about the annealing gap which can be gained in this way can be used to design optimized quantum annealing protocols with a practical time-to-solution benefit. Our results are obtained from simulating random Ising Hamiltonians, representing hard-to-solve instances of the exact cover problem.
3D Pedestrian Detection in Farmland by Monocular RGB Image and Far-Infrared Sensing

The automated driving of agricultural machinery is of great significance for the agricultural production efficiency, yet is still challenging due to the significantly varied environmental conditions through day and night. To address operation safety for pedestrians in farmland, this paper proposes a 3D person sensing approach based on monocular RGB and Far-Infrared (FIR) images. Since public available datasets for agricultural 3D pedestrian detection are scarce, a new dataset is proposed, named as “FieldSafePedestrian”, which includes field images in both day and night. The implemented data augmentations of night images and semi-automatic labeling approach are also elaborated to facilitate the 3D annotation of pedestrians. To fuse heterogeneous images of sensors with non-parallel optical axis, the Dual-Input Depth-Guided Dynamic-Depthwise-Dilated Fusion network (D5F) is proposed, which assists the pixel alignment between FIR and RGB images with estimated depth information and deploys a dynamic filtering to guide the heterogeneous information fusion. Experiments on field images in both daytime and nighttime demonstrate that compared with the state-of-the-arts, the dynamic aligned image fusion achieves an accuracy gain of 3.9% and 4.5% in terms of center distance and BEV-IOU, respectively, without affecting the run-time efficiency.
‘Virtually’ the same learning outcomes. A case study of a virtual client briefed communication project across borders

As the world becomes increasingly more turbulent, complex and competitive, there has been an increased interest in creativity as the major force to prepare students for these demands. At the same time, digital native students today are used to working in an environment rich with instantly accessible information and knowledge, yet paradoxically students are less satisfied with their courses in the online environment. Educators are thus challenged to use innovative methods to nurture creativity online as they would do in the face-to-face classroom.&#x0D; In 2010, six European universities created an international project, named Cross Border Brand Communication (CBBC) that has taken place every year since but in 2020 took place online due to the Covid-19 pandemic. This article describes and analyzes the best practices resulting from this case and addresses the research questions as to whether the creative learning outcomes and teaching practices online are the same as face-to-face and to what extent are students satisfied with this learning environment. Reflection on the project and case indicates that although learning outcomes online are “virtually” the same, the student experience is inferior to such face-to-face projects.&#x0D;
A Causal Perspective on Meaningful and Robust Algorithmic Recourse

Algorithmic recourse explanations inform stakeholders on how to act to revert unfavorable predictions. However, in general ML models do not predict well in interventional distributions. Thus, an action that changes the prediction in the desired way may not lead to an improvement of the underlying target. Such recourse is neither meaningful nor robust to model refits. Extending the work of Karimi et al. (2021), we propose meaningful algorithmic recourse (MAR) that only recommends actions that improve both prediction and target. We justify this selection constraint by highlighting the differences between model audit and meaningful, actionable recourse explanations. Additionally, we introduce a relaxation of MAR called effective algorithmic recourse (EAR), which, under certain assumptions, yields meaningful recourse by only allowing interventions on causes of the target.