Reviewerly/exHarmony huggingface dataset:
Wavefront restoration from lateral shearing data using spectral interpolation

Although a lateral-shear interferometer is robust against optical component vibrations, its interferogram provides information about differential wavefronts rather than the wavefronts themselves, resulting in the loss of specific frequency components. Previous studies have addressed this limitation by measuring four interferograms with different shear amounts to accurately restore the two-dimensional wavefront. This study proposes a technique that employs spectral interpolation to reduce the number of required interferograms. The proposed approach introduces an origin-shift technique for accurate spectral interpolation, which in turn is implemented by combining two methods: natural extension and least-squares determination of ambiguities in uniform biases. Numerical simulations confirmed that the proposed method accurately restored a two-dimensional wavefront from just two interferograms, thereby indicating its potential to address the limitations of the lateral-shear interferometer.
Contextual Embeddings for Ukrainian: A Large Language Model Approach to Word Sense Disambiguation

This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Ukrainian language based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on the dataset generated in an unsupervised way to obtain better contextual embeddings for words with multiple senses. The paper presents a method for generating a new dataset for WSD evaluation in the Ukrainian language based on the SUM dictionary. We developed a comprehensive framework that facilitates the generation of WSD evaluation datasets, enables the use of different prediction strategies, LLMs, and pooling strategies, and generates multiple performance reports. Our approach shows 77,9% accuracy for lexical meaning prediction for homonyms.
Graph-based multi-layer querying in Parseme Corpora

We present a graph-based tool which can be used to explore Verbal Multi-Word Expression (VMWE) annotated in the Parseme project. The tool can be used for linguistic exploration on the data, for helping the manual annotation process and to search for errors or inconsistencies in the annotations.
Exploring Data Augmentation for Code Generation Tasks

Advances in natural language processing, such as transfer learning from pre-trained language models, have impacted how models are trained for programming language tasks too. Previous research primarily explored code pre-training and expanded it through multi-modality and multi-tasking, yet the data for downstream tasks remain modest in size. Focusing on data utilization for downstream tasks, we propose and adapt augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. Further analysis suggests that our methods work orthogonally and show benefits in output code style and numeric consistency. We also discuss test data imperfections.
Hate Speech, incitamento all’odio, incitación al odio: EU Parallel Corpora, Legal Discourse, Metadiscourse and Translation

According to Sandrelli (2019, p. 111), &amp;ldquo;the multilingual co-drafting process produces equally authentic language versions of the same document in all the EU official languages. However, what actually happens in practice is that EU legislation is drafted in one language (English, in most cases) and is then translated into all the other ones&amp;rdquo;. Starting from this assumption, the aim of this paper is to investigate a series of hate speech-related EU documents in order to explore certain features of hate discourse and hate discourse-related phraseology, metadiscourse and translation issues in the English, Italian and Spanish versions of the texts. The quantitative and qualitative analysis will look at the use of peculiar language constructions in the three languages in relation, among other features, to hateful rhetoric, discrimination, violent behaviour, intolerance, harassment, gender inequalities, extremism and racism. Additionally, the features of metadiscourse (Hyland, 2019 [2005]) will be scrutinised in the three languages in order to ascertain whether and to what extent they function as rhetorical markers conferring a persuasive rather than merely an informative and prescriptive character to the texts under consideration. The parallel corpora include documents which date back to 2021. They appear to have as their underlying aim that of disseminating and circulating hate discourse-related counteractions, good practices and procedures in controversial cultural contexts and environments, especially those associated with such divisive matters as the safeguarding of human rights and human dignity of diverse religious, ethnic and social groups.
Students Engagement in Flipped Course through Motivational Design

This study investigates the impact of motivating design on participation in an flipped course, exercising primary sources similar as published literature, academic journals, and other applicable information. The exploration process commences with relating suitable sources through library exploration. foreign incitement gave pupils marks and instruments for course objects. natural provocation interventions created engaging and demanding course challenges. Both motivational design interventions bettered pupil engagement. still, natural provocation bettered pupil engagement further than foreign provocation, especially in delicate tasks. The exploration adds to the motivational design and flips classroom literature by showing that motivational design interventions increase pupil engagement in Indonesian seminaries. The results show that natural provocation treatments, which give interesting and hard conditioning, may be more successful at fostering engagement and provocation than foreign provocation development.
Bibliometric Analysis of Open Government: A Study on the Open Government Partnership

This study aims to analyze the Open Government Partnership (OGP) bibliometrics. The OGP is a global effort to realize open government. In the Google Scholar database, in the span of the last decade, there have been 117 OGP articles that have been cited 486 times. From the article, there are 15 keyword clusters, 1196 total link strengths, and 428 occurrences. Open government partnership, national government, action plans, local government, and multilateral institutions are the keywords with the highest scores. Currently, 73 countries and 110 local governments have joined the OGP. To achieve open government, OGP is committed to increasing the availability of information on government activities, supporting citizen participation, applying the highest standards of professional integrity, and increasing access to new technologies. As a follow-up, OGP established ten policy areas, namely: anti-corruption and integrity, civic space, digital governance, fiscal openness, inclusion, justice, natural resources, open parliaments, public service delivery, and the right to information. OGP has also forged partnerships with leading multilateral institutions to promote and support open government
Grambank’s Typological Advances Support Computational Research on Diverse Languages

Hannah J. Haynie, Damián Blasi, Hedvig Skirgård, Simon J. Greenhill, Quentin D. Atkinson, Russell D. Gray. Proceedings of the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP. 2023.
A Cancellation Law for Probabilistic Processes

We show a cancellation property for probabilistic choice.If µ ⊕ ρ and ν ⊕ ρ are branching probabilistic bisimilar, then µ and ν are also branching probabilistic bisimilar.We do this in the setting of a basic process language involving non-deterministic and probabilistic choice and define branching probabilistic bisimilarity on distributions.Despite the fact that the cancellation property is very elegant and concise, we failed to provide a short and natural combinatorial proof.Instead we provide a proof using metric topology.Our major lemma is that every distribution can be unfolded into an equivalent stable distribution, where the topological arguments are required to deal with uncountable branching.
Direct Text to Speech Translation System Using Acoustic Units

This paper proposes a direct text to speech translation system using discrete acoustic units. This framework employs text in different source languages as input to generate speech in the target language without the need for text transcriptions in this language. Motivated by the success of acoustic units in previous works for direct speech to speech translation systems, we use the same pipeline to extract the acoustic units using a speech encoder combined with a clustering algorithm. Once units are obtained, an encoder-decoder architecture is trained to predict them. Then a vocoder generates speech from units. Our approach for direct text to speech translation was tested on the new CVSS corpus with two different text mBART models employed as initialisation. The systems presented report competitive performance for most of the language pairs evaluated. Besides, results show a remarkable improvement when initialising our proposed architecture with a model pre-trained with more languages.
SA-BiGCN: Bi-Stream Graph Convolution Networks With Spatial Attentions for the Eye Contact Detection in the Wild

Eye contact is essential in transmitting information and intention in the wild environment (e.g., urban streets or parking lots) with mixed vehicles and pedestrians. Compared with the vision image data, the human skeleton data are deemed to be robust to unconstrained surroundings and illumination. However, the skeleton graph-based approaches are mainly used for the action recognition. It is challenging to directly apply them to the eye detection task, which is momentary and dynamic given the complex wild environment. This paper proposes a Bi-stream Spatial Attention Graph Convolution Network (SA-BiGCN) for eye contact detection in the wild. We design a directed, nose-centric skeleton graph to capture relevant and hierarchical information and their interactions. We also propose a Bi-stream graph convolution network model with spatial attention to dynamically extract and fuse skeleton joints and bones information. The model was validated by comparing with state-of-art models on three large-scale public datasets, including JAAD, PIE, and LOOK. The results highlight the accuracy and generalization performance of the proposed SA-BiGCN model in detecting the eye contact in the wild environment. The ablation analysis validates the importance of the skeleton graph design, the spatial attention mechanism in the feature fusion process, as well as the model robustness against noisy skeleton data in terms of part occlusions, block occlusions, random occlusions, and random deviations.
CloudFactory: An Open Toolkit to Generate Production-like Workloads for Cloud Infrastructures

Cloud infrastructures are large-scale and complex platforms designed to host a wide diversity of applications and workloads. Given these complexity and scale factors, simulators and benchmarks are broadly adopted in vitro to study their behaviors, prototype new software components and heuristics, and evaluate their effective performances.However, both state-of-the-art simulations and benchmarks may suffer from a representativeness problem, as the reported results can vary depending on their input workloads. For example, a Infrastructure-as-a-Service (IaaS) platform aims to host Virtual Machines (VMs), whose characteristics (resource configurations, workload intensity, arrival/departure rate, etc.) can greatly differ depending on Cloud providers and public/private deployments. Addressing this IaaS representativeness thus requires Cloud providers to share production-scale datasets, which might be considered sensitive. Moreover, Simulations and benchmarks require a specific experiment scenario that cannot be easily generated from Cloud providers characteristics.To address these issues, this paper introduces CloudFactory, a IaaS workload generator. Our contribution is first composed of a library that can be used by Cloud providers to share IaaS statistics, instead of raw datasets. Then, we introduce a generator designed to produce realistic VM workloads that match these statistics. CloudFactory is made available as open-source software that can be adopted by Cloud providers and researchers to foster the evaluation of new contributions.As an example, we perform an analysis on scheduling evolution for different IaaS workload intensity of two different Cloud providers: Microsoft Azure and Chameleon. We also report on OVHcloud statistics computed from CloudFactory and compare them to other Cloud providers.
Using Mind Mapping to Improve Students' Writing Skills of the Eleventh Grade at MAS Yaspen Nurul Huda Serapuh ABC in the Academic of the Year 2023

The aim of this study is to investigate the use of mind mapping as a tool to improve the writing skills of students in Yaspen Nurul Huda Serapuh ABC 2023. Four students with writing difficulties participated in the study. In the study, researchers used observational techniques to determine students’ writing abilities through mind mapping. Teacher performed descriptive analyzes of students’ writing test scores before and after they applied the ”mind mapping” technique in their writing. The purpose of this study was also to determine the effectiveness of mind mapping in teaching writing. The results of this study provide insight into the effectiveness of using mind mapping in writing classes in these schools to improve students writing skills.&#x0D; &#x0D; Keywords: using mind mapping, improve students’ skills, results of study
A Grounded Conceptual Model for Ownership Types in Rust

Programmers learning Rust struggle to understand ownership types, Rust’s core mechanism for ensuring memory safety without garbage collection. This paper describes our attempt to systematically design a pedagogy for ownership types. First, we studied Rust developers’ misconceptions of ownership to create the Ownership Inventory, a new instrument for measuring a person’s knowledge of ownership. We found that Rust learners could not connect Rust’s static and dynamic semantics, such as determining why an ill-typed program would (or would not) exhibit undefined behavior. Second, we created a conceptual model of Rust’s semantics that explains borrow checking in terms of flow-sensitive permissions on paths into memory. Third, we implemented a Rust compiler plugin that visualizes programs under the model. Fourth, we integrated the permissions model and visualizations into a broader pedagogy of ownership by writing a new ownership chapter for The Rust Programming Language , a popular Rust textbook. Fifth, we evaluated an initial deployment of our pedagogy against the original version, using reader responses to the Ownership Inventory as a point of comparison. Thus far, the new pedagogy has improved learner scores on the Ownership Inventory by an average of 9
PENSHIP: Implementation of Image Processing Methods for the Mission of Kontes Kapal Cepat Tak Berawak Nasional (KKCTBN)

Artificial intelligence using image processing method is the development of robotics technology that can make robots navigate automatically. The automatic navigation system is one of the important elements in the automation system in managing the movement of this ship robot. However, until now it is still rare to find water transportation system tools that are equipped with the ability to navigate and run autonomously. In this research an autonomous control system has been designed for ship robots by utilizing image processing technology on a mini pc platform. The RGB value of the image that has been captured with the camera and converted using the HSV filter and the threshold value that is set on the mini pc to produce an output image that has been set in the reading room so that the ship’s robot can move automatically through obstacles. The Obstacle Detection System has achieved 80-90% accuracy at an ideal distance of 1-2 meters between the webcam and a spherical object and the ship has successfully reached the finish line determined by the committee of the Kontes Kapal Cepat Tak Berawak Nasional (KKCTBN).
Implementation of Information Communication Technology in Enhancing Early Childhood Language Development

This study aims to determine the effect of using Information Communication and Technology in improving language development in early childhood. The research method used is true experimental research. The subjects of this study were children aged 4-6 years in grades B1 and B2, the number of group B1 was 17 children and the number of group B2 was 17 children. The data collection tool used is in the form of observation sheets for the language development of children aged 4-6 years after being treated with ICT-based learning media. The results of the t-test show that the asymp. Significant (2-tailed) of 0.012 with a significant value &lt;0.06. The hypothesis is accepted, meaning that ICT-based learning media can improve the language development of children aged 4-6 years for the better. Therefore, researchers provide suggestions that educators can use internet media, computers and technology as an alternative in learning activities, especially in early childhood language development.
Marathi text summarization through NLP and deep learning mechanism

&lt;p&gt;Every day, an ever-increasing amount of people gain access to the internet platform. This has proven to be efficient in creating cost-effective internet platform deployments and applications. The growth in the amount of people using the platform has resulted in a rise in the quantity of information accessible on the internet in the form of news, media, and other forms of communication. This causes evaluating and comprehending a significant amount of textual information a very challenging task. For the objective of generating textual summaries for Marathi texts, an effective and trustworthy approach is required. Through the use of machine learning methods, a successful strategy for extracting summary for the Marathi text has been generated for this objective. To obtain the Marathi text summary, the proposed method uses feature extraction as well as deep belief networks and decision tree methodologies. The experimentation was carried out on the performance of the Term Frequency-Inverse Document Frequency (TF-IDF) in the stopword elimination procedure, along with the evaluation of the summarization outcome which achieves a Mean Absolute Error (MAE) of 2.8 for the stopword removal approach through TF-IDF technique and a precision of 95.49% with an accuracy of 92.76%.&lt;/p&gt;
Improving Transitions of Care: Designing a Blockchain Application for Patient Identity Management

&nbsp; The annual ConV2X is a leading international health tech symposium driving real world evidence, strategy, research, operations and trends to create a blueprint for a new digital health era. The 2021 symposium&nbsp;featured a scientific program of academic/research presentations in addition to business and industry talks. The research track&nbsp;focused on exploring and sharing developments in blockchain and emerging technologies in health and clinical medicine. Submissions were based on original research, conceptual frameworks, proposed applications, position papers, case studies, and real-world implementation. Selection was based on a peer-review process. Faculty, students, and industry researchers were encouraged to submit abstracts to present&nbsp;ideas before an informed and knowledgeable audience of industry leaders, policy makers, funders, and researchers. This presentation was selected by the scientific review committee. Submission Review Committee Dave Kochalko, CEO of ARTiFACTS Anjum Khurshid, UT Austin Carlos Caldas, UT Engineering Gil Alterovitz, Harvard Medical School Kayo Fujimoto, UT Health Houston Lei Zhang, University of Glasglow Sean Manion, CSciO of ConsenSys Health Vijayakuman Varadarajan, University of South Wales Vikram Dhillon, Wayne State University Yuichi Ikeda, Kyoto University &nbsp
Semi-autonomous Flight for Jet Spraying Drones to Inspect and Repair Structures

“Jet Spraying Drone” that can spray repair materials onto concrete structures has been developed. However, manual operation of drones that flies in at high altitudes makes it difficult to fly and keep a suitable position for spray works. In this study, we developed a “semi-autonomous flight system” that can keep position autonomously and have versatility. With this system, drones keep distance and angle to the target wall, and altitude autonomously, and can be operated manually only to the left and right direction. The experimental results on two different types of drones showed the flight accuracy and versatility of the system.
Enhancing a cybersecurity curriculum development tool with a competence framework to meet industry needs for cybersecurity

Current and emerging cybersecurity and computing professionals must manage their competencies and skills according to current job market needs, including formal education, professional certifications, and experience. Recent studies show that cybersecurity skills will be in high demand in the industry in the coming years. There is currently a significant gap between available jobs and the skills of suitable candidates, and existing software engineering and cybersecurity training has an important role to play in addressing this. This puts pressure on cybersecurity education providers, such as universities, to align with industry needs and develop the content of cybersecurity courses and curricula more systematically based on business needs. We asked 88 Finnish IT professionals working in software development and cybersecurity how much time they spend developing their skills in a year, what types of training they use, and what topics they need more training on, in order to understand the industry’s security assurance training needs and the level of competence required. The solution to systematically develop cybersecurity course and curriculum content is to create a planning framework that combines the European Cybersecurity Taxonomy and the European Cybersecurity Skills Framework, including the e-Competence level, and university course content, to identify role-based training needs and gaps in course content.