Reviewerly/exHarmony huggingface dataset:
Continuous Person Identification and Tracking in Healthcare by Integrating Accelerometer Data and Deep Learning Filled 3D Skeletons

With the technological development in healthcare, environments such as rehabilitation clinics and patients’ houses, are increasingly monitored by multi-device systems. To aggregate information, overcome privacy issues and device failures, it is essential to match measurements from different sources and associate them to a particular patient. While cameras are used to detect and track anonymized persons, wearable devices can acquire inertial and health information. The challenge is to correctly pair the tracked persons to their status, such as heart rate, collected by other devices. Recently, many works have been proposed, in several scenarios, to tackle sensor fusion-based tracking using a large variety of information. However, when the budget is limited, the involved sensing devices lack of inertial components, such as gyroscope, and may have low precision. In this work, we propose a novel solution to match unlabeled 3D skeletons, detected by a depth camera, with on-wrist wearable devices equipped only with accelerometer. Additionally, a Deep Learning submodule, named SkeletonRNN, is introduced to overcome camera failures in the 3D skeletons points detection and fill missing joints. A complete dataset containing skeletons and accelerations measurements, of dailies and rehabilitation activities, has been collected, manually annotated, and is available for testing purposes. We trained and tested the SkeletonRNN using data augmentation on our dataset, the final average 3D point prediction error is 11.00cm and the skeleton-device pairing accuracy of the overall system is 76.62% on a total of 231 chucks. Datasets, code and experiments can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/matteo-bastico/SkeletonRNN</uri> .
WebQuest as a Factor of Teaching Teamwork

An educational WebQuest can and should be used in teaching teamwork to students. Readiness for social interaction and implementation of one’s role in a team is a fundamentally important personal and professional quality (i.e., a subsystem of social and professional competency) in the modern world. The organization of group work of students is a mechanism for the development of both teamwork skills and personal experience of this work. The digital transformation of the educational environment creates favorable conditions for the development of competences as well as personal and professional qualities of students, including the willingness to work in a team: in the digital educational environment, it is possible to organize information interaction between students. An educational WebQuest as a competency-based learning technology stimulates both individual and group activities of students, which can be managed by students themselves. The authors have substantiated that version control in performing WebQuest tasks is an effective mechanism for obtaining primary information, which makes it possible to assess students’ readiness to work in a team. A method of processing the primary information reflecting the actions (and their results) during the students’ teamwork is proposed in this connection. The research involves a pedagogical experiment performed at Kuban State Technological University (Krasnodar, Russia), including the university-based regional school technology park. The analysis of the factual data shows the high efficiency of using WebQuests for organizing teamwork of both junior university students and school students involved in studying at the technology park. The theoretical significance of the research results is that they can contribute to further scientific understanding of such a problem as the development of students’ readiness for social interaction and realization of their role in the team, the practical significance lies in the fact that the research results can be a basis for the design of pedagogical technologies aimed at developing the specified personal and professional quality in students.
RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization

A release note is a technical document that describes the latest changes to a software product and is crucial in open source software development.However, it still remains challenging to generate release notes automatically.In this paper, we present a new dataset called RNSum, which contains approximately 82,000 English release notes and the associated commit messages derived from the online repositories in GitHub.Then, we propose classwise extractivethen-abstractive/abstractive summarization approaches to this task, which can employ a modern transformer-based seq2seq network like BART and can be applied to various repositories without specific constraints.The experimental results on the RNSum dataset show that the proposed methods can generate less noisy release notes at higher coverage than the baselines.We also observe that there is a significant gap in the coverage of essential information when compared to human references.Our dataset and the code are publicly available.
Technologizing youth training for entrepreneurship to fulfil sustainable development goals

The relevance of the article can be justified by the requirements for youth entrepreneurship training defined by sustainable development goals. In particular, these requirements are expected to promote the continuous, comprehensive and sustainable economic growth of the country, as well as full and decent employment for all. The object of the research is the technologization of youth training for entrepreneurship. The aim of the research lies in justifying and experimentally verifying the author’s method of introducing pedagogical technologies in youth training for entrepreneurship in vocational education institutions. Results: the author’s method of introducing pedagogical technologies is defined as a system of dependent variables, pedagogical factors and algorithms of their application. The article also presents a modelled algorithm of the simultaneous introduction of the author’s technologies (motivating future specialists towards business activity, improving their financial literacy and capacity for project activity and effective self-management) in educational institutions. The effectiveness of this method has been verified during a pedagogical experiment. This experiment aimed to prove the positive dynamics in the levels of future specialists’ entrepreneurship competence. Conclusions: In the short run, the author’s method of introducing pedagogical technologies in youth training for entrepreneurship allows one to optimize teachers’ activities (by simplifying the choice of pedagogical influence factors and isolating dependent variables), strengthen the systematicity, controllability, reproducibility and effectiveness of the author’s technologies. In the medium run, it will promote the technologicalization of vocational education and help achieve sustainable development goals. Experimental verification of the author’s method has shown a significant increase in students’ levels of entrepreneurship competence. Furthermore, it has proved the synergistic effect of the systematic use of innovative forms and methods of pedagogical influence on personality.
Semantic Interoperability Model in Healthcare Internet of Things Using Healthcare Sign Description Framework

The healthcare sector has experienced significant technological advances; however, interoperability is one of the biggest challenges. Interoperability in healthcare refers to the capacity to communicate across different healthcare environments. The format, language, syntax, and interpretation of data differ from one healthcare setting to another. Therefore, the lack of interoperability hampers effective communication and data exchange between two healthcare settings. Following the introduction of the Internet of Things (IoT) in healthcare, document-level interoperability is no longer the sole concern; device-level interoperability is also critical. This paper introduces a new Sign Description Framework for healthcare IoT called Healthcare Sign Description Framework (HSDF). Three different signs in healthcare, namely the Vital sign, Medication sign, and Symptom sign, are discussed here. Our proposal demonstrates how interoperability can be achieved using the novel healthcare sign description framework. Implementation of this framework will lead to improved diagnosis and increased cost-effectiveness of treatment.
Cleaning Highly Unbalanced Multisource Image Dataset for Quality Control in Cervical Precancer Screening

Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). Image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97% and F1 score above 94%) on the test set.
The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments

An audience's prior beliefs and morals are strong indicators of how likely they will be affected by a given argument.Utilizing such knowledge can help focus on shared values to bring disagreeing parties towards agreement.In argumentation technology, however, this is barely exploited so far.This paper studies the feasibility of automatically generating morally framed arguments as well as their effect on different audiences.Following the moral foundation theory, we propose a system that effectively generates arguments focusing on different morals.In an in-depth user study, we ask liberals and conservatives to evaluate the impact of these arguments.Our results suggest that, particularly when prior beliefs are challenged, an audience becomes more affected by morally framed arguments.
UAV Collaboration for Autonomous Target Capture

Tony, Lima Agnel Jana, Shuvrangshu Varun, V. P. Shorewala, Shantam Vidyadhara, B. V. Gadde, Mohitvishnu S. Kashyap, Abhishek Ravichandran, Rahul Krishnapuram, Raghu Ghose, DebasishCapturing moving objects using unmanned aerial vehicles (UAVs) is a challenging task. Many UAV applications require the capture of dynamic aerial targets. Successful interception requires accurate detection of the object, continuous tracking, and safe engagement without damaging any involving vehicles. This work presents the algorithmic details and hardware implementation for capturing a moving ball in a collaborative framework in an outdoor environment. The tracking and grabbing algorithm is developed using image-based guidance from the information of a monocular camera. The target image is detected and tracked using ML-based algorithm and Kalman filter. Finally, the proposed framework is simulated in ROS-Gazebo to evaluate the performance of individual algorithms and further implemented on hardware to validate the system’s real-time performance. The proposed system could be utilized for several applications like counter-UAV systems, fruit picking, among many others.
Weighted self Distillation for Chinese word segmentation

Recent researches show that multi-criteria resources and n-gram features are beneficial to Chinese Word Segmentation (CWS).However, these methods rely heavily on such additional information mentioned above and focus less on the model itself.We thus propose a novel neural framework, named Weighted self Distillation for Chinese word segmentation (WeiDC).The framework, which only requires unigram features, adopts self-distillation technology with four hand-crafted weight modules and two teacher models configurations.Experiment results show that WeiDC can make use of character features to learn contextual knowledge and successfully achieve state-ofthe-art or competitive performance in terms of strictly closed test settings on SIGHAN Bakeoff benchmark datasets.Moreover, further experiments and analyses also demonstrate the robustness of WeiDC.Source codes of this paper are available on Github 1 .
OIE@OIA: an Adaptable and Efficient Open Information Extraction Framework

Different Open Information Extraction (OIE) tasks require different types of information, so the OIE field requires strong adaptability of OIE algorithms to meet different task requirements.This paper discusses the adaptability problem in existing OIE systems and designs a new adaptable and efficient OIE system -OIE@OIA as a solution.OIE@OIA follows the methodology of Open Information eXpression (OIX): parsing a sentence to an Open Information Annotation (OIA) Graph and then adapting the OIA graph to different OIE tasks with simple rules.As the core of our OIE@OIA system, we implement an endto-end OIA generator by annotating a dataset (we make it open available) and designing an efficient learning algorithm for the complex OIA graph.We easily adapt the OIE@OIA system to accomplish three popular OIE tasks.The experimental show that our OIE@OIA achieves new SOTA performances on these tasks, showing the great adaptability of our OIE@OIA system.Furthermore, compared to other end-to-end OIE baselines that need millions of samples for training, our OIE@OIA needs much fewer training samples (12K), showing a significant advantage in terms of efficiency.
Using Speech and NLP Resources to build an iCALL platform for a minority language, the story of An Scéalaí, the Irish experience to date

This paper describes how emerging linguistic resources and technologies can be used to build a language learning platform for Irish, an endangered language.This platform, An Scéalaí, harvests learner corpora -a vital resource both to study the stages of learners' language acquisition and to guide future platform development.A technical description of the platform is provided, including details of how different speech technologies and linguistic resources are fused to provide a holistic learner experience.The active continuous participation of the community, and platform evaluations by learners and teachers, are discussed.
An Enhanced Approach for CP-ABE with Proxy Re-encryption in IoT Paradigm

In Internet of Things (IoT), encryption is a technique in which plaintext is converted to ciphertext to make it non-recovered by the attacker without secret key. Ciphertext policy attribute based encryption (CP-ABE) is an encryption technique aimed at multicasting feature i.e. user can only decrypt the message if policy of attributes mentioned in ciphertext is satisfied by the user’s secret key attributes. In literature, the authors have improvised the existing technique to enhance the naïve CP-ABE scheme. Recently, in 2021, Wang et al. have proposed the CP-ABE scheme with proxy re-encryption and claimed it to be efficient as to its predecessors. However, it follows the variable length ciphertext in which size of ciphertext is increased with the number of attributes. Also, it leads to computation overhead on the receiver during decryption which will be performed by the IoT devices. Thus, in this paper we have proposed the improved scheme to provide the constant length ciphertext with proxy re-encryption to reduce the computation and communication time. The proposed scheme is secured under Decisional Bilinear Diffie-Hellman (DBDH) problem.
Hierarchical Knee Image Synthesis Framework for Generative Adversarial Network: Data From the Osteoarthritis Initiative

Medical images synthesis is useful to address persistent issues such as the lack of training data diversity and inflexibility of traditional data augmentation faced by medical image analysis researchers when developing their deep learning models. Generative adversarial network (GAN) can generate realistic image to overcome the abovementioned problems. We proposed a GAN model with hierarchical framework (HieGAN) to generate high-quality synthetic knee images as a prerequisite to enable effective training data augmentation for deep learning applications. During the training, the proposed framework embraced attention mechanism before the 256 × 256 scale in generator and discriminator to capture salient information of knee images. Then, a novel pixelwise-spectral normalization configuration was implemented to stabilize the training performance of HieGAN. We evaluated the proposed HieGAN on large scale knee image dataset by using Am Score and Mode Score. The results showed that HieGAN outperformed all relevant state-of-art. Hence, HieGAN can potentially serve as an important milestone to promote future development of more robust deep learning models for knee image segmentation. Future works should extend the image synthesis evaluation to clinical-related Visual Turing Test and synthetic data augmentation for deep learning segmentation task.
Data Graphs

At the foundation of any knowledge graph is the principle of first applying a graph abstraction to data, resulting in an initial data graph. We now discuss a selection of graph-structured data models that are commonly used in practice to represent data graphs. We then discuss the primitives that form the basis of graph query languages used to interrogate such data graphs.
Use of the ND tool: An open tool for the synthetic generation of 1-min solar data from hourly means with geographic flexibility

In this paper, we present a description of how to use a web based tool employing the ND model by Larrañeta et al. [1]. The tool is suited for downscaling DNI, GHI or coupled DNI and GHI from 1-h to 1-min. It requires only an annual solar radiation dataset in the hourly resolution as input and provides 1-min data in any location without local adaptation. We have applied the tool in three locations with different climates. The similitude between measured and generated DNI distributions has been evaluated through the Kolmogorov-Smirnov test Integral (KSI) for annual synthetic 1-min datasets. Obtained KSI values range from 6.2 W/m2 to 11.5 W/m2
Are Shortest Rationales the Best Explanations for Human Understanding?

Existing self-explaining models typically favor extracting the shortest possible rationales -snippets of an input text "responsible for" corresponding output -to explain the model prediction, with the assumption that shorter rationales are more intuitive to humans.However, this assumption has yet to be validated.Is the shortest rationale indeed the most humanunderstandable?To answer this question, we design a self-explaining model, LimitedInk, which allows users to extract rationales at any target length.Compared to existing baselines, LimitedInk achieves compatible endtask performance and human-annotated rationale agreement, making it a suitable representation of the recent class of self-explaining models.We use LimitedInk to conduct a user study on the impact of rationale length, where we ask human judges to predict the sentiment label of documents based only on LimitedInkgenerated rationales with different lengths.We show rationales that are too short do not help humans predict labels better than randomly masked text, suggesting the need for more careful design of the best human rationales. 1
PENGARUH MODEL PEMBELAJARAN QUANTUM TEACHING DAN TALKING STICK TERHADAP HASIL BELAJAR IPS PADA PESERTA DIDIK SEKOLAH DASAR DI KECAMATAN DEMAK

The purpose of research it is to determine : (1) the effect of the application of the model study of quantum against the results of learning Theme 5 on the charge lesson IPS participants learners grade elementary school in the District of Demak , (2) the effect of the application of the model of learning talking stick to the result of learning Scene 5 in charge of the lesson IPS participants learners grade elementary school in the District of Demak, and (3) differences in the influence of the model of learning quantum teaching and talking stick to the result of learning Theme 5 on the charge lesson IPS participants learners grade elementary school in the District of Demak. The method of research using a quasi-experimental design , with the population of the entire student class V School Elementary District of Demak from 55 elementary schools with the number of 1,627 participants learners . The sample of the study is the participant students grade VA SD Bintoro 5 (35 participant students ) and a class VB SD Bintoro 5 (34 participant students ), as a class experiment . As a class control is student class V SD Bintoro 2 with the number of 41 participants learners.
A Low-Cost Rough Terrain Explorer Robot Fabrication Using Rocker Bogie Mechanism

Rocker-bogie mechanism is proven and renowned for its excellent performance in all types of exploration robots, especially in Mars rover robots. This paper aims to fabricate a low–cost explorer robot with a rocker-bogie mechanism that can run in all sorts of terrains. As the explorer robot is a prototype version of the Mars rover robot, the entire structure's size is confined. Only the mechanism is demonstrated using standard DC motors and wheels. A wireless communication system is implemented using internet of things technology, and a gas sensor is placed in the robot to collect the surrounding environment's gas level data. In the end, performance tests are accomplished to check the rocker-bogie mechanism's stability, and the sensor's data analysis is done.
Generating Personalized Explanations for Recommender Systems Using a Knowledge Base

In the last decade, we have seen an increase in the need for interpretable recommendations. Explaining why a product is recommended to a user increases user trust and makes the recommendations more acceptable. The authors propose a personalized explanation generation system, PEREXGEN (personalized explanation generation) that generates personalized explanations for recommender systems using a model-agnostic approach. The proposed model consists of a recommender and an explanation module. Since they implement a model-agnostic approach to generate personalized explanations, they focus more on the explanation module. The explanation module consists of a task-specialized item knowledge graph (TSI-KG) generation from a knowledge base and an explanation generation component. They employ the MovieLens and Wikidata datasets and evaluate the proposed system's model-agnostic properties using conventional and state-of-the-art recommender systems. The user study shows that PEREXGEN generates more persuasive and natural explanations.