Reviewerly/exHarmony huggingface dataset:
Deep-CSA: Deep Contrastive Learning for Dynamic Survival Analysis With Competing Risks

Survival analysis (SA) is widely used to analyze data in which the time until the event is of interest. Conventional SA techniques assume a specific form for viewing the distribution of survival time as the hitting time of a stochastic process, and explicitly model the relationship between covariates and the distribution of the events hitting time. Although valuable, existing SA models seldom consider to model the dynamic correlations between covariates and more than one event of interest (i.e., competing risks) in the disease progression of subjects. To alleviate this critical problem, we propose a novel deep contrastive learning model to obtain a deep understanding of disease progression of subjects with competing risks from their longitudinal observational data. Specifically, we design a self-supervised objective for learning dynamic representations of subjects suffering from multiple competing risks, such that the relationship between covariates and each specific competing risk changes over time can be well captured. Experiments on two open-source clinical datasets, i.e., MIMIC-III and EICU, demonstrate the effectiveness of our proposed model, with remarkable improvements over the state-of-the-art SA models.
Bimodal Camera Pose Prediction for Endoscopy

Deducing the 3D structure of endoscopic scenes from images is exceedingly challenging. In addition to deformation and view-dependent lighting, tubular structures like the colon present problems stemming from their self-occluding and repetitive anatomical structure. In this paper, we propose SimCol, a synthetic dataset for camera pose estimation in colonoscopy, and a novel method that explicitly learns a bimodal distribution to predict the endoscope pose. Our dataset replicates real colonoscope motion and highlights the drawbacks of existing methods. We publish 18k RGB images from simulated colonoscopy with corresponding depth and camera poses and make our data generation environment in Unity publicly available. We evaluate different camera pose prediction methods and demonstrate that, when trained on our data, they generalize to real colonoscopy sequences, and our bimodal approach outperforms prior unimodal work.
Code smells in Elixir

Elixir is a new functional programming language whose popularity is rising in the industry. However, there are few works in the literature focused on studying the internal quality of systems implemented in this language. Particularly, to the best of our knowledge, there is currently no catalog of code smells for Elixir. Therefore, in this paper, through a grey literature review, we investigate whether Elixir developers discuss code smells. Our preliminary results indicate that 11 of the 22 traditional code smells cataloged by Fowler and Beck are discussed by Elixir developers. We also propose a list of 18 new smells specific for Elixir systems and investigate whether these smells are currently identified by Credo, a well-known static code analysis tool for Elixir. We conclude that only two traditional code smells and one Elixir-specific code smell are automatically detected by this tool. Thus, these early results represent an opportunity for extending tools such as Credo to detect code smells and then contribute to improving the internal quality of Elixir systems.
Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter

PPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values ​​of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the model that uses polynomial kernel produces higher accuracy, both applied to the One Against One and One Against Rest methods. In contrast, the model that uses the RBF kernel produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus.&#x0D;
Visualizing the invisible: User-centered design of a system for the visualization of flows and concentrations of particles in the air

This study presents two experiments addressing the representation of scientific data, in particular airflows, with a user-centered design approach. Our objective is to provide users feedback to data visualization designers to help them choose an air flow representation that is understandable and attractive for non-experts. The first study focuses on static markers allowing to visualize an airflow, with information characterizing the direction and the intensity. In a second study, carried out in an immersive virtual environment, two information were added, the temporal evolution and the concentration of pollutants in the air. To measure comprehension and attractiveness, participants were asked to answer items on Likert scales (experiment 1) and to answer User Experience Questionnaire (experiment 2). The results revealed that arrows seem to be a very common and understandable form to represent orientation and direction of flow, but that they should be improved to be more attractive by making them brighter and more transparent, as the representation could occlude the scene, especially in virtual reality. To solve this problem, we suggest giving the users the ability to define the specific area where they want to see the air flow, using a cross-sectional view. Vector fields and streamlines could therefore be applied in a virtual reality context.
Parallel and Practical Approach of Efficient Image Chaotic Encryption Based on Message Passing Interface (MPI)

Encrypting pictures quickly and securely is required to secure image transmission over the internet and local networks. This may be accomplished by employing a chaotic scheme with ideal properties such as unpredictability and non-periodicity. However, practically every modern-day system is a real-time system, for which time is a critical aspect for achieving the availability of the encrypted picture at the proper moment. From there, we must improve encryption's performance and efficiency. For these goals, we adopted the distributed parallel programming model, namely, the message passing interface (MPI), in this study. Using the message passing interface, we created a novel parallel crypto-system. The suggested approach outperforms other models by 1.5 times. The suggested parallel encryption technique is applicable.
An RG-FLAT-CRF Model for Named Entity Recognition of Chinese Electronic Clinical Records

The goal of Clinical Named Entity Recognition (CNER) is to identify clinical terms from medical records, which is of great importance for subsequent clinical research. Most of the current Chinese CNER models use a single set of features that do not consider the linguistic characteristics of the Chinese language, e.g., they do not use both word and character features, and they lack morphological information and specialized lexical information on Chinese characters in the medical field. We propose a RoBerta Glyce-Flat Lattice Transformer-CRF (RG-FLAT-CRF) model to address this problem. The model uses a convolutional neural network to discern the morphological information hidden in Chinese characters, and a pre-trained model to obtain vectors with medical features. The different vectors are stitched together to form a multi-feature vector. To use lexical information and avoid the problem of word separation errors, the model uses a lattice structure to add lexical information associated with each word, which can be used to avoid the problem of word separation errors. The RG-FLAT-CRF model scored 95.61%, 85.17%, and 91.2% for F1 on the CCKS 2017, 2019, and 2020 datasets, respectively. We used statistical tests to compare with other models. The results show that most p-values less than 0.05 are statistically significant.
A Never-Ending Project for Humanity Called “the Web”

In this paper we summarized the main historical steps in making the Web, its foundational principles and its evolution. First we mention some of the influences and streams of thought that interacted to bring the Web about. Then we recall that its birthplace, the CERN, had a need for a global hypertext system and at the same time was the perfect microcosm to provide a cradle for the Web. We stress how this invention required to strike a balance between the integration of and the departure from the existing and emerging paradigms of the day. We then review the pillars of the Web architecture and the features that made the Web so viral compared to competitors. Finally we survey the multiple mutations the Web underwent no sooner it was born, evolving in multiple directions. We conclude on the fact the Web is now an architecture, an artefact, a science object and a research and development object, and of which we haven’t seen the full potential yet.
Quantum Computation of Hydrogen Bond Dynamics and Vibrational Spectra

Calculating the observable properties of chemical systems is often classically intractable and is widely viewed as a promising application of quantum information processing. Yet one of the most common and important chemical systems in nature - the hydrogen bond - has remained a challenge to study using quantum hardware on account of its anharmonic potential energy landscape. Here, we introduce a framework for solving hydrogen-bond systems and more generic chemical dynamics problems using quantum logic. We experimentally demonstrate a proof-of-principle instance of our method using the QSCOUT ion-trap quantum computer, in which we experimentally drive the ion-trap system to emulate the quantum wavepacket of the shared-proton within a hydrogen bond. Following the experimental creation of the shared-proton wavepacket, we then extract measurement observables such as its time-dependent spatial projection and its characteristic vibrational frequencies to spectroscopic accuracy (3.3 cm$^{-1}$ wavenumbers, corresponding to > 99.9% fidelity). Our approach introduces a new paradigm for studying the quantum chemical dynamics and vibrational spectra of molecules, and when combined with existing algorithms for electronic structure, opens the possibility to describe the complete behavior of complex molecular systems with unprecedented accuracy.
Siamese-Gap Network for Early Detection of Knee Osteoarthritis

Knee OsteoArthritis (OA) is a common musculoskeletal disorder, which causes reduced mobility for seniors. Due to the semi-quantitative nature of the Kellgren-Lawrence (KL) grading system, medical practitioners’ grading is subjective, being entirely based on their experience. With the development of computer vision, Computer-Aided Diagnosis (CAD) systems based on deep learning methods such as convolutional neural networks (CNNs) have shown success in knee OA diagnosis. In this paper, we propose a new approach, the so-called Siamese-GAP Network, for the early detection of knee OA through a KL-grade classification. More precisely, a set of Global Average Pooling (GAP) layers is integrated into the Siamese network used to extract features from each level. The obtained features are then combined to improve the classification performance. Our experimental results on baseline X-ray images from the OsteoArthritis Initiative (OAI) dataset show that the proposed approach presents potential results for the early detection of knee OA.
Fuzzy-PI Controller Tuned with HBBO for 2 DOF Robot Trajectory Control

The main aim of our study is to control a 2 DOF robot manipulator with Fuzzy-PI and adjust its parameters with human behavior-based optimization. The fuzzy system that we have introduced is based on Takagi&ndash;Sugeno-type: it adequately handles uncertainties, ambiguities and it is able to tune the PID parameters in non-linearity situations. The HBBO was dedicated to find the best scaling factor of fuzzy logic as well as the PI&rsquo;s parameter. The results of our study show the effectiveness of the proposed algorithm to optimize the controller&rsquo;s parameter; therefore, the dynamics of robot follows perfectly a desired trajectories.
Beyond duplicates

Software projects use Issue Tracking Systems (ITS) like JIRA to track issues and organize the workflows around them. Issues are often inter-connected via different links such as the default JIRA link types Duplicate, Relate, Block, or Subtask. While previous research has mostly focused on analyzing and predicting duplication links, this work aims at understanding the various other link types, their prevalence, and characteristics towards a more reliable link type prediction. For this, we studied 607,208 links connecting 698,790 issues in 15 public JIRA repositories. Besides the default types, the custom types Depend, Incorporate, Split, and Cause were also common. We manually grouped all 75 link types used in the repositories into five general categories: General Relation, Duplication, Composition, Temporal / Causal, and Workflow. Comparing the structures of the corresponding graphs, we observed several trends. For instance, Duplication links tend to represent simpler issue graphs often with two components and Composition links present the highest amount of hierarchical tree structures (97.7%). Surprisingly, General Relation links have a significantly higher transitivity score than Duplication and Temporal / Causal links.
The Candy Workshop: Supporting Rich Sensory Modalities in Constructive Data Physicalization

Constructive data physicalization (i.e. the creation of visualizations by non-experts using physical elements) is a promising research area in a context of rapid democratization of data collection and visualization, driven notably by the quantified-self movement. Despite a prolific body of work developed to explore physicalization as a mean to communicate data to individuals, little is known about how people transform data into physical artefacts. Current research also falls short in studying constructive physicalizations using other sensory modalities than sight or touch. Building on the principles of data edibilization, we propose to use candies as a medium to study constructive data physicalization processes, due to their ability to leverage multiple sensory channels. We conducted a preliminary study (candy workshop) to gain insights into how people make use of various sensory modalities in the construction of data physicalizations. We hope to inspire new research using candies as accessible research material.
Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation

Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (e.g., emotions and topics). Existing works often utilize fine-tuning or resort to extra attribute classifiers, yet suffer from storage and inference time increases. To address these concerns, we explore attribute-based CTG in a prompt-based manner. In short, the proposed Tailor represents each attribute as a pre-trained continuous vector (i.e., single-attribute prompt) and guides the generation of a fixed PLM switch to a pre-specified attribute. We experimentally find that these prompts can be simply concatenated as a whole to multi-attribute CTG without any re-training, yet raises problems of fluency decrease and position sensitivity. To this end, Tailor provides a multi-attribute prompt mask and a re-indexing position-ids sequence to bridge the gap between the training (one prompt for each task) and testing stage (concatenating more than one prompt). To further enhance such single-attribute prompt combinations, Tailor also introduces a trainable prompt connector, which can be concatenated with any two single-attribute prompts to multi-attribute text generation. Experiments on 11 attribute-specific generation tasks demonstrate strong performances of Tailor on both single-attribute and multi-attribute CTG, with 0.08\% training parameters of a GPT-2.
Gen “Z” and Tourism Destination: A Tourism Perspective of Augmented Reality Gaming Technology

In contemporary Tourism industry, DMOs are necessary to adopt and offer innovative experience in order to attract contemporary tourists. Gamification, combined with Augmented Reality and all the relevant to this technology innovations are examined in this paper. Efforts should rely on the demands and needs of generation Z tourists as up and coming generation. However, there are specific implications such as the use of augmented reality smart glasses, incentives and personal data protection that occur. This paper contributes in understanding the new Generation called “Z”, under the light of tourism and the effective use of augmented reality for tourism purposes, combined in one travel-product experience.
Case method in quasi-professional training of prospective teachers

In the study, the technology of work with cases in quasi-professional training of prospective teachers was designed and the results of academic progress were monitored. The designed technology of work on cases was based on the principles of the competent education. The diagnostic methods were chosen in accordance with the goals of the study experiment that allowed monitoring the dynamics of the researched qualities. The expert evaluation was based on the developed scale of 10 in accordance with the cognitive levels: knowledge, understanding, application, analysis, synthesis and evaluation. The experiment has proved that forming and control the competencies with the help of designed technology are more productive that allows making a conclusion about its productivity as well as the effectiveness of created pedagogical conditions. The technology of work with cases in quasi-professional training of prospective teachers includes methods and forms of studying, the description of students’ and teacher’s activity, and has the following components: a target, tasks, the organizational and pedagogical conditions, the informative component, the diagnostics and control, the structure of interaction, the technological component, and the effectiveness. The realization of case technology can be presented at three stages: the preparatory stage, the executive stage, the reflexive stages, which are described by the target, methods and forms, the students’ activity, forming competencies, the teacher’s activity, and control of results. The experimental study of implementing technology of using the case method in the educational process was conducted on the basis of Vinnytsia Mykhailo Kotsiubynsky State Pedagogical University among pre-service teachers (96 participants) during two semesters while studying the course of Pedagogics.
Devising a method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm

This paper considers the improved method for segmenting complex structured images acquired from space observation systems based on the particle swarm algorithm. Unlike known ones, the method for segmenting complex structured images based on the particle swarm algorithm involves the following: – highlighting brightness channels in the Red-Green-Blue color space; – using a particle swarm method in the image in each channel of brightness of the RGB color space; – image segmentation is reduced to calculating the objective function, moving speed, and a new location for each swarm particle in the image in each RGB color space brightness channel. Experimental studies have been conducted on the segmentation of a complex structured image by a method based on the particle swarm algorithm. It was established that the improved segmentation method based on the particle swarm algorithm makes it possible to segment complex structured images acquired from space surveillance systems. A comparison of the quality of segmenting a complex structured image was carried out. The comparative visual analysis of well-known and improved segmentation methods indicates the following: – the improved segmentation method based on the particle swarm algorithm highlights more objects of interest (objects of military equipment); – the well-known k-means method assigns some objects of interest (especially those partially covered with snow) to the snow cover (marked in blue); – the improved segmentation method also associates some objects of interest that are almost completely covered with snow with the snow cover (marked in blue). It has been established that the improved segmentation method based on the particle swarm algorithm reduces segmentation errors of the first kind by an average of 12 % and reduces segmentation errors of the second kind by an average of 8 %
Learning To Retrieve Prompts for In-Context Learning

In-context learning is a recent paradigm in natural language understanding, where a large pre-trained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without any update to its parameters. However, performance has been shown to strongly depend on the selected training examples (termed prompt). In this work, we propose an efficient method for retrieving prompts for in-context learning using annotated data and a LM. Given an input-output pair, we estimate the probability of the output given the input and a candidate training example as the prompt, and label training examples as positive or negative based on this probability. We then train an efficient dense retriever from this data, which is used to retrieve training examples as prompts at test time. We evaluate our approach on three sequence-to-sequence tasks where language utterances are mapped to meaning representations, and find that it substantially outperforms prior work and multiple baselines across the board.
IMPROVING STUDENTS’ PRONUNCIATION SKILL USING ELSA SPEAK APPLICATION

The purpose of this research is to Introduce the ELSA Speak Application as media to improve students' pronunciation skills. English Language Speech Assistant is an acronym for ELSA. English Language Speech Assistant can be downloaded for free from AppStore or Google Play. complete with various features to improve the pronunciation and speaking of students with American accents by training them with various exercises to pronounce words/phrases/sentences correctly. Corresponding to the microphone icon that the learner can use directly to speak as the audio has been listened to.This research is an Action Research that aims to Improve students’ pronunciation skills using ELSA Speak Application. This research discusses how ELSA Speak Application as learning media can improve students’ pronunciation skills. The data were collected through a test of pronunciation and Interviews in Classroom action research. The researcher made three cycles and gave a score for each cycle. In the first cycle, the students’ Average score is 70 points, the second cycle is 75 points and the last cycle is 80 points. ELSA Speak application helps students pronounce a variety of words more easily and comprehensively. The results showed that the use of the ELSA Speak Application has provided convenience and benefits for students in improving their English Pronunciation skills by using the ELSA Speak application.