Reviewerly/exHarmony huggingface dataset:
Integration of artificial intelligence and precision oncology in Latin America

Next-generation medicine encompasses different concepts related to healthcare models and technological developments. In Latin America and the Caribbean, healthcare systems are quite different between countries, and cancer control is known to be insufficient and inefficient considering socioeconomically discrepancies. Despite advancements in knowledge about the biology of different oncological diseases, the disease remains a challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers. With the development of molecular biology, better diagnosis methods, and therapeutic tools in the last years, artificial intelligence (AI) has become important, because it could improve different clinical scenarios: predicting clinically relevant parameters, cancer diagnosis, cancer research, and accelerating the growth of personalized medicine. The incorporation of AI represents an important challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers in cancer care. Therefore, some studies about AI in Latin America and the Caribbean are being conducted with the aim to improve the performance of AI in those countries. This review introduces AI in cancer care in Latin America and the Caribbean, and the advantages and promising results that it has shown in this socio-demographic context.
Is this question going to be closed? Answering question closibility on Stack Exchange

Community question answering sites (CQAs) are often flooded with questions that are never answered. To cope with the problem, experienced users of Stack Exchange are now allowed to mark newly posted questions as closed if they are of poor quality. Once closed, a question is no longer eligible to receive answers. However, identifying and closing subpar questions takes time. Therefore, the purpose of this article is to develop a supervised machine learning system that predicts question closibility, the possibility of a newly posted question to be eventually closed. Building on extant research on CQA question quality, the supervised machine learning system uses 17 features that were grouped into four categories, namely, asker features, community features, question content features and textual features. The performance of the developed system was tested on questions posted on Stack Exchange from 11 randomly chosen topics. The classification performance was generally promising and outperformed the baseline. Most of the measures of precision, recall, F1-score and area under the receiver operating characteristic curve (AUC) were above 0.90 irrespective of the topic of questions. By conceptualising question closibility, the article extends previous CQA research on question quality. Unlike previous studies, which were mostly limited to programming-related questions from Stack Overflow, this one empirically tests question closibility on questions from 11 randomly selected topics. The set of features used for classification offers a framework of question closibility that is not only more comprehensive but also more parsimonious compared with prior works.
Entanglement-assisted, noise-assisted, and monitoring-enhanced quantum bath tagging

We analyze the capability of discriminating the statistical nature of a thermal bath by exploiting the interaction with an additional environment. We first shows that, at difference with the standard scenario where the additional environment is not present, the modified evolution induced by the mere presence of the extra bath allows to improve the discrimination task. We then also consider the possibility of continuously monitoring the additional environment and we discuss in detail how to obtain improved performances in the discrimination by considering different kinds of interaction, i.e. different jump operators, and different monitoring strategies corresponding to continuous homodyne and photo-detection. Our strategy can be in principle implemented in a circuit QED setup and paves the way to further developments of quantum probing via continuous monitoring.
An advanced W-shaped network with adaptive multi-scale supervision for osteosarcoma segmentation

Osteosarcoma is one of the most common malignant bone tumors in adolescents, hence a precise and reliable automatic segmentation method is urgently needed in clinical practice. In this paper, an advanced W-shaped network is proposed for automatic and accurate segmentation of osteosarcoma in computed tomographic images. This deep model is developed based on two cascaded baseline U-Nets where feature maps of the same scales in encoding and decoding paths of both networks are fused in terms of advanced skip connections. Different from simple skip connections in the traditional U-Net which fuse low-level and high-level feature maps directly, the advanced skip connection module learns fine details from low level feature maps before concatenating to the corresponding high-level feature maps. Multiple side outputs are used to supervise the training process of the network. Multi-scale channel attention module is introduced to enable the network learn to suppress the irrelevant side outputs while highlight the useful ones to osteosarcoma tasks. The performance of our method is evaluated on a home-built dataset containing 2303 computed tomographic images of osteosarcoma whose results show that our method outperforms the U-Net and Multiple Supervised Residual Network with improvements of 7.47% and 2.59% in dice similarity coefficient, respectively. Our method also performs better than our previously developed W-Net++ with an improvement of 1.04% in dice similarity coefficient. • A multi-scale attention block to assign different weights to different side outputs. • An advanced skip connection to extract fine details from the low-level feature map. • A multi-scale fusion strategy for features from different U-Nets in the cascade.
Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, F2-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.
Research on Skeleton Data Compensation of Gymnastics based on Dynamic and Static Two-dimensional Regression using Kinect

Abstract The intelligent training and assessment of gymnastics movements require studying motion trajectory and reconstructing the character animation. Microsoft Kinect has been widely used due to its advantages of low price and high frame rate. However, its optical characteristics are inevitably affected by illumination and occlusion. It is necessary to reduce data noise via specific algorithms. Most of the existing research focuses on local motion but lacks consideration of the whole human skeleton. Based on the analysis of the spatial characteristics of gymnastics and the movement principle of the human body, this paper proposes a dynamic and static two-dimensional regression compensation algorithm. Firstly, the constraint characteristics of human skeleton motion were analyzed, and the maximum constraint table and Mesh Collider were established. Then, the dynamic acceleration of skeleton motion and the spatial characteristics of static limb motion were calculated based on the data of adjacent effective skeleton frames before and after the collision. Finally, using the least squares polynomial fitting to compensate and correct the lost skeleton coordinate data, it realizes the smoothness and rationality of human skeleton animation. The results of two experiments showed that the solution of the skeleton point solved the problem caused by data loss due to the Kinect optical occlusion. The data compensation time of an effective block skeleton point can reach 180 ms, with an average error of about 0.1 mm, which shows a better data compensation effect of motion data acquisition and animation reconstruction.
Ensemble Effort Estimation: An updated and extended systematic literature review

Ensemble Effort Estimation (EEE) techniques combine several individual software estimation methods in order to address the weaknesses of individual methods for prediction tasks. A systematic review published in 2016 analyzed empirical studies on EEE techniques published between 2010 and (January) 2016. The research on EEE has continuously evolved over the past five years (2016–2020), generating new findings that should be aggregated to the existing body of evidence on the subject. The goal of this paper is to update the systematic review from 2016 with new findings from studies published between 2016 (full year) and 2020 (inclusive). To conduct our review update, we followed existing guidelines for updating systematic reviews in software engineering and other fields. We started with an appraisal of the background and methods of the 2016 review, which resulted in the updated review protocol used to conduct our study. We retrieved 3,682 papers using automatic searching techniques, from which we selected 30 papers for data extraction and analysis. Our findings reinforce the results of the previous review in that machine learning is still the technique most common to construct EEE and that the ensemble techniques have outperformed the individual models. We added new evidence showing that there is no clear superiority of an EEE model over the others. Also, we found that ensemble dynamic selection is still little used in Software Effort Estimation (SEE). This review adds new evidence about the use of EEE techniques in software development which reinforces previous findings and also shows research opportunities in constructing more effective EEE. Besides, ensemble dynamic selection appears as a promising area of research which still is underexplored.
Sonic Interactions in Virtual Environments: The Egocentric Audio Perspective of the Digital Twin

Abstract The relationships between the listener, physical world, and virtual environment (VE) should not only inspire the design of natural multimodal interfaces but should be discovered to make sense of the mediating action of VR technologies. This chapter aims to transform an archipelago of studies related to sonic interactions in virtual environments (SIVE) into a research field equipped with a first theoretical framework with an inclusive vision of the challenges to come: the egocentric perspective of the auditory digital twin. In a VE with immersive audio technologies implemented, the role of VR simulations must be enacted by a participatory exploration of sense-making in a network of human and non-human agents, called actors. The guardian of such locus of agency is the auditory digital twin that fosters intra-actions between humans and technology, dynamically and fluidly redefining all those configurations that are crucial for an immersive and coherent experience. The idea of entanglement theory is here mainly declined in an egocentric spatial perspective related to emerging knowledge of the listener’s perceptual capabilities. This is an actively transformative relation with the digital twin potentials to create movement, transparency, and provocative activities in VEs. The chapter contains an original theoretical perspective complemented by several bibliographical references and links to the other book chapters that have contributed significantly to the proposal presented here.
Translator attribution for Arabic using machine learning

Abstract Given a set of target language documents and their translators, the translator attribution task aims at identifying which translator translated which documents. The attribution and the identification of the translator’s style could contribute to fields including translation studies, digital humanities, and forensic linguistics. To conduct this investigation, firstly, we develop a new corpus containing the translations of world-famous books into Arabic. We then pre-process the books in our corpus which mainly involves cleaning irrelevant material, morphological segmentation analysis of words, and devocalization. After pre-processing the books, we propose to use 100 most frequent words and/or morphologically segmented function words as writing style markers of the translators (i.e. stylometric features) to differentiate between translations of different translators. After the completion of features extraction process, we applied several supervised and unsupervised machine-learning algorithms along with our novel cluster-to-author index to perform this task. We found that the translators are not invisible, and morphological analysis may not be more useful than just using the 100 most frequent words as features. The support vector machine linear kernel algorithm reported 99% classification accuracy. Similar findings were reported by the unsupervised machine-learning methods, namely, K-mean clustering and hierarchical clustering.
Generating synthetic mixed discrete-continuous health records with mixed sum-product networks

Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate synthetic data that can be shared for subsequent statistical analysis. This anonymization method was evaluated with respect to privacy and information loss.Using a simulation study, information loss was quantified by assessing whether synthetic data could reproduce regression parameters obtained from the original data. Predictors variable types were varied between continuous, count, categorical, and mixed discrete-continuous. Additionally, we measured whether the MSPN approach successfully anonymizes the data by removing associations between background and sensitive information for these datasets.The synthetic data generated with MSPNs yielded regression results highly similar to those generated with original data, differing less than 5% in most simulation scenarios. Standard errors increased compared to the original data. Particularly for smaller datasets (1000 records), this resulted in a discrepancy between the estimated and empirical standard errors. Sensitive values could no longer be inferred from background information for at least 99% of tested individuals.The proposed anonymization approach yields very promising results. Further research is required to evaluate its performance with other types of data and analyses, and to predict how user parameter choices affect a bias-privacy trade-off.Generating synthetic data from MSPNs is a promising, easy-to-use approach for anonymization of sensitive individual health data that yields informative and private data.
Using spreadsheets as learning tools for neural network simulation

The article supports the need for training techniques for neural network computer simulations in a spreadsheet context. Their use in simulating artificial neural networks is systematically reviewed. The authors distinguish between fundamental methods for addressing the issue of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools for neural network simulation, application of third-party add-ins to spreadsheets, development of macros using embedded languages of spreadsheets, use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins, and On the article, methods for creating neural network models in Google Sheets, a cloud-based spreadsheet, are discussed. The classification of multidimensional data presented in R. A. Fisher's "The Use of Multiple Measurements in Taxonomic Problems" served as the model's primary inspiration. Discussed are various idiosyncrasies of data selection as well as Edgar Anderson's participation in the 1920s and 1930s data preparation and collection. The approach of multi-dimensional data display in the form of an ideograph, created by Anderson and regarded as one of the first effective methods of data visualization, is discussed here.
Global-local Feature Aggregation for Event-based Object Detection on EventKITTI

Event sequence conveys asynchronous pixel-wise visual information in a low power and high temporal resolution manner, which enables more robust perception under challenging conditions, e.g., fast motion. Two main factors limit the development of event-based object detection in traffic scenes: lack of high-quality datasets and effective event-based algorithms. To solve the first problem, we propose a simulated event-based detection dataset named EventKITTI, which incorporates the novel event modality information into a mixed two-level (i.e. object-level and video-level) detection dataset under traffic scenarios. EventKITTI possesses the high-quality event stream and the largest number of categories at microsecond temporal resolution and 1242×375 spatial resolution, exceeding existing datasets. As for the second problem, existing algorithms rely on CNN-based, spiking or graph architectures to capture local features of moving objects, leading to poor performance in objects with incomplete contours. Hence, we propose event-based object detectors named GFA-Net and CGFA-Net. To enhance the global-local learning ability in the spatial dimension, GFA-Net introduces transformer with edge-based position encoding and multi-scale feature fusion to detect objects on static frame. Furthermore, CGFA-Net optimizes edge-based position encoding with close-loop learning based on previous detected heatmap, which aggregates temporal global features across event frames. The proposed event-based object detectors achieve the best speed-accuracy trade-off on EventKITTI, approaching an 81.3% MAP at 33.0 FPS on object-level detection dataset and a 64.5% MAP at 30.3 FPS on video-level detection dataset.
A Benchmark for Vision-based Multi-UAV Multi-object Tracking

Vision-based multi-sensor multi-object tracking is a fundamental task in the applications of a swarm of Unmanned Aerial Vehicles (UAVs). The benchmark datasets are critical to the development of computer vision research since they can provide a fair and principled way to evaluate various approaches and promote the improvement of corresponding algorithms. In recent years, many benchmarks have been created for single-camera single-object tracking, single-camera multi-object detection, and single-camera multi-object tracking scenarios. However, up to the best of our knowledge, few benchmarks of multi-camera multi-object tracking have been provided. In this paper, we build a dataset for multi-UAV multi-object tracking tasks to fill the gap. Several cameras are placed in the VICON motion capture system to simulate the UAV team, and several toy cars are employed to represent ground targets. The first-perspective videos from the cameras, the motion states of the cameras, and the ground truth of the objects are recorded. We also propose a metric to evaluate the performance of the multi-UAV multi-object tracking task. The dataset and the code for algorithm evaluation are available at our GitHub (https://github.com/bitshenwenxiao/MUMO).
Characteristics of Naturally Formed Nanoparticles in Various Media and Their Prospecting Significance in Chaihulanzi Deposit

In recent years, the exploration of concealed deposits has become extremely urgent as the shortage of surface resources worsens. In this study, naturally formed nanoparticles in five media (deep-seated fault gouge, ascending gas flow, soil, shallow groundwater and deep groundwater) in Chaihulanzi Au deposit, China, were analyzed by transmission electron microscopy. The characteristics of category, shape, lattice parameters, chemical component and association were obtained. The results show that deep media can carry natural nanoparticles to the surface media, resulting in an increased proportion of O and metal chemical valence such as Pb and Cu in nanoparticles. The metal elements Au, Ag, Cu, Zn and As in nanoparticles correspond to those of orebody minerals. Au-Ag-Cu, Fe-As, Cu-Sn and Pb-Zn element associations in nanoparticles are similar to those of mineral composition or orebody paragenesis in Chaihulanzi deposit. Compared with nanoparticle characteristics in deposit and background areas, it can be deduced that natural ore-bearing nanoparticles come from concealed orebodies. With the characteristics of more oxide forms and the dislocation of the crystal lattice, these nanoparticles are formed by faulting and oxidation. Nanoparticles produced in concealed orebodies that migrate from the deep to the surface media could be used for prospecting.
Applying Data-Driven-Based Logistic Function and Prediction-Area Plot to Map Mineral Prospectivity in the Qinling Orogenic Belt, Central China

This study combines data-driven-based logistic functions with prediction–area (P–A) plot for delineating target areas of orogenic Au deposits in the eastern margin of the Qinling metallogenic belt, central China. First, appropriate geological and geochemical factors were identified, optimized, and transformed into a series of fuzzy numbers with a range of 0–1 through a data-driven-based logistic function in order to determine the evidence layer for prospecting orogenic Au. In addition, the P–A plot was derived on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme, in the process of which acidic magmatic activity proved to be the most important factor of ore-controlling. Moreover, to further prove the advantages of this method, a traditional linear knowledge-driven approach was carried out for comparative purposes. Finally, based on concentration–area (C–A) fractal theory, the fractal thresholds were determined and a mineral prospecting map was generated. The obtained prediction map consisted of high, medium, low, and weak metallogenic potential areas, accounting for 2.5%, 16.1%, 38.4%, and 43% of the study area, containing 2, 3, 1, and 0 of the 6 known mine occurrences contained, respectively. The P–A plot indicated that the result predicted 83% of Au deposits with 17% of the area, confirming the joint application of the data-driven-based logistic function and P–A plot to be a simple, effective, and low-cost method for mineral prospectivity mapping, that can be a guidance for further work in the study area.