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Context and motivation: AI-based systems (i.e., systems integrating some AI model or component) are becoming pervasive in society. A number of characteristics of AI-based systems challenge classical requirements engineering (RE) and raise questions yet to be answered. Question: This vision paper inquires the role that RE should play in the development of AI-based systems with a focus on three areas: roles involved, requirements’ scope and non-functional requirements. Principal Ideas: The paper builds upon the vision that RE shall become the cornerstone in AI-based system development and proposes some initial ideas and roadmap for these three areas. Contribution: Our vision is a step towards clarifying the role of RE in the context of AI-based systems development. The different research lines outlined in the paper call for further research in this area. This paper is part of the project TED2021-130923B-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenera-tionEU”/PRTR.

Objective: To summarize significant research contributions on addressing bias, equity, and literacy in health delivery systems published in 2021. Methods: An extensive search using PubMed and Scopus was conducted to identify peer-reviewed articles published in 2021 that examined ways that informatics methods, approaches, and tools could address bias, equity, and literacy in health systems and care delivery processes. The selection process comprised three steps: (1) 15 candidate best papers were first selected by the two section editors; (2) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (3) the final selection of three best papers was conducted by the editorial committee of the Yearbook. Results: Selected best papers represent studies that characterized significant challenges facing biomedical informatics with respect to equity and practices that support equity and literacy in the design of health information systems. Selected papers represent the full spectrum of this year’s yearbook theme. In general, papers identified in the search fell into one of the following categories: (1) descriptive accounts of algorithmic bias in medical software or machine learning approaches; (2) enabling health information systems to appropriately encode for gender identity and sex; (3) approaches to support health literacy among individuals who interact with information systems and mobile applications; and (4) approaches to engage diverse populations in the use of health information systems and the biomedical informatics workforce Conclusions: Although the selected papers are notable, our collective efforts as a biomedical informatics community to address equity, literacy, and bias remain nascent. More work is needed to ensure health information systems are just in their use of advanced computing approaches and all persons have equal access to health care and informatics tools.

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers’ abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations.

The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization (DP). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose DP while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-point values with the use of a computationally faster surrogate model with high accuracy and low offset.

This article reports a simple and inexpensive leak-proof paper pad with an initial selection of a paper substrate on the grounds of surface morphology and fluid absorption time. Herein, a drying method is used for glucose detection on a paper pad through colorimetric analysis, and the spot detection of glucose is analyzed by optimizing the HRP concentration and volume to obtain accurate results. The rapid colorimetric method for the detection of glucose on the paper pad was developed with a limit of detection (LOD) of 2.92 mmol L−1. Furthermore, the effects of the detection conditions were investigated and discussed comprehensively with the help of chemometric methods. Paper pads were developed for glucose detection with a range of 0.5–20 mM (apropos to the normal glucose level in the human body) and 0.1–0.5 M (to test the excessive intake of glucose). The developed concept has huge potential in the healthcare sector, and its extension could be envisioned to develop the reported paper pad as a point-of-care testing device for the initial screening of a variety of diseases.

Source codeused for "Comparison of deep and shallow graph representation learning algorithms for detecting modules in molecular networks" paper

The clinical spectrum of atrial fibrillation means that a patient-individualised approach is required to ensure optimal treatment. Cardiac computed tomography can accurately delineate atrial structure and function and could contribute to a personalised care pathway for atrial fibrillation patients. The imaging modality offers excellent spatial resolution and has been utilised in pre-, peri- and post-procedural care for patients with atrial fibrillation. Advances in temporal resolution, acquisition times and analysis techniques suggest potential expanding roles for cardiac computed tomography in the future management of patients with atrial fibrillation. The aim of the current review is to discuss the use of cardiac computed tomography in atrial fibrillation in pre-, peri- and post-procedural settings. Potential future applications of cardiac computed tomography including atrial wall thickness assessment and epicardial fat volume quantification are discussed together with emerging analysis techniques including computational modelling and machine learning with attention paid to how these developments may contribute to a personalised approach to atrial fibrillation management.

Datasets used for "Benchmarking graph representation learning algorithms for detecting modules in molecular networks"

Phenotypic plasticity, the ability to produce multiple phenotypes from a single genotype, represents an excellent model with which to examine the relationship between gene expression and phenotypes. Analyses of the molecular foundations of phenotypic plasticity are challenging, however, especially in the case of complex social phenotypes. Here we apply a machine learning approach to tackle this challenge by analyzing individual-level gene expression profiles of Polistes dominula paper wasps following the loss of a queen. We find that caste-associated gene expression profiles respond strongly to queen loss, and that this change is partly explained by attributes such as age but occurs even in individuals that appear phenotypically unaffected. These results demonstrate that large changes in gene expression may occur in the absence of outwardly detectable phenotypic changes, resulting here in a socially mediated de-differentiation of individuals at the transcriptomic level but not at the levels of ovarian development or behavior., Connecting genotypes to complex social behaviour is challenging. Taylor et al. use machine learning to show a strong response of caste-associated gene expression to queen loss, wherein individual wasp’s expression profiles become intermediate between queen and worker states, even in the absence of behavioural changes.

Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with the naked eye. Herein, we demonstrate that a machine learning (ML)-based paper sensor platform accurately determines the color changes. We fabricated a colorimetric paper sensor by adsorbing polyaniline nanoparticles (PAni-NPs), whose color changes from blue to green when the ambient pH decreases. Adding glucose oxidase (GOx) to the paper sensor enables colorimetric glucose detection. Target analytes (10 μL) were aliquoted onto the paper sensors, and their images were taken with a smartphone under the same conditions in a darkroom. The red-green-blue (RGB) data from the images were extracted and used to train and test three regression models: support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR). Of the three regression models, RFR performed the best at estimating pH levels (

Recovered paper is the most important raw material for paper industry today and paper industry is one of the forerunners of circular economy. However, despite the long history with recycling the characterization of recovered paper quality is still not solved sufficiently. Here the automatic classification of paper objects can bring significant progress. Up to now, most technical approaches use NIR sensors and visual cameras, which can only “see” the surface of the objects. This often leads to misclassifications, since, for example, many packages look like graphic papers due to their printed surface. To improve these challenging classification tasks, sensors for mechanical properties of the paper objects can make an important contribution. In this paper, an automatic sensor is presented which can measure force characteristics when penetrating paper objects with specially shaped measuring tips. These force characteristics show good correlations to the mechanical properties determined in the laboratory according to standardized methods.

Objectives: In this synopsis, we identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2021. Methods: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and imaging informatics. Except for the sensor section, we only consider papers that have been published in journals providing at least three articles in the query response. Using a three-point Likert scale (1=not include, 2=maybe include, and 3=include), we reviewed the titles and abstracts of all database returns. Only those papers which reached two times three points were further considered for full paper review using the same Likert scale. Again, we only considered works with two times three points and provided these for external reviews. Based on the external reviews, we selected three best papers, as it happens that the three highest ranked papers represent works from all three parts of this section: sensors, signals, and imaging informatics. Results: The search for papers was executed in January 2022. After removing duplicates and conference proceedings, the query returned a set of 88, 376, and 871 papers for sensors, signals, and imaging informatics, respectively. For signals and images, we filtered out journals that had less than three papers in the query results, reducing the number of papers to 215 and 512, respectively. From this total of 815 papers, the section co-editors identified 35 candidate papers with two times three Likert points, from which nine candidate best papers were nominated after full paper assessment. At least three external reviewers then rated the remaining papers and the three best-ranked papers were selected using the composite rating of all external reviewers. By accident, these three papers represent each of the three fields of sensor, signal, and imaging informatics. They were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Deep and machine learning techniques are still a dominant topic as well as concepts beyond the state-of-the-art. Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intense research. Current research focuses on creating and processing heterogeneous sensor data towards meaningful decision support in clinical settings.

Objectives: To identify and summarize the top bioinformatics and translational informatics papers published in 2021 for the IMIA Yearbook. Methods: We performed a broad literature search to retrieve Bioinformatics and Translational Informatics (BTI) papers and coupled this with a series of editorial and peer reviews to identity the top papers in the area. Results: We identified a final candidate list of 15 BTI papers for peer-review; from these candidates, the top three papers were chosen to highlight in this synopsis. These papers expand the integration of multi-omics data with electronic health records and use advanced machine learning approaches to tailor models to individual patients. In addition, our honorable mention paper foreshadows the growing impact of BTI research on precision medicine through the continued development of large clinical consortia. Conclusion: In the top BTI papers this year, we observed several important trends, including the use of deep-learning approaches to analyse diverse data types, the development of integrative and web-accessible bioinformatics pipelines, and a continued focus on the power of individual genome sequencing for precision health.

Printing industry rapidly is adopting digital technologies and the requirements in terms of speed and print quality are also becoming more demanding. The is a wide range of possible quality defects in printed paper. This makes it impossible to have humans inspect the printed paper for such a big amount of possible quality defects at the high-speeds the printouts are produced. Printing industry is not taking advantage of the Artificial Intelligence to detect defects in printed paper at speed without human intervention. It is possible to generate millions of images (captures) with printed content from a printing system every day. Most of these images will not have any defect but some other will and can be used to generate a data set to be used in a machine learning system. The intention of this research work is to find ways artificial intelligence can help on automatically detecting defects on printed paper in a printing system and classifying them, without human intervention. Focusing on scratches, I've explored what are the actual proposals and solutions, and how machine learning can help improving them by using datasets with different techniques, implementing possible solutions and comparing the obtained results. La industria de la impresión está adoptando rápidamente las tecnologías digitales y los requisitos en términos de velocidad y calidad de impresión también son cada vez más exigentes. Existe una amplia gama de posibles defectos de calidad en el papel impreso. Esto hace imposible que los humanos inspeccionen el papel impreso para detectar una cantidad tan grande de posibles defectos de calidad a las altas velocidades a las que se producen las impresiones. La industria de la impresión no está aprovechando la Inteligencia Artificial para detectar defectos en el papel impreso a velocidad sin intervención humana. Es posible generar millones de imágenes (capturas) con contenido impreso desde un sistema de impresión cada día. La mayoría de estas imágenes no tendrán ningún defecto, pero otras sí y pueden ser utilizadas para generar un conjunto de datos que se utilizarán en un sistema de aprendizaje automático. La intención de este trabajo de investigación es encontrar formas en que la inteligencia artificial pueda ayudar a detectar automáticamente los defectos en el papel impreso en un sistema de impresión y clasificarlos, sin intervención humana. Centrándome en los arañazos, he explorado cuáles son las propuestas y soluciones actuales, y cómo el aprendizaje automático puede ayudar a mejorarlas utilizando conjuntos de datos con diferentes técnicas, implementando las posibles soluciones y comparando los resultados obtenidos. La indústria de la impressió està adoptant ràpidament les tecnologies digitals i els requisits en termes de velocitat i qualitat d'impressió també són cada vegada més exigents. Existeix una àmplia gamma de possibles defectes de qualitat en el paper imprès. Això fa impossible que els humans inspeccionin el paper imprès per a detectar una quantitat tan gran de possibles defectes de qualitat a les altes velocitats a les quals es produeixen les impressions. La indústria de la impressió no està aprofitant la Intel·ligència Artificial per a detectar defectes en el paper imprès a velocitat sense intervenció humana. És possible generar milions d'imatges (captures) amb contingut imprès des d'un sistema d'impressió cada dia. La majoria d'aquestes imatges no tindran cap defecte, però altres sí i poden ser utilitzades per a generar un conjunt de dades que s'utilitzaran en un sistema d'aprenentatge automàtic. La intenció d'aquest treball de recerca és trobar formes en què la intel·ligència artificial pugui ajudar a detectar automàticament els defectes en el paper imprès en un sistema d'impressió i classificar-los, sense intervenció humana. Centrant-me en les esgarrapades, he explorat quines són les propostes i solucions actuals, i com l'aprenentatge automàtic pot ajudar a millorar-les utilitzant conjunts de dades amb diferents tècniques, implementant les possibles solucions i comparant els resultats obtinguts.

Microfluidic paper-based analytical devices (μPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, μPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer μPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of μPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in μPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of μPADs.

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems., SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems, ISBN:978-1-4503-9529-8

Summary Objective: To identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2020. Method: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and image informatics. We only considered papers that have been published in journals providing at least three articles in the query response. Section editors then independently reviewed the titles and abstracts of preselected papers assessed on a three-point Likert scale. Papers were rated from 1 (do not include) to 3 (should be included) for each topical area (sensors, signals, and imaging informatics) and those with an average score of 2 or above were subsequently read and assessed again by two of the three co-editors. Finally, the top 14 papers with the highest combined scores were considered based on consensus. Results: The search for papers was executed in January 2021. After removing duplicates and conference proceedings, the query returned a set of 101, 193, and 529 papers for sensors, signals, and imaging informatics, respectively. We filtered out journals that had less than three papers in the query results, reducing the number of papers to 41, 117, and 333, respectively. From these, the co-editors identified 22 candidate papers with more than 2 Likert points on average, from which 14 candidate best papers were nominated after intensive discussion. At least five external reviewers then rated the remaining papers. The four finalist papers were found using the composite rating of all external reviewers. These best papers were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions. Sensors, signals, and imaging informatics is a dynamic field of intense research. The four best papers represent advanced approaches for combining, processing, modeling, and analyzing heterogeneous sensor and imaging data. The selected papers demonstrate the combination and fusion of multiple sensors and sensor networks using electrocardiogram (ECG), electroencephalogram (EEG), or photoplethysmogram (PPG) with advanced data processing, deep and machine learning techniques, and present image processing modalities beyond state-of-the-art that significantly support and further improve medical decision making.

The clinical importance of blood group (BG) antigens is related to their ability to induce immune antibodies that can cause hemolysis. Yet, ABO and D (Rh) are still considered to be the key antigens for healthy blood transfusion and secondary antigens are the next priority. Serological typing is the most widely used typing method. Rapid and accurate blood grouping plays an important role in some clinical conditions, rather than conventional techniques. Hence, developing a simple and economical model for rapid blood grouping would facilitate these tests. In recent decades, paper-based microfluidics such as μPADs has gained much interest in wide application areas such as point-of-care diagnostic. In this study, we evaluated μPADs that are performed for blood grouping and its recent progress. A comprehensive literature search was performed using databases including PUBMED, SCOPUS, Web of Science and Google Scholar. Keywords were blood grouping or typing, paper analytical device, rapid test, etc. After investigation of search results, 16 papers from 2010 to 2020 were included. Further information in detail was classified in Table 1. Generally, two principles for blood typing μPADs are introduced. The lateral chromatographic flow method and the vertical flow-through method that detects BG in a visual-based manner. To detect results with acceptable clarity many factors and challenges like paper, blood sample, buffer, Ab and RBC interaction and also μPADs stability need to be considered, which are discussed. In conclusion, the simplicity, stability, cheapness, portability and biocompatibility of μPADs for blood grouping confirming its utility and also they have the capability to robust, universal blood-grouping platform. Table 1 Summary of blood grouping tests using paper-based analytical devices Antigens Type of diagnosis Validation method Sample No Accuracy Action time Paper type Stability Sample dilution Buffer Ref A, B, Rh Forward volunteers records 5 - - Whatman No. 4 - 1/2 PBS* (Khan et al. 2010) A, B, Rh Forward gel assay test and conventional slide test 100 100% 1 min Whatman No. 4 and Kleeenex paper towel 7 Days in 4 °C 1/1 NSS (Al-Tamimi et al. 2012) A, B, Rh Forward gel card assay 99 100% 20 Sec + Washing Kleeenex paper towel - 1/1 NSS (Li et al. 2012) A, B, Rh Forward - - - - Kleeenex paper towel - 45/100 PSS (Li et al. 2013) A, B, Rh Forward gel card assay 98 100% 1.5 min Kleeenex paper towel - 85/100 PBS (Guan et al. 2014b) C, E, c, e, K, Jka, Jkb, M, N, S, P1, and Lea Forward gel card assay 266 100% - Kleeenex paper towel - 1/1 NSS (Li et al. 2014b) A, B, Rh Forward and Reverse conventional slide test 96 ≈ 91% 10 min Whatman No. 1 21 Days in 4 °C 1/2 NSS (Noiphung et al. 2015) C, c, E, e, K, k, Fya, Fyb, Jka, Jkb, M, N, S and s, P1, Lea and Leb Forward - 478 - - Kleeenex paper towel - 1/1 NSS, PBS (Then et al. 2015) A, B Forward and Reverse conventional slide test 76 100% 5-8 min Whatman No. 4 38 Days in 4 °C 1/4, 1/1 NSS (Songjaroen and Laiwattanapaisal 2016) D, K Forward volunteers records 210 - 7.5 min Kleenex paper towel - 1/1 NSS (Yeow et al. 2016) A, B, c, e, D, C, E, M, N, S, s, P1, Jka, Jkb, Lea, Leb, Fya, and Fyb Forward and Reverse gel card assay 3550 ≈100% 30 s Fiber glass and cotton linter 180 Days in 25 °C 45/100, 1/1 PBS (Zhang et al. 2017) A, B Forward conventional slide test 598 100% 3 min Whatman No. 113 14 Day in 4 °C 1/1 NSS (Songjaroen et al. 2018) A, B, Rh Forward conventional slide test - - 30 Sec + Washing Unrefined sisal paper - 1/2 NSS (Casals-Terré et al. 2019) A, B, Rh Forward - - - - Whatman No.1 - 1/1 NSS (Ansari et al. 2020) ABORh Forward and Reverse conventional slide test - 100% Unrefined Eucalyptus papers - 1/2 NSS, PBS (Casals-Terré et al. 2020) A, B, Rh Forward - - - 30 Sec + Washing Whatman No. 4 modified with chitosan ≥ 100 days in 25 °C 1/1 NSS (Parween et al. 2020)

Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.

Due to the increasing use of the Internet and other online resources, there is tremendous growth in the data of text documents. It is not possible to manage this huge data manually. This has led to the growth of fields like text mining and text summarization. This paper presents a title prediction model for research papers using Recursive Recurrent Neural Network (Recursive RNN) and evaluates its performance by comparing it with sequence-to-sequence models. Research papers published between 2018 and 2020 were obtained from a standard repository, viz. Kaggle, to train the title prediction model. The performance of different versions of Recursive RNN and Seq2Seq was evaluated in terms of training and hold-out loss. The experimental results show that Recursive RNN models perform significantly better than the other models.

International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.

Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

This code repository includes the codes used in the following paper: "Using Deep Learning to Investigate the Public’s Emotional Responses to Work from Home During the COVID-19 Pandemic"

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter–electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.

Cancer is characterized as a heterogeneous disease of various types. The early detection and prognosis of a cancer type have turned into a major requirement, as it facilitates successive medical treatment of patients. The research team has classified the cancer patients into high or low-risk groups. This makes it a significant task for the medical teams to study the application of deep learning and machine learning models. As a result, such techniques have been employed for modeling the development and treatment of cancer conditions. Additionally, the machine learning tools can have the ability the significant detection features from complex datasets. Numerous techniques like Support Vector Machines (SVM), Bayesian Networks (BN), Decision Trees (DT), Artificial Neural Networks (ANN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) has been broadly utilized in cancer research. As per the current survey, the detection rate is about 99.89%, which shows the prediction models' efficiency and precise decision making. However, it is proven that deep learning and machine learning approaches can enhance cancer progression. An adequate level of estimation is required for such approaches for considering the daily medical practice. This survey analyzes and learns the diverse contributions of cancer prediction models using intelligent approaches. Further, the paper tries to categorize the different algorithms, the utilized datasets, and utilized environments. Along with this, various performance measures evaluated in each contribution is sorted out. An extensive search is conducted relevant to machine learning and deep learning methods in cancer susceptibility, recurrence, and survivability prediction, and the existing challenges in this area are clearly described. However, ML models are still in the testing as well as the experimentation phase for cancer prognoses. As the datasets are getting larger with higher quality, researchers are building increasingly accurate models. Moreover, ML models have a long way to go, and most of the models still lack sufficient data and suffer from bias.

Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in health research. The purpose of this methods paper is to describe three machine learning algorithms in detail and provide an example of their use in maternal outcomes research. The three algorithms, classification and regression trees, least absolute shrinkage and selection operator, and random forest, may be used to understand risk groups, select variables for a model, and rank variables’ contribution to an outcome, respectively. While machine learning has plenty to contribute to health research, it also has some drawbacks, and these are discussed as well. In order to provide an example of the different algorithms’ function, they were used on a completed cross-sectional study examining the association of oxytocin total dose exposure with primary cesarean section. The results of the algorithms are compared to what was done or found using more traditional methods.

Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91–95%) strain-specific pathogen identification and quantification capabilities. The trained PCA–NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps. Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. By integrating paper chromogenic arrays (PCAs) and machine learning, a system was developed to automatically recognize PCA patterns on multiplexed viable pathogens with strain-level specificity.

Thermal papers, commonly used for printed receipts or lottery tickets, are omnipresent in our everyday life. They are regarded as semi-porous substrates, and yet can be critical to analyze when looking for latent fingermarks due to their thermosensibility. The aim of this study was to investigate a development sequence that would better combine the adequate detection techniques in order to maximize the chances to develop latent fingermarks left on these substrates. Different methods of development have been compared on test substrates: black magnetic powder, Lumicyano™, thermal development, ninhydrin and 1,2-indanedione/ZnCl2. Whitening stages and thermal development have been focused on, tested and optimized. The results of these preliminary tests enabled the study of three development sequences. They have subsequently been compared to the one currently used in the Gendarmerie's laboratories and the best results have been provided during pseudo-operational comparative trials by one of these sequences, consisting in 6 stages.

The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all.

Paper-based digital microfluidic biochips (or P-DMFBs) are becoming highly impelling due to its low-cost and in-place fabrication of electrodes and control wiring on a single piece of paper having an inkjet printer and conductive ink. Despite enormous advantages, several complex design rules also subsist, such as avoidance of induced control interference, minimum separation among the control lines, and congestion-free wiring on a single layer, which is to be correlated leading toward overall feasibility of the design. Several cost raising issues, such as schedule length, control pin count, and wire length, must be considered for attaining a successful fluid-control codesign. Moreover, design gaps exist among the subtasks of the fluid level, control level, and fluid-control design as a whole, which undeniably impose expensive design cycles increasing overall cost. This article builds a machine learning-based model for the pin-constrained P-DMFBs to predict violation in control design beforehand and accordingly guides the fluid-control codesign to tackle important cost-driving issues while attaining congestion- and conflict-free wiring. This model effectively eliminates the design cycles producing a low-cost platform. The predictive model has been evaluated over a balanced data set. Several benchmarks for assessing the performance are studied.

Printing industry rapidly is adopting digital technologies and the requirements in terms of speed and print quality are also becoming more demanding. The is a wide range of possible quality defects in printed paper. This makes it impossible to have humans inspect the printed paper for such a big amount of possible quality defects at the high-speeds the printouts are produced. Printing industry is not taking advantage of the Artificial Intelligence to detect defects in printed paper at speed without human intervention. It is possible to generate millions of images (captures) with printed content from a printing system every day. Most of these images will not have any defect but some other will and can be used to generate a data set to be used in a machine learning system. The intention of this research work is to find ways artificial intelligence can help on automatically detecting defects on printed paper in a printing system and classifying them, without human intervention. Focusing on scratches, I've explored what are the actual proposals and solutions, and how machine learning can help improving them by using datasets with different techniques, implementing possible solutions and comparing the obtained results. La industria de la impresión está adoptando rápidamente las tecnologías digitales y los requisitos en términos de velocidad y calidad de impresión también son cada vez más exigentes. Existe una amplia gama de posibles defectos de calidad en el papel impreso. Esto hace imposible que los humanos inspeccionen el papel impreso para detectar una cantidad tan grande de posibles defectos de calidad a las altas velocidades a las que se producen las impresiones. La industria de la impresión no está aprovechando la Inteligencia Artificial para detectar defectos en el papel impreso a velocidad sin intervención humana. Es posible generar millones de imágenes (capturas) con contenido impreso desde un sistema de impresión cada día. La mayoría de estas imágenes no tendrán ningún defecto, pero otras sí y pueden ser utilizadas para generar un conjunto de datos que se utilizarán en un sistema de aprendizaje automático. La intención de este trabajo de investigación es encontrar formas en que la inteligencia artificial pueda ayudar a detectar automáticamente los defectos en el papel impreso en un sistema de impresión y clasificarlos, sin intervención humana. Centrándome en los arañazos, he explorado cuáles son las propuestas y soluciones actuales, y cómo el aprendizaje automático puede ayudar a mejorarlas utilizando conjuntos de datos con diferentes técnicas, implementando las posibles soluciones y comparando los resultados obtenidos. La indústria de la impressió està adoptant ràpidament les tecnologies digitals i els requisits en termes de velocitat i qualitat d'impressió també són cada vegada més exigents. Existeix una àmplia gamma de possibles defectes de qualitat en el paper imprès. Això fa impossible que els humans inspeccionin el paper imprès per a detectar una quantitat tan gran de possibles defectes de qualitat a les altes velocitats a les quals es produeixen les impressions. La indústria de la impressió no està aprofitant la Intel·ligència Artificial per a detectar defectes en el paper imprès a velocitat sense intervenció humana. És possible generar milions d'imatges (captures) amb contingut imprès des d'un sistema d'impressió cada dia. La majoria d'aquestes imatges no tindran cap defecte, però altres sí i poden ser utilitzades per a generar un conjunt de dades que s'utilitzaran en un sistema d'aprenentatge automàtic. La intenció d'aquest treball de recerca és trobar formes en què la intel·ligència artificial pugui ajudar a detectar automàticament els defectes en el paper imprès en un sistema d'impressió i classificar-los, sense intervenció humana. Centrant-me en les esgarrapades, he explorat quines són les propostes i solucions actuals, i com l'aprenentatge automàtic pot ajudar a millorar-les utilitzant conjunts de dades amb diferents tècniques, implementant les possibles solucions i comparant els resultats obtinguts.

Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an

PLEASE GO TO LATEST VERSION In this report we demonstrate how we made the multi-lingual text classifier to match research papers to the Sustainable Development Goals (SDGs) of the United Nations. We trained the BERT multi-language model to classify the 169 individual SDG Targets, based on the English abstracts in the corpus of 1.4 million research papers. We gathered that data using Scopus with the Aurora SDG Query model v5, which has an evaluated average precision of 70% and recall of 14%. This is a follow-up project of the query based Aurora SDG classification model v5. The purpose of this project is to try and tackle several issues: 1. to label also research output to an SDG that is written in a non-english language, 2. to include papers that use other terms than the exact terms used in keyword searches, 3. to have a classification model that works independent from any other database specific query language. In this report we show how we decided to use the abstracts only and the mBERT model to train the classifier. Also we show why we trained 169 individual models, instead of 1 multi-label model, including the evaluation for prediction. We will show how to prepare the data for training, and how to run the code to train the models on multiple GPU cores. Next we show how to prepare the data for prediction and how to use the code to predict English and non-English texts. And finally we evaluate the model by reviewing a sample of non-English research papers, and provide some tips to increase the reliability of the predicted outcomes. This collection will contain: Report / technical documentation describing the method and evaluating the models. AI_for_mapping_... SDGs_.pdf Text classification models: a table containing the download urls for each of the mBERT models for each SDG-Target and SDG-Goal in .h5 format. SDGs_many_BERTs_models_download_urls.csv (.csv format, semicolon separated) for only SDG-Goals models: https://doi.org/10.5281/zenodo.5835849 Training data sample in .csv format. Containing abstract and columns of SDG-Targets with 1 or 0. train_data_sample_aurora_sdg_v5_worldwide_set_doi_abstracts_sdg_targets_2009-2020-in-columns.csv (.csv format, comma separated) get full data here (1.4 million doi's labeled to SDG Targets): https://doi.org/10.5281/zenodo.5205672 due to licencing agreements with Scopus we are only allowed to share a limited amount of abstracts. Support https://i4oa.org to get more free-to-use abstracts in Crossref. Training code in python. Explaining what parameters we used to train the models on GPU hardware. train.py get / fork code here: https://github.com/Aurora-Network-Global/sdgs_many_berts Test Statistics on accuracy of each of the trained models SDGs_many_BERTs_models_test_statistics.csv (.csv format, tab separated) Prediction data sample. Text file (UTF8) containing abstracts of papers in different languages on each row. predict_data_sample_multiple_language_abstracts.csv (.csv format, list, languages row 2-31: EN, row 32-152: NL ) Prediction code in python. Setup to run the models to classify a text fragment. predict.py get / fork code here: https://github.com/Aurora-Network-Global/sdgs_many_berts, Acknowledgements Many thanks to Maéva Vignes from University of South Denmark, to allow us to use their UCloud HPC facilities and budget to train the mBERT models on their GPU's. Funded by Funded by European Commission, Project ID: 101004013, Call: EAC-A02-2019-1, Programme: EPLUS2020, DG/Agency: EACEA Read more [ Project website | Zenodo Community | Github ] Change log 2022-03-31 | v1.0 | final version of the report 2022-03-31 | v0.8 | added final draft of the report 2022-02-01 | v0.7.2 | changed download urls for SDG 11 to latest version 2 (adding 3 missing target models) in SDGs_many_BERTs_models_download_urls.xslx 2022-02-01 | v0.7.1 | added files SDGs_many_BERTs_models_download_urls.xslx and .csv, I forgot to upload in version 0.7 2022-02-01 | v0.7 | changed files SDGs_many_BERTs_models_download_urls.xslx and .csv to match URLs of new version dataset SDG-Goals-Only models, which now separates files of the models, instead of one big .zip file. 2021-12-06 | v0.6 | added URL to SDG-Goals-Only models in second line of file SDGs_many_BERTs_models_download_urls.csv. 2021-12-06 | v0.5 | added report and documentation. (NEED TO FINISH SECTIONS EVALUATION AND CONCLUSION) 2021-12-06 | v0.4 | added sample data and code for training and for predicting. To reproduce the models yourself and to make use of the trained models yourself. 2021-12-01 | v0.3 | added .csv file with accuracy statistics of the models 2021-11-30 | v0.2 | added .csv file with download urls of the models 2021-10-29 | v0.1 | added initial .md file as placeholder for this dataset

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.

Objective Our aim was to develop an efficient search strategy for prognostic studies and clinical prediction guides (CPGs), optimally balancing sensitivity and precision while independent of MeSH terms, as relying on them may miss the most current literature. Materials and Methods We combined 2 Hedges-based search strategies, modified to remove MeSH terms for overall prognostic studies and CPGs, and ran the search on 269 journals. We read abstracts from a random subset of retrieved references until ≥ 20 per journal were reviewed and classified them as positive when fulfilling standardized quality criteria, thereby assembling a standard dataset used to calibrate the search strategy. We determined performance characteristics of our new search strategy against the Hedges standard and performance characteristics of published search strategies against the standard dataset. Results Our search strategy retrieved 16 089 references from 269 journals during our study period. One hundred fifty-four journals yielded ≥ 20 references and ≥ 1 prognostic study or CPG. Against the Hedges standard, the new search strategy had sensitivity/specificity/precision/accuracy of 84%/80%/2%/80%, respectively. Existing published strategies tested against our standard dataset had sensitivities of 36%–94% and precision of 5%–10%. Discussion We developed a new search strategy to identify overall prognosis studies and CPGs independent of MeSH terms. These studies are important for medical decision-making, as they identify specific populations and individuals who may benefit from interventions. Conclusion Our results may benefit literature surveillance and clinical guideline efforts, as our search strategy performs as well as published search strategies while capturing literature at the time of publication.

Nowadays, Machine Learning (ML) model of the structure-property relation based on large data from reliable physical models becomes a new and promising approach for material design. The present work demonstrates such approach to examine the variation in microstructure features on mechanical properties of paper materials. After the generation of a “big” dataset of fiber network samples, morphological feature data, including interfiber contact properties were extracted and statistically evaluated. By performing cohesive finite element simulations, the mechanical properties including failure strain, effective stiffness, and maximal stress of fiber networks under tensile test were determined and served along with structural feature data for the ML analysis. Gradient Boosting method achieved a performance score of approx. 0.9 for all mechanical properties of such complex fibrous structure. It was found that “disorderness” represented by the variation of fiber network orientation and the mean contact area size to be the most influential factors to the failure strain and effective stiffness. Whereas the failure strength was driven by the homogeneous distribution of the contact areas. The results validated the strong orientation dependence of fibrous materials in experimental observations and enlighten the importance of sensitivity as feature parameters and the striking potential of ML for material optimization.

Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

A method for image retrieval based on supervised local regression and global alignment (sLRGA) with relevance feedback for insect identification is presented in this paper. Based on the novel sLRGA, which is an extended version of LRGA, the proposed method estimates ranking scores for image retrieval in such a way that the neighborhood structure of a feature space of the database can be optimally preserved with consideration of class information. This is the main contribution of this paper. By measuring the relevance between all of the images and the query image in the database, sLRGA realizes accurate image retrieval. Furthermore, when positive/negative labels to retrieved images are given by users, the proposed method can improve image retrieval performance considering the query relevance information via use of both relevance feedback and sLRGA. This is the second contribution of this paper. Experimental results show the effectiveness of the proposed method.

These are the slides for the paper "A Comprehensive Study of k-Portfolios of Recent SAT Solvers", presented at the conference [*SAT 2022*](http://satisfiability.org/SAT22/). You can find the paper [here](https://www.doi.org/10.4230/LIPIcs.SAT.2022.2).

Background: The aim of this study was to investigate near infrared (NIR) spectroscopy ability to estimate nanofibril concentration, physical and mechanical properties of Kraft paper reinforced with cellulose nanofibrils (CNF). For this purpose, paper sheets were prepared by mixing unbleached Eucalyptus fibers and cellulose nanofibrils. Twelve treatments result from the combination of the nominal grammages 75, 85, 95 and 105 g m-² with the nanofibril concentrations of 1, 5 and 10wt%, with 7 sheets per treatment. NIR spectra measured directly on paper specimens were correlated with physical and mechanical properties values obtained through conventional laboratory analyzes. Results: Principal component analysis (PCA) revealed no separation among specimens related to nanofibril content. Partial least squares regression (PLS-R) models for estimating nanofibril content, tensile index, stretch and resistance to air passage yielded R 2cv ranging from 0.73 to 0.98. Partial least squares - discriminant analysis (PLS-DA) correctly classified up to 93% of the paper specimens both by grammage and nanofibril content using NIR spectra. Conclusion: This approach appears to be suitable for predicting physical and mechanical properties of Kraft papers and can detect cellulose nanofibril content in the cellulose handsheets.

SummaryMachine-learning techniques are used in this BJPsych special issue on precision medicine in attempts to create statistical models that make clinically relevant predictions for individual patients. In this primer, we outline five key points that are helpful for a new reader to consider in order to engage with the field and evaluate the literature. These points include the consideration of why we are interested in new statistical approaches, how they may produce individualised predictions, what caveats need to be kept in-mind and why the interest and engagment of clinicians and clinical researchers is critical to successful model development and implementation. We hope that the following primer will provide shared understanding to encourage dialogue between clinical and methodological fields.