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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.

In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available.

Analysis of images with deep learning and machine learning has recently show more accuracy in medical image processing. The paper review the major machine learning methods is used for the extraction of retinal fundus image. It describes how hardware has been modified according to time for retinal eye segmentation that device is helpful for the environment and also useful for humans The algorithms used for image extraction, clear and noise-free Images of different color scales are used to improve image quality. The paper gives a review of how researchers and scientists have been trying different methods to improve vessel segmentation in the future.

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system, a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical&ndash, physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health, and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO2, mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Free-falling paper shapes exhibit rich, complex and varied behaviours that are extremely challenging to model analytically. Physical experimentation aids in system understanding, but is time-consuming, sensitive to initial conditions and reliant on subjective visual behavioural classification. In this study, robotics, computer vision and machine learning are used to autonomously fabricate, drop, analyse and classify the behaviours of hundreds of shapes. The system is validated by reproducing results for falling discs, which exhibit four falling styles: tumbling, chaotic, steady and periodic. A previously determined mapping from a non-dimensional parameter space to behaviour groups is shown to be consistent with these new experiments for tumbling and chaotic behaviours. However, steady or periodic behaviours are observed in previously unseen areas of the parameter space. More complex hexagon, square and cross shapes are investigated, showing that the non-dimensional parameter space generalizes to these shapes. The system highlights the potential of robotics for the investigation of complex physical systems, of which falling paper is one example, and provides a template for future investigation of such systems. The dynamics of paper shapes in free fall are still not fully understood, despite being discussed for more than 150 years. Collecting large amounts of data has the potential to give us new insights and a robotics system could generate and analyse data in large quantities.

In this paper, we investigate the fast downlink beamforming for full-dimension multiple-input multiple-output systems under correlated Rician channels. Under the assumption that the base station (BS) has only statistical channel state information (CSI), we decouple each user’s beamforming vector and derive their optimal beamforming vector through the maximization of the average signal-to-leakage-plus-noise ratio (SLNR) lower bound. Then, to reduce the computation time, a model-driven deep learning (DL)-based beamforming algorithm is proposed, as well as a data-driven algoriothm for comparison. In the model-driven DL-based beamforming algorithm, the process of obtaining the beamforming vector is separated into two parallel neural networks which are constructed and trained independently. The proposed algorithms can achieve similar ergodic rate as the optimal beamforming algorithm with much less computation time, and the model-driven algorithm requires less computing resource than the data-driven algorithm.

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper’s ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies.

Recycling wastewater of the pulping and paper-making industry are widely considered for clean production, which heavily rely on the timely and accurate monitoring in paper-making wastewater treatment processes. A novel predicting model based on genetic-deep belief network algorithm was proposed to improve the predictive accuracy and reliability for process monitoring. Considering the deep belief networks (DBN) as a deep learning model is aiming to describe the relationship among variables in a complex process modeling, genetic algorithm (GA) was employed to reduce the input variables dimensionality, simplify the network structure and overcome the dynamic characteristic difficulties of process data in monitoring. Compared with DBN and back propagation neural network (BPNN), the GA-DBN effectively achieved a better predictive accuracy than other tests models in complex wastewater treatment processes. The value of the coefficient of determination of GA-DBN model is increased by 1.71–1.86% and 5.21–9.32%, respectively. The GA-DBN model can be structured with fewer variables or samples to describe the complex paper-making wastewater treatment process, obtaining the better model performance and predictive accuracy.

With today’s eye-tracking technologies, it is possible to analyze cognitive processes such as reading. There are many studies on that topic. We have studied some of these studies that analyze eye movements recording during reading by various machine learning algorithms.

This paper has a brief introduction to a algorithm that can automatically identify the defective cutting region when automatic paper cutter does quality analysis. The algorithm analyzed the characteristics of processed shear mark image,detected defects,found a critical point of defective part ,then a unique regional recognition algorithm was developed based on the basis of the general processing algorithm. The algorithms can pinpoint the region that cutting defects exist according to the coordinates of critical point accurately, improve the efficiency of quality analysis, and also has stated that image processing is available in the regional recognition of automatic paper cutter.

According to the features of paper currency CIS image, the fuzzy set theory is initial used to improve the contrast of banknote target and background, paper money’s edge pixels are obtained by scanning, and then the sub-pixel edges are fitted by least square method; in the fitting process, the error threshold method is applied for eliminating noisy edge points, then slope correction of paper money is carried out according to the angle of sub-pixel edge line and the note area is extracted, finally, fuzzy enhancement is used for the extracted image again. Experiments show that, this method has good versatility, high accuracy and strong anti-interference ability, it can even extract the paper currency area effectively in the edge deterioration and unfilled corner condition, and the enhanced image is in favor of subsequent paper currency recognition.

Based on the analysis of the types and characteristics of Chinese traditional paper-cut patterns, we propose a new algorithm for extracting and vectoring the patterns. On the contour extraction, the algorithm uses a grayscale, binary, contour tracking method. On the vectorization, we improved the segment Displaced curve segments so that you can make the final contour paper cutting pattern vector into a control point and a curve. Using this algorithm, we can quickly get the vector contour of the pattern of manual cutting. Further, we can carry on the processing, such as filling, deformation, combination, animation control, so as to complete the core function of the paper cutting animation software. The algorithm is also suitable for the extraction and the vector of common bitmap image, although the algorithm is put forward for the particularity of the paper cutting patterns.

We use image enhancement algorithms to optimize the perception of automotive HMI for bright ambient light. Our “ΔL/L” tone mapping with color rendering enables easy integration of display parameters and was evaluated best by subjects. Various displays were examined and measured for comparison of enhancement methods incl. parameters and perception by subjects.

In this paper, two novel techniques developed by the authors to measure certain quality variables in paper manufacturing are described. These methods are based on the new online image sensors for digital image processing of paper samples. The techniques use the two dimensional Fourier transform of the original images to estimate basically two variables: the floc size distribution in paper formation and creping of tissue paper. In the first case, a new method to estimate the floc size distribution in paper is presented. The method computes only a reduced portion of the spectrum to determine, in frequency, the floc sizes present. The technique to measure creping gives the degree of roughness that has been induced on the tissue paper by looking also at its power spectra distribution. This characteristic is essential in determining the smoothness of tissue paper. >

Online examination system is built on internet applications,aimed at achieving information management of examinations. Online examination system in the generation of test papers, the submission and approval can be done automatically on the network. We proposes a new heuristic genetic test paper algorithm, and the proposed algorithm is applied to the online examination system. From the experimental results,the improved algorithm has achieved satisfactory performance.

Although advantages of traditional Hough transform of such Good robustness and anti-interference have been found in the process of detection of complex paper diseases such as Streak, fold, however, quantity calculation has imposed limitation on the application of this method in dealing process demanding high instantaneity. Probabilistic Hough Transform fits in a straight line by approach of Hough transform. The approach of selecting fitting points by random drawing and combination with meter counter and output a boundary line where random drawing points locates on condition that such points satisfy limitation of threshold value which has been pre-setted is able to reduce the calculation and period of algorithm operation therefore fitts detection of complex paper diseases demanding high instantaneity.

We illustrate the Efficient Forward Filtering Backward Smoothing (EFFBS) algorithm proposed by Khreich et al. (2010) for estimation of hidden Markov models. The algorithm is aimed at reducing the amount of memory required by the Baum-Welch recursions, while having the same complexity in terms of number of operations. In implementing the EFFBS algorithm we found a numerical problem that limits its applicability. We discuss this problem in detail, providing some possible explanations of the causes of the error, together with two illustrative examples.

Algorithms are based on some influential principles like Survival of the Fittest and with some natural phenomena in Genetic Inheritance. The key for searching the solution in improved function optimization problems are based only on Selection and Mutation operators. This paper reflects on Survival selection schemes specifically like Truncate Selection, Proportionate Selection, Tournament Selection and Ranking Based Selection. In this paper we calculate the best fittest value among the populations which is generated.

Objective To evaluate the design characteristics of studies that evaluated the performance of artificial intelligence (AI) algorithms for the diagnostic analysis of medical images. Materials and methods PubMed MEDLINE and Embase databases were searched to identify original research articles published between January 1, 2018 and August 17, 2018 that investigated the performance of AI algorithms that analyze medical images to provide diagnostic decisions. Eligible articles were evaluated to determine 1) whether the study used external validation rather than internal validation, and in case of external validation, whether the data for validation were collected, 2) with diagnostic cohort design instead of diagnostic case-control design, 3) from multiple institutions, and 4) in a prospective manner. These are fundamental methodologic features recommended for clinical validation of AI performance in real-world practice. The studies that fulfilled the above criteria were identified. We classified the publishing journals into medical vs. non-medical journal groups. Then, the results were compared between medical and non-medical journals. Results Of 516 eligible published studies, only 6% (31 studies) performed external validation. None of the 31 studies adopted all three design features: diagnostic cohort design, the inclusion of multiple institutions, and prospective data collection for external validation. No significant difference was found between medical and non-medical journals. Conclusion Nearly all of the studies published in the study period that evaluated the performance of AI algorithms for diagnostic analysis of medical images were designed as proof-of-concept technical feasibility studies and did not have the design features that are recommended for robust validation of the real-world clinical performance of AI algorithms.

The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm.

Advanced map-matching algorithms use location and heading of GPS points along with geometrical and topological features of digital road networks to find the road segment on which the vehicle is moving. However, GPS errors sometimes impede map-matching algorithms in finding the correct segment, especially in dense and complicated parts of the network, such as near intersections with acute angles or on close parallel roads. In this paper an artificial neural network (ANN) approach is explored to improve the segment identification accuracy of map-matching algorithms. The proposed ANN is continuously trained by using the horizontal shift imposed on GPS points and once it is trained, it will be used to correct raw GPS points before inputting them into the map-matching algorithm. Integrating the proposed ANN enabled an existing map-matching algorithm to find the correct segments for some of the GPS points where the original map-matching algorithm had failed to do so.

Most works covering the topic of transfer learning propose an algorithm to solve a given domain adaptation problem, then test the algorithm using real-world datasets. A test with a real-world dataset represents a single transfer learning test condition, which partially measures an algorithm's performance. Previous research has placed little emphasis on developing a comprehensive and uniform test for transfer learning algorithms. With this in mind, a test framework is proposed, comprising of distortion profiles which define a comprehensive test suite. The unique contribution of this paper is the definition of a test framework that measures a more complete profile of a transfer learning algorithm's capability, facilitating the identification of relative poor and good performance areas. As a proof of concept, the test framework is used to test a homogeneous transfer learning algorithm. The test framework will be the basis for a number of future applications.

Two players of Rock-Paper-Scissors are modeled as adaptive agents which use a reinforcement learning algorithm and exhibit chaotic behavior in terms of trajectories of probability in mixed strategies space. This paper demonstrates that an external super-agent can exploit the behavior of the other players to predict favorable moments to play against one of the other players the symbol suggested by a sub-optimal strategy. This third agent does not affect the learning process of the other two players, whose only goal is to beat each other. The choice of the best moment to play is based on a threshold associated with the Local Lyapunov Exponent or the Entropy, each computed by using the time series of symbols played by one of the other players. A method for automatically adapting such a threshold is presented and evaluated. The results show that these techniques can be used effectively by a super-agent in a game involving adaptive agents that exhibit collective chaotic behavior.

Highly realistic visualization becomes crucial in numerous applications such as: medical imaging, simulation and entertainment systems. In this article, we describe the image/video capture system aimed for autostereoscopic multiview displays, as well as view interpolation and image restoration algorithms used to enhance the image quality. We evaluate the proposed algorithms on a head tracking based stereoscopic multiview display.

According to the problem that conventional laminated paper counting algorithms have some unavoidable shortcomings such as high dependence on the quality of laminated paper and noise sensitivity, we propose a laminated paper counting algorithm based on compressive sensing (CS) and Hough transform (HT). In the proposed algorithm, the over-complete dictionary which is created by dispersing the Hough transform space of straight lines acts as the sparse matrix. Making use of high degree of sparse nature of the laminated paper image, we can obtain the accurate result through using CS theory. Experimental results on simulation images and laminated paper images have shown that our proposed algorithm can effectively restrain noise of the laminated paper image, and will get accurate experimental results with fewer CS measurements.

Nowadays volume images are frequently used in many applications. Volume images of paper can be analyzed to increase the understanding of the complexity of the fiber network in paper and its effect on the optical and mechanical properties of paper. We show how curve and surface representations of the fiber, the fiber wall, and the fiber lumen can be computed using distance transform based algorithms. These representations are used for easy computation of wall thickness, degree of collapse, fiber length, slenderness ratio, fiber curl, and torsional resistance for the fibers. Free-fiber segments are identified, again using a distance transform based algorithm. Finally, we show tools for qualitative visual inspection of the fibers. The methods are evaluated and illustrated using both sets of synthetic and of real data. In addition to the analysis of the fiber network in paper, they can be used in many other applications where shape analysis of elongated objects is to be performed.

This study attempts to determinate color changes based on time in inks applied on the surface of wood-free uncoated paper with offset printing during drying. This study consists of two main cases: (1) Experimental analysis: By preparing a test page according to the 12647-2 principle with an offset printing system, test prints were applied to 120 g/m2 wood-free uncoated paper using an ECI 2002 CMYK test chart. Each press was measured being subject to process every 15 min in the first 2 h, then hour by hour between 2 and 12 h, then 4---4 h between 12 and 24 h, and then 6---6 h between 24 and 48 h. CIELAB and reflectance values between 380 and 720 nm of the target, 1,485 colors of the test chart were obtained. To see the drying and color changes of the ink on paper, changes were determined by printing on the paper and applying artificial neural network (ANN) to spectrophotometer data at the stated time intervals. (2) Empirical analysis: The use of the ANN has been proposed as numerical approach to get of empirical equations of color changes in inks applied on the surface of wood-free uncoated paper with offset printing during drying. Based on the outputs of the study, ANN model can be used to estimate the effects of digital proofing systems used in color management on print quality with high confidence with the use of the acquired equations without experimental study. In the study, as colors are defined in terms of wave length, in case, all wave lengths are taken into consideration, certain wave length changes have been taken into consideration.

The paper presents and evaluates the power of a new scheme that generates search heuristics mechanically for problems expressed using a set of functions or relations over a finite set of variables. The heuristics are extracted from a parameterized approximation scheme called Mini-Bucket elimination that allows controlled trade-off between computation and accuracy. The heuristics are used to guide Branch-and-Bound and Best-First search. Their performance is compared on two optimization tasks: the Max-CSP task defined on deterministic databases and the Most Probable Explanation task defined on probabilistic databases. Benchmarks were random data sets as well as applications to coding and medical diagnosis problems. Our results demonstrate that the heuristics generated are effective for both search schemes, permitting controlled trade-off between preprocessing (for heuristic generation) and search.

Effective recognition and localization of paper defect based on machine vision is the key issue for paper defect detection system. This paper proposed an improved algorithm by combination with Blob analysis algorithm and image preprocessing approach to detect the paper defects which exist in captured images by a linear charge coupled device (CCD) camera. First, the defected images are preprocessed, such as image denoising, image segmentation, connectivity analysis, and then extract effective paper textures: defect amount, regional area, long axis, short axis, central position and so on, meanwhile draw the minimum bounding rectangles. Compared with the traditional morphology algorithm and threshold segmentation and fractal feature algorithm, the improved algorithm is validated by a great deal of experimental results with high detection efficiency and defects localization accuracy.

With the increasing use of CiteSeer academic search engines, the accuracy of such systems has become more and more important. The paper adopts the improved particle swarm optimization algorithm for training conditional random field model and applies it into the research papers’ title and citation retrieval. The improved particl swarm optimization algorithm brings the particle swarm aggregation to prevent particle swarm from being plunged into local convergence too early, and uses the linear inertia factor and learning factor to update particle rate. It can control algorithm in infinite iteration by the iteration between particle relative position change rate. The results of which using the standard research papers’ heads and references to evaluate the trained conditional random field model shows that compared with traditionally conditional random field model and Hidden Markov Model, the conditional random field model ,optimized and trained by improved particle swarm, has been better ameliorated in the aspect of F1 mean error and word error rate. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2188 Full Text: PDF

Most traditional scientific papers are unstructured documents, which are difficult to meet the requirement of structured retrieval, statistical classification and association analysis and other high-level application, how to extract and analyze the structured information of the papers becomes a challenging problem. A structured information extraction algorithm for unstructured and/or semi-structured machine-readable documents is proposed, it according to the extracted rules after feature learning on the basis of analyzing the basic structure and format features of traditional scientific papers, which extracts the title, author, abstract, keywords, text and other elements of paper from the unstructured documents, such as Word, then exports the structured text from the traditional scientific papers with the format required by multi-dimensional scientific papers, it can meet the requirements of structured retrieval, statistical classification and other high-level applications of scientific papers.

In order to keep up with the requirements of College English Diagnostic Test System developed by our team, after analyzing the pros and cons of current test paper generating algorithms, the MCKP algorithm, which is based on maximum coverage of knowledge points (MCKP), is proposed. In this paper, the knowledge hierarchy of College English Test (CET4) of China is constructed to provide the foundation for MCKP. Then given three main parameters, including the required items number, the mastery rates of knowledge-point and the testing frequencies of knowledge points, the theory and implementation of the MCKP algorithm is elaborated. At last, experiments indicate relatively high success rate and effectiveness of test paper generating process. Therefore, MCKP algorithm can provide an essential improvement in test paper generating process. Keywordsmaximum covarage of knowledge points (MCKP); knowledge hierarchy of CET-4;mastery rates ;testing frequencies; success rate

There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the hidden (variable) transformation. We perform a detailed formal comparison of these two transformations. Our comparison focuses on two backtracking algorithms that maintain a local consistency property at each node in their search tree: the forward checking and maintaining arc consistency algorithms. We first compare local consistency techniques such as arc consistency in terms of their inferential power when they are applied to the original (non-binary) formulation and to each of its binary transformations. For example, we prove that enforcing arc consistency on the original formulation is equivalent to enforcing it on the hidden transformation. We then extend these results to the two backtracking algorithms. We are able to give either a theoretical bound on how much one formulation is better than another, or examples that show such a bound does not exist. For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem. Our results can be used to help decide if applying one of these transformations to all (or part) of a constraint satisfaction model would be beneficial.

The present paper deals with constructive method of three-dimensional shapes learned from nesting behavior of paper wasps. Paper wasps can construct comb of hexagonal cells with regular arrangement. This paper describes observation and analysis of nesting behavior. Observation results show that characteristic movements of antennae affect the shapes of nest and that nesting behavior connect with so called “taxis.” Computer simulation was conduct to ascertain relationship between constructed shapes and simple moving rules. The results are as follows : 1. Antennae are used in each process of nest construction as a wide variety of tools. 2. The simulation results show that simple moving rules such as “taxis” lead to the construction of straight line, curved line and hexagonal line, starting from the shapes of the base as a mark. 3. Construction algorithm of three-dimensional objects based on a simple moving rules is derived from analysis of paper wasps nest construction behavior. It is considered a method for construction of three dimentional shapes starting from an arbitrary base shapes as a mark.

Summary: The Big Five factors structure is currently the benchmark for personality dimensions. In the domain of adjectives, various instruments have been developed to measure the Big Five. In this contribution we propose a methodology to find a simple factorial structure and we apply this methodology to the domain of Big Five as measured by adjectives. Using data collected on a sample of 337 subjects, we propose a five-factor benchmark structure derived from the 50 best marker adjectives selected among the adjectives contained in three instruments specifically developed to measure the Big Five (i.e., Goldberg 's 100 adjectives list, IASR-B5, and SACBIF). We use this common factor structure (or benchmark structure) to investigate the differences and the similarities between the three operationalizations of the Big Five, and to investigate the placements of the full set of adjectives contained in the three instruments. The main features of the proposed methodology and the generalizability of the obtained results are discussed.

International audience; In this note, we point out two major errors in the paper "Minimizing total tardiness on parallel machines with preemptions" by Kravchenko and Werner (2012). More precisely, they claimed to have proved that both problems P|pmtn|∑T j and P|r j ,p j =p,pmtn|∑T j are NP -Hard. We give a counter-example to their proofs, letting the complexity of these two problems open.