Reviewerly/exHarmony huggingface dataset:
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 January 2021; 126 (1): 75–76. doi: https://doi.org/10.1352/1944-7558-126.1.75 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest Search
Extending electronic medical records vector models with knowledge graphs to improve hospitalization prediction

Abstract Background Artificial intelligence methods applied to electronic medical records (EMRs) hold the potential to help physicians save time by sharpening their analysis and decisions, thereby improving the health of patients. On the one hand, machine learning algorithms have proven their effectiveness in extracting information and exploiting knowledge extracted from data. On the other hand, knowledge graphs capture human knowledge by relying on conceptual schemas and formalization and supporting reasoning. Leveraging knowledge graphs that are legion in the medical field, it is possible to pre-process and enrich data representation used by machine learning algorithms. Medical data standardization is an opportunity to jointly exploit the richness of knowledge graphs and the capabilities of machine learning algorithms. Methods We propose to address the problem of hospitalization prediction for patients with an approach that enriches vector representation of EMRs with information extracted from different knowledge graphs before learning and predicting. In addition, we performed an automatic selection of features resulting from knowledge graphs to distinguish noisy ones from those that can benefit the decision making. We report the results of our experiments on the PRIMEGE PACA database that contains more than 600,000 consultations carried out by 17 general practitioners (GPs). Results A statistical evaluation shows that our proposed approach improves hospitalization prediction. More precisely, injecting features extracted from cross-domain knowledge graphs in the vector representation of EMRs given as input to the prediction algorithm significantly increases the F1 score of the prediction. Conclusions By injecting knowledge from recognized reference sources into the representation of EMRs, it is possible to significantly improve the prediction of medical events. Future work would be to evaluate the impact of a feature selection step coupled with a combination of features extracted from several knowledge graphs. A possible avenue is to study more hierarchical levels and properties related to concepts, as well as to integrate more semantic annotators to exploit unstructured data.
Collective Controllerism

Music has been constantly reinvented by a multitude of inventions. In recent years, digital technologies have given rise to practices that not only break free from traditional canons of musical literacy, but further invite engagement by artists whose predominant expressive medium is other than sound. This chapter examines this emerging phenomenon through the perspective of a collaboration between a contemporary dancer and a controllerist, i.e., a non-musician performer of electronic music using tangible interfaces for real-time sequencing, manipulation, and generation of sound. The chapter begins with the author’s reflection on his artistic identity, and the challenges of operating on the intersection of practices utilising different expressive mediums. A literature review follows, outlining the lineage of controllerism as an emergent practice borne out of commercial music technology and the transition of disc jockeys from analogue to digital equipment. Through a Practice Research methodology, a reflective work is analysed as to detail the different devices and mapping strategies that control sound, as well as designing an environment that affords dancer’s cognition of the music-making processes and allows them to utilise movement towards actively participating in a controllerist performance. With controllerism extended to a hybrid collaborative domain, the (non-) musician’s perspective shifts away from utilising movement data as merely a further input device complementing traditional controllers, and instead considers dancer collaborators as music performers that can be entrusted with commanding crucial sonic elements. The chapter concludes by suggesting ways for controllerism to become further established as a research topic through interface standardisation and the development of transcription systems.
Super-resolution reconstruction of thermal imaging of power equipment based on Generative Adversarial Network with Channel Filtering

Non-contact infrared thermal imaging can help people visually see the temperature distribution on the surface of an object, which is of great significance for power equipment status monitoring and health status evaluation. However, the expensive cost and technical barriers of high-resolution infrared imagers limit the application of high-resolution thermal imaging images in the online monitoring of power Internet of Things equipment. Super-resolution reconstruction meets the resolution requirements while reducing costs. This paper realizes the enhancement of thermal imaging of power equipment by constructing a generative confrontation network with channel filtering. This network embeds a threshold filtering module in the generator part of SRGAN (super-resolution generative confrontation network), and uses channel information for autonomous threshold learning. Filtering improves the stability of training on the basis of reducing image noise; at the same time, the peak information of the image is strengthened through edge extraction technology to improve the network's ability to restore the edge of the image. The analysis of the calculation example shows that: using PSNR (peak signal to noise ratio) and SSIM (structural similarity) indicators to analyze the overall data and edge data after reconstruction, the results are significantly improved, and the subjective visual effect after reconstruction is clearer. Higher engineering practical value.
Fitness Value Curves Prediction in the Evolutionary Process of Genetic Algorithms Applied to Benchmark Function

This work intends to adopt fitness curves prediction from Genetic Algorithms (GAs) proposed in [Almeida et al. 2021], in the context of a more complex function, which is the Schwefel benchmark function. The prediction is performed with the knowledge only of the GA initialization parameters, using the Random Forest model. This approach addresses the main gap in the original work achieving good results, which makes this approach more promising.
The Fri Sampling and Recovery of Pulse Streams Based on Discrete Cosine Transform

The Finite Rate of Innovation (FRI) sampling theory can realize the sub-Nyquist sampling and reconstruction of the pulse streams, which aims to reconstruct the characteristic parameters rather than the time domain waveform. Different from the classical spectral estimation algorithm in this framework, we regard Discrete Cosine Transform (DCT) as the bridge between the discrete time domain and the parameter domain, and use the pure real field information to estimate the parameters in order to speed up the estimation process. Based on optimization theory, AFoD algorithm is proposed to solve the conjugate roots problem caused by DCT coefficients with which the original signal composed of K pulses per unit time can be perfectly reconstructed with only 4K samples. In order to improve the anti-noise performance of AFoD algorithm, we propose the improved MUoD and ESoD algorithms. We also design the 2DFoD algorithm for sampling and reconstruction of 2D pulses streams. Simulation results show that our estimation schemes can recover signal parameters perfectly with a low sampling rate, and the speed of parameter estimation is much faster than that of classical FRI method at the same sampling rate. The simulation experiments give the expected excellent results
Image super-resolution using CNNs for dual-focal camera system

Image super-resolution (SR) is the problem of recovering a high-resolution (HR) image from a low-resolution (LR) image of the same scene. It is an ill-posed problem since high frequency details are completely lost in low-resolution images. To overcome this problem, many methods based on deep learning have been proposed. In our dual-focal camera system, we use a beam-splitter to capture images of the same scene at different resolutions. The shorter focal length module produces the wide-view image with the low resolution, and the longer focal length module produces the tele-view image via optical zooming. The long-focal image contains more details than short-focal image so can be used to guide short-focal image to recover high frequency part. However, existing SR approaches ignore the nature of image degradation, which limits the use of these approaches in challenging cases. In this paper, we propose a novel method which is based on the dual-focal camera system. To reconstruct a wide-view image with the high-resolution, we design an end-to-end deep learning model. First, we using a blur kernel estimation convolution neural networks (CNNs) to obtain the mapping relationship between the short-focal image and the long-focal image. The kernel estimation network is a Laplacian pyramid, which can efficiently learn per-pixel kernels to recover the HR image. Then, we interpolate the kernel and apply it to the wide- view image so as to get the high-resolution wide-view image. Extensive experiments show that our method achieves favorable performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
Estimation Techniques for Scrum: A Qualitative Systematic Study

Every competitive IT industry cannot avoid underestimating their projects’ effort, cost, and time. Some scrum project is completed delayed and undergoes difficulties due to over budgeting and a lack of needed functions. Software project failures are caused by incorrect and imprecise estimation; thus, it should be taken into account. A substantial change is required when Agile-based processes (e.g., Scrum) are introduced to the industry. The analysis is still difficult with Agile since requirements are constantly changing. Projects, individuals, and resistance issues, incorrect usage of cost factors, unawareness of regression testing work, readability of software requirements size as well as its related complexities, and so forth are all causes behind the difference in anticipated and real effort. This work analysis examined several publications and prospective researchers striving to narrow the actual and estimated effort gap. Decision-Based techniques significantly outperformed non-Decision Based and conventional estimating strategies by extensive literature analysis. We found that the regression test based estimation technique should be improved for accurate estimation of effort. However, scrum still needs a significant estimation technique to resolve the over budgeting issue. This study discussed the machine learning techniques, there proficiencies for estimation and flaws. The overall effort is the sum of all sprints components’ efforts, and it repeats after the prospective deliverable version.
DISTANT EDUCATION. STUDENTS’ POINT OF VIEW

The perception of positive and negative features of the distance education by students of Moscow schools and universities is considered. By comparison of the sociological surveys data conducted before the pandemic and those done currently the author reveals a number of invariant difficulties for students associated with adaptation to the general learning environment and interaction with the teacher. The main obstacles to the distance learning progress were the organizational unpreparedness of universities and schools, as well as the overload of the Internet and educational platforms. The reasons for the predominance of a positive attitude to distance learning over a negative one are the time-savings, increased mobility and the use of new opportunities for self-education.
Biometric gun holsters go on sale

New York-based Vara Safety has started shipping a range of ‘Reach’ biometric gun holsters, which aim to stop anyone except the owner getting hold of the weapon.
Operational framework for Enhancing Trust in Identity Management as-a-Service (IdMaaS)

The promise of access to contextual expertise, advanced security tools and an increase in staff augmentation coupled with reduced computing costs has indisputably made cloud computing a computing platform of choice, so enticing that many organizations had to migrate some if not all their services to the cloud. Identity-management-as-a-service (IdMaaS), however, is still struggling to mature due to lack of trust. Lack of trust arises from losing control over the identity information (user credentials), identity management system as well as the underlying infrastructure, raising a fear of loss of confidentiality, integrity and availability of both the identities and the identity management system. This paper recognizes the need for a trust framework comprising of both the operational and technical Frameworks as a holistic approach towards enhancing trust in IdMaaS. To this end however, only the operational Framework will form the core of this paper. The success of IdMaaS will add to the suite of other matured identity management technologies, spoiling the would-be identity service consumers with a wide choice of identity management paradigms to pick from, at the same time opening entrepreneurial opportunities to cloud players.
A Railway Intrusion Detection Framework Based on Vehicle Front Video

Railway intrusion seriously threatens railway safety and may cause serious casualties and huge property losses. With the development of autonomous driving, image processing algorithms based on vehicle front video have developed rapidly. This paper proposes a railway intrusion detection framework based on vehicle front video. The framework mainly consists of two stages: object detection and semantic segmentation. In the first stage, an object detection algorithm is used to detect potential intrusion objects. The second stage uses the semantic segmentation algorithm to obtain the railway perimeter area. If the object is found within the railway perimeter, it is regarded as an intrusion. The railway intrusion detection framework proposed in this paper starts the semantic segmentation algorithm only when the object detection algorithm detects potential intrusion objects. Finally, the proposed framework is tested on railway video and achieves 100% accuracy and 23 FPS (Frames Per Second).
A Study on Chinese Classical Novel Education and Capstone Design

The purpose of this study is to find a new method of classical education through case analysis of Capstone Design. With the advent of the new media era, the form of human life is changing rapidly. Moreover, various situations arising from COVID-19 constantly make us think about human values. In this situation, the problem of educating literature should also seek change. Above all, if you focus only on classical translation and teach or explain text-oriented, it will be difficult to cultivate talented people suitable for the new era.<BR> In order to overcome the limitations of existing classical education, I would like to pay attention to Capstone Design. Capstone Design is an educational program for engineering students, but it can be expanded into a literary education course. Based on the understanding of the classics, students directly tell stories to produce and plan various cultural contents. Through this, students can develop creative and convergent thinking, and experience a closer distance between university education and industrial sites.
Pose estimation using polarimetric imaging in low-light environment

Visual pose estimation is of great significance in the field of precision measurement and navigation and positioning. However, under low-light illumination conditions, the feature extraction of traditional visual images is easy to fail, resulting in the failure of visual pose estimation. This paper proposes a pose estimation method based on polarimetric imaging under low-light illumination conditions. This method calculates the polarization information of the target environment and uses the polarization image to calculate the pose. Since the degree of polarization can highlight the contour of the target, this method combines the advantages of the polarization characteristics of the target environment to estimate the pose. Through environmental experiments, we found that the grayscale distribution of the polarization image is more uniform in the low-light environment, and the grayscale does not change significantly with the illumination. We verified the feasibility of the proposed posture estimation method based on polarimetric imaging. This method provides a technical reference for special scenarios (tunnels, underground parking lots).
Research on the Algorithm of Cross-region Target Tracking in the Perimeter of High Speed Railway Based on Deep Learning

With the continuous development of science and technology, more and more fields are applying emerging video surveillance technology to ensure public safety. Compared with the way of manually processing video, the video monitoring technology based on deep learning also has the advantages of higher efficiency, high accuracy and intelligence when processing more and more massive video image data. In this paper, aiming at the detection requirements of personnel intrusion in the perimeter of high-speed railways, it realizes cross-region tracking of suspicious persons. It studies the application of deep learning cross-region tracking algorithm based on ResNet-50 in high-speed railway perimeter intrusion detection, and analyzes its reliability and optimizes accuracy. The degree reached 88%. Compared with the results of other cross-region tracking algorithms, the detection accuracy is still high. Therefore, it can be seen that the high-speed rail perimeter cross-region target tracking based on deep learning is feasible and efficient.
Inversion of elastic constants of composite materials by single test based on virtual fields method

Abstract In this paper, a method of obtaining four elastic constants of orthotropic composites by single tensile test is proposed. The principle of this method is to obtain the full field strain on the surface of the specimen through the tensile test of the specimen with a specific geometry, and to invert the measured in-plane strain field into the virtual fields method (VFM) to obtain the elastic constants. Based on the deformation field data generated by finite element numerical simulation, the effects of design parameters such as effective length, notch position, notch size and fillet radius on the identification results of elastic parameters of V-notch tensile specimen are studied. The results show that the inversion results of symmetrical V-notch specimen with 50 mm length, 12 mm notch size and 3.1 mm fillet radius are the best, and the sum of absolute relative errors of four elastic constants is less than 3%, which verifies the feasibility of the method.
Instance Selection dengan Naïve Bayes pada Klasifikasi Kanker Serviks

There are several deadly disease for woman, one of which is servical cancer. The growth and development of the disease is very slow, so that treatment if know form the beginning will facilitate the healing process, but conversely unknown cancers from the beginning will become dangereous and deadly disease due to relatively difficult healing. Biopsy action is one way to detect the presence of cancer. In the previous study, classification of cervical cancer had the bighest accuracy value of 97,515% using the decision tree method of several feature selection technique. for this reason, this research will use the decision tree or tree C4.5 classification method, logistic function and zeroR which were previously carried out processing with instance selection with Naïve Bayes by reducing the elimination of missing values with the aim of increasing the level of accuracy better than previous studies. C4.5 classification in this study has the most maximum results compared to other classification methods with an accuracy value of 99,69%.
Study on the Efficiency of Video Scene Detection Based on YOLO Series

The safety of high-speed railway operation in 2021 Beijing Winter Olympics puts forward higher requirements for the speed and accuracy of detecting foreign body intrusion on the high-speed railway. Based on the sampling data of the severe weather of high-speed railway cameras, this paper builds a network and uses empirical weights to explore the efficiency of the most widely used YOLO series algorithms. Besides, we use 16380 images as the testing data set to test the speed and accuracy of detecting algorithms, trying to find the best method to give us the real-time detecting results and meet emergencies promptly, which has profound significance both socially and economically.
The Analysis of the Measurement System Accuracy Based on 2D Laser Optical Micrometer When Measuring API Pipe Thread Pitch and Height

A new method for measuring threads has been proposed. A measuring system based on a robot arm, a linear actuator, and a 2D laser micrometer was created. The law of the distribution of the height and pitch of the threads measured by the new system has been determined. Casts of special composite materials were used to verify the measurement error of the 2D laser micrometer. Casts from the thread surface were measured using a precision optical microscope to determine pitch and height values with an accuracy ten times greater than that of the assembled experimental system. Twenty independent measurements were made to estimate the relative error of the thread height and pitch values. In order to estimate the absolute error of the values of height and pitch of the thread, a comparison of the results of measurements of the microscope and the system was held. The ability of a 2D laser micrometer to achieve the standard measurement accuracy requirements of the API 5CT for threaded pipes was experimentally verified.
Development of an IoT Structural Damage Monitoring system

During a devastating seismic event, damage to building infrastructure is observed. Many times, there are trapped people in the ruins. Delays in pulling out the injured people under the debris are likely to lead to their death. This paper focuses on the development of a structural damage monitoring system that will help to mitigate the secondary consequences of an earthquake. The system's overall framework is described. An accelerometer and a microprocessing unit with wireless transmission capabilities compose a node of this system. Data transmission, acquisitioning as well as damage diagnostic procedures are all discussed.