Reviewerly/exHarmony huggingface dataset:
QUALITY OF MANAGEMENT IN HIGHER EDUCATION BY THE EXAMPLE OF TOP UNIVERSITIES OF RUSSIA

In the modern conditions of globalisation, universities find themselves in the conditions of large international competition.The national programs of socioeconomic development of most countries, including Russia, envisage large requirements to universities to drivers of the development of the "knowledge society", the innovative and digital economy and the acceleration of the rate of economic growth.This paper dwells on the methodological aspects of the modern paradigm of quality of higher educational institution management, which substantiates the scientific basis of adapting educational establishments in the sphere of market relations.The authors determine the scientific approaches that form the modern paradigm of university management quality, develop a conceptual and theoretical model of quality of higher educational establishment management as a subject of the market of the educational services market, and elaborate the essence of the main technical attributes.The offered principles stimulate the increase of the effectiveness of educational activities of a higher educational establishment and are manifested in its using the economic levers, in particular the entrepreneurial mechanisms in the activities of an educational subject of t h e market in the conditions of competitive environment and formation of the information society in Russia.
Backdoor Mitigation in Deep Neural Networks via Strategic Retraining

Deep Neural Networks (DNN) are becoming increasingly more important in assisted and automated driving. Using such entities which are obtained using machine learning is inevitable: tasks such as recognizing traffic signs cannot be developed reasonably using traditional software development methods. DNN however do have the problem that they are mostly black boxes and therefore hard to understand and debug. One particular problem is that they are prone to hidden backdoors. This means that the DNN misclassifies its input, because it considers properties that should not be decisive for the output. Backdoors may either be introduced by malicious attackers or by inappropriate training. In any case, detecting and removing them is important in the automotive area, as they might lead to safety violations with potentially severe consequences. In this paper, we introduce a novel method to remove backdoors. Our method works for both intentional as well as unintentional backdoors. We also do not require prior knowledge about the shape or distribution of backdoors. Experimental evidence shows that our method performs well on several medium-sized examples.
Curved surface measurement method using chromatic confocal sensor and tilt scanning

Abstract Large measurement ranges and curvature changes are characteristics of curved surfaces, making it difficult to measure their topography. In this study, a four-axis measurement system is developed, and a tilt scanning measurement method using a chromatic confocal sensor is proposed to measure curved surfaces. The measurement area is divided into different parts, in which the tilt scanning measurement is performed with different angles of the chromatic confocal sensor. After the scanning, the complete measurement data are restored by data processing and stitching. Compared with conventional scanning methods, the proposed method greatly expands the measurement range. Finally, the effectiveness of the developed measurement system and the tilt scanning measurement method is clearly verified by measuring a spherical specimen with a radius of 50 mm.
Data Augmentation Through Expert-Guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning

Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase. Sometimes the dynamics of the model is invariant with respect to some transformations of the current state and action. Recent works showed that an expert-guided pipeline relying on Density Estimation methods as Deep Neural Network based Normalizing Flows effectively detects this structure in deterministic environments, both categorical and continuous-valued. The acquired knowledge can be exploited to augment the original data set, leading eventually to a reduction in the distributional shift between the true and the learned model. Such data augmentation technique can be exploited as a preliminary process to be executed before adopting an Offline Reinforcement Learning architecture, increasing its performance. In this work we extend the paradigm to also tackle non-deterministic MDPs, in particular, 1) we propose a detection threshold in categorical environments based on statistical distances, and 2) we show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.
Investigation and mapping of geological construction materials in parts of chemoga river sub basin, debre markos, Ethiopia

Currently the demands of geomaterials are intensively increasing all over the country regarding to the expansions of towns for the consumption of engineering structures and buildings. Therefore, searching workable, serviceable and accessible geomaterials nearby areas is a vital subject to stabilize the inflation of geomaterials. The present research was carried out in the Chemoga river sub basin in the Southern direction of Debre Markos town, North Western Ethiopia. To meet out the objectives, detailed surface explorations were carried out through systematically selected travel lines (across the strike and along streams) to detect the different lithologic formations and the contacts; A number of representative rock samples were collected for visual interpretations; GPS reading and metallic meter were the materials used in the field to quantify stratigraphic units, bedding thickness and lamination the lithologic formations; The Brunton compass was used in the field to measure the orientation (dip direction, dip amount and strikes) of geological structures. Besides to this, related literatures were reviewed to have a general background on the subject matter.Basalt and Sandstone are the dominant lithogical units in the study area and have existed with about an average thickness of 500 m and 145 m respectively. Fresh to slightly weathered basalt and columnar basalt that used as a masonry stones are ease to access, workable and the most dominant lithologic units in the study area. Fine to coarse grain reddish to light color sandstone interbedded with siltstone and mudstone is well exposed. Based on the detail field exploration and judgments, Sandstone unit has been well exposed in a desirable potential and quality. And based on the detail field investigation and researcher judgment, the sandstone resource could answer the demands of construction materials for the surrounding areas of Debre Markos town for several thousand of years. Geometrically, mostly massive and steep rock faces are well developed which are difficult for quarrying process and required high explosive materials. Generally, a sandstone rock unit is surrounded by three major normal faults and inaccessible as compared to basalt unit. Regarding to environmental influences/effects, the area is sparsely farmed and it is not common that an extractive activity does not show any environmental and social impacts. Chert is exposed on geographically limited area and rarely observed following stream cuts. Chert and basalt units are slightly to highly weathered and showed alterations on the surface. Finally, the potential and the spatial distribution of geomaterials are investigated and revealed on the map using ArcGIS software and on the basis of the findings of the present research, recommendations are forwarded.
Manipulator Trajectory Optimization Using Reinforcement Learning on a Reduced-Order Dynamic Model with Deep Neural Network Compensation

This article reports the construction of an articulated manipulator’s hybrid dynamic model and trajectory planning and optimization of the manipulator using deep reinforcement learning (RL) on the dynamic model. The hybrid model was composed of a physical-based reduced-order dynamic model, linear friction and damping terms, and a deep neural network model to compensate for the nonlinear characteristics of the manipulator. The hybrid model then served as the digital twin of the manipulator for trajectory planning to optimize energy efficiency and operation speed by using RL while taking obstacle avoidance into consideration. The proposed strategy was simulated and experimentally validated. The energy consumption along paths was reduced and the speed was increased so the manipulator could achieve more efficient motion.
Concordancing

This chapter first provides an historical overview of concordances and concordancers. Then it describes how different types of corpus resources and tools can be integrated into a computer-assisted translation environment. Finally, it surveys a set of parameters, including data search and display options, which may be used to evaluate concordancing applications.
Tuning-Free Plug-and-Play Hyperspectral Image Deconvolution With Deep Priors

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images (HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a powerful regularizer and to set the regularization parameters. To address these issues, in this paper we introduce a tuning-free Plug-and-Play (PnP) algorithm for HSI deconvolution. Specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative sub-problems. A flexible blind 3D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising sub-problem with different noise levels. A measure of 3D residual whiteness is then investigated to adjust the penalty parameters when solving the quadratic sub-problems, as well as a stopping criterion. Experimental results on both simulated and real-world data with ground-truth demonstrate the superiority of the proposed method.
AI: Workload Reduction by Autonomous Reporting of Normal Chest Radiographs

HomeRadiologyVol. 307, No. 3 PreviousNext Reviews and CommentaryEditorialAI: Workload Reduction by Autonomous Reporting of Normal Chest RadiographsChang Min Park Chang Min Park Author AffiliationsFrom the Department of Radiology, Seoul National University College of Medicine and Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Korea.Address correspondence to the author (email: [email protected]).Chang Min Park Published Online:Mar 7 2023https://doi.org/10.1148/radiol.230252MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Henderson M. RSNA News: “Radiology facing a global shortage”. https://www.rsna.org/news/2022/may/Global-Radiologist-Shortage. Published May 10, 2022. Accessed February 1, 2023. Google Scholar2. Shoshan Y, Bakalo R, Gilboa-Solomon F, et al. Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology 2022;303(1):69–77. Link, Google Scholar3. Plesner LL, Müller FC, Nybing JD, et al. Autonomous Chest Radiograph Reporting Using AI: Estimation of Clinical Impact. Radiology 2023;307(3):e222268. Google Scholar4. Alayrac JB, Donahue J, Luc P, et al. Flamingo: a visual language model for few-shot learning. arXiv:2204.14198. https://arxiv.org/abs/2204.14198. Accessed January 22, 2023. Google ScholarArticle HistoryReceived: Feb 1 2023Revision requested: Feb 7 2023Revision received: Feb 7 2023Accepted: Feb 9 2023Published online: Mar 07 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleAutonomous Chest Radiograph Reporting Using AI: Estimation of Clinical ImpactMar 7 2023RadiologyRecommended Articles Whole-Body Clinical Applications of Digital TomosynthesisRadioGraphics2016Volume: 36Issue: 3pp. 735-750Implementation of Synthesized Two-dimensional Mammography in a Population-based Digital Breast Tomosynthesis Screening ProgramRadiology2016Volume: 281Issue: 3pp. 730-736Multi-Institutional Evaluation of Digital Tomosynthesis, Dual-Energy Radiography, and Conventional Chest Radiography for the Detection and Management of Pulmonary NodulesRadiology2016Volume: 282Issue: 1pp. 236-250Clinical Performance of Synthesized Two-dimensional Mammography Combined with Tomosynthesis in a Large Screening PopulationRadiology2017Volume: 283Issue: 1pp. 70-76Can AI Help Make Screening Mammography “Lean”?Radiology2019Volume: 293Issue: 1pp. 47-48See More RSNA Education Exhibits Artificial Intelligence in Breast Imaging: Past, Present, and FutureDigital Posters2020New Horizons: Artificial Intelligence (AI) In Digital Breast Tomosynthesis (DBT)Digital Posters2021A Primer on Machine Learning and Artificial Intelligence Applications in Breast ImagingDigital Posters2019 RSNA Case Collection Invasive Lobular CarcinomaRSNA Case Collection2021Diffuse idiopathic pulmonary neuroendocrine cell hyperplasiaRSNA Case Collection2020Pulmonary HamartomaRSNA Case Collection2021 Vol. 307, No. 3 Metrics Altmetric Score PDF download
Enhanced Learning and Forgetting Behavior for Contextual Knowledge Tracing

Knowledge tracing (KT) is based on modeling students’ behavior sequences to obtain students’ knowledge state and predict students’ future performance. The KT task aims to model students’ knowledge state in real-time according to their historical learning behavior, so as to predict their future learning performance. Online education has become more critical in recent years due to the impact of COVID-19, and KT has also attracted much attention due to its importance in the education field. However, previous KT models generally have the following three problems. Firstly, students’ learning and forgetting behaviors affect their knowledge state, and past KT models have yet to exploit this fully. Secondly, the input of traditional KT models is mainly limited to students’ exercise sequence and answers. In the learning process, students’ answering performance can reflect their knowledge level. Finally, the context of students’ learning sequence also affects their judgment of the knowledge state. In this paper, we combined educational psychology theories to propose enhanced learning and forgetting behavior for contextual knowledge tracing (LFEKT). LFEKT enriches the features of exercises by introducing difficulty information and considers the influence of students’ answering behavior on the knowledge state. In order to model students’ learning and forgetting behavior, LFEKT integrates multiple influencing factors to build a knowledge acquisition module and a knowledge retention module. Furthermore, LFEKT introduces a long short-term memory (LSTM) network to capture the contextual relations of learned sequences. From the experimental results, it can be seen that LFEKT had better prediction performance than existing models on four public datasets, which indicates that LFEKT can better trace students’ knowledge state and has better prediction performance.
Pedestrian Tracking in UAV Images With Kalman Filter Motion Estimator and Correlation Filter

Visual object tracking is one of the significant parts of systems varying from autonomous driving to drone-based surveillance/tracking. Discriminative correlation filter (DCF)-based trackers have proved their prominence in the past few years. These utilize visual information present in images for monitoring. This work uses the Kalman filter to derive a motion estimation model. It combines it with two DCF-based trackers, namely, the kernelized correlation filter and the discriminative correlation filter with channel and spatial reliability for pedestrian tracking. Camera motion-compensated versions of the trackers are also presented. The performance of the proposed methodology is presented in terms of success rate (SR) and precision (P). Real-time power consumption, memory occupancy, and speed of trackers on the Jetson Nano (ARM Cortex-A57, 4 GB RAM) board have been presented.
Chinese News Title Classification Model Based on ERNIE-TextRCNN

With the rapid development of the Internet, the number of online news are increasing significantly. The efficient identification and classification of news through news headlines is of great significance for news regulation and information gathering. This paper proposes a Chinese news title classification model based on the combination of ERNIE and TextRCNN. It uses the ERNIE model to generate news text title vectors. Through the convolutional layer of TextRCNN, the generated vector is extracted with a bidirectional long short-term memory network. Then, the important parts of the text are obtained through the pooling layer, and finally classified by softmax. The experimental results show that the improved ERNIE-TextRCNN model has an accuracy value of 95.23% and an F1 value of 95.21% on the public THUCNews data set, which is significantly better than other models in evaluation indicators such as accuracy, precision, recall, and F1 value.
SeqMIA: Membership Inference Attacks Against Machine Learning Classifiers Using Sequential Information

Recent studies demonstrate that membership inference attacks can steal private information via only black-box queries to machine learning classifiers. However, existing attack techniques have not fully utilized the membership information hidden within the prediction vectors. In this work, we present a novel attack method SeqMIA that treats the prediction vectors as sequences and leverage recurrent neural networks to mine the sequential information, so as to conduct more powerful attacks. We experimentally prove that SeqMIA outperforms state-of-the-art attack techniques with respect to inference accuracy, and the highest accuracy is 87.85%.
Optically-interconnected, hardware-efficient, electronic-photonic neural network using compact multi-operand photonic devices

Optical neural network (ONN) is a promising platform for implementing deep learning tasks thanks to the critical features of light, such as high parallelism, low latency, and low power consumption. Previous ONN architectures are mainly composed of arrays of single-operand photonic devices such as Mach-Zehnder Interferometer (MZI) or microring resonator arrays. However, as the size of deep neural networks (DNNs) continues to grow, these ONNs will encounter unnecessary hardware costs, such as large chip areas and high power consumption. In this work, we devise several compact customized multi-operand active photonic components for tensor operations, for example, multi-operand ring modulators, to reduce the hardware cost of optical AI accelerators. Furthermore, we propose ONN architectures based on these multi-operand active photonic components. Compared to previous ONNs based on single-operand MZI or microring arrays, our work uses fewer optical and electrical components to implement matrix multiplications with comparable task performance. Finally, we experimentally demonstrate the utility of our proposed ONN architectures based on multi-operand photonic devices in several deep learning tasks.
Lightweight Image Super-Resolution with ConvNeXt Residual Network

Single image super-resolution based on convolutional neural networks has been very successful in recent years. However, as the computational cost is too high, making it difficult to apply to resource-constrained devices, a big challenge for existing approaches is to find a balance between the complexity of the CNN model and the quality of the resulting SR. To solve this problem, various lightweight SR networks have been proposed. In this paper, we propose lightweight and efficient residual networks (IRN), which differ from previous lightweight SR networks that aggregate more powerful features by improving feature utilization through complex layer-connection strategies. The main idea is to simplify feature aggregation by using simple and efficient residual modules for feature learning, thus achieving a good trade-off between the computational cost of the model and the quality of the resulting SR. In addition, we revisit the impact of the activation function in the model and observe that different activation functions have an impact on the performance of the model. The experiment results show that IRN outperforms previous state-of-the-art methods in benchmark tests while maintaining a relatively low computational cost. The code will be available at https://github.com/kptx666/IRN .
Adaptive tracking and classification algorithm for multiple extended targets based on irregular shape driven

The extended target Gaussian mixture probability hypothesis density (ET-GM-PHD) filter is considered as a promising algorithm for tracking multiple extended targets (METs). However, when the densities of the newborn targets are unknown and some closely spaced targets exist in the tracking scenarios, the tracking accuracy will decrease greatly, even some targets are missed tracking. For these problems, we propose an adaptive tracking and classification algorithm for METs based on B-spline shape driven. Firstly, the B-spline shape features of the estimated MET are extracted and employed for improving the measurement partition, especially for the closely spaced METs. Then shape-feature-based association matrix between the measurements and the predicted targets is calculated for identifying the newborn targets and reducing the time complexity of the Gaussian component calculation. Moreover, the extracted shape features are also used for target state update and classification, which can further enhance the tracking accuracy. Experimental results show that the proposed algorithm has a high execution efficiency and tracking accuracy for the complicated scenarios with irregular-shaped closely spaced METs and unknown newborn target densities.
StreetScouting dataset: A Street-Level Image dataset for finetuning and applying custom object detectors for urban feature detection

The recent advancements in the field of deep learning have fundamentally altered the manner in which certain challenges and problems are addressed. One area that stands to greatly benefit from such innovations is the realm of urban planning, where the utilization of these tools can facilitate the automatic detection of landscape objects in a given area. However, it must be noted that these data-driven methodologies necessitate significant amounts of training data to attain desired results. This challenge can be mitigated through the application of transfer learning techniques, which reduce the amount of required data and permit the customization of these models through fine-tuning. The present study presents street-level imagery, which can be utilized for fine-tuning and deployment of custom object detectors in urban environments. The dataset comprises 763 images, each accompanied by bounding box annotations for five landscape object classes, including trees, waste bins, recycling bins, shop storefronts, and lighting poles. Furthermore, the dataset includes sequential frame data obtained from a camera mounted on a vehicle, capturing a total of three hours of driving, encompassing various regions within the city center of Thessaloniki.