Reviewerly/exHarmony huggingface dataset:
Effective Anti-corruption as a Competitive Advantage of a Modern University

The subject area of the article is the improvement of the competitive position of a modern Russian university due to such an important component of its image in the educational services market as an organization that effectively counteracts corruption by its own staff. The solution of this problem is provided by the introduction of new technologies implemented by the joint forces of the two headquarters instances represented by the personnel service and the security service. The thesis is argued that the degree of protection from the threat of corruption on the part of both the teaching staff and other officials can be legitimately considered as an additional competitive advantage of any subject of the national higher education or, accordingly, its competitive disadvantage. Possible forms of corruption actions by various categories of employees of an educational institution are specified. Threats to its reputational and property security are determined in case of insufficiently effective counteraction to corruption. The dangers and opportunities associated with the use of various anti-corruption measures already proposed by specialists in the researched field of ensuring personnel security in the field of higher professional education are analyzed. Innovations in personnel work are proposed and argued, which can significantly reduce the likelihood of corrupt actions on the part of teachers and managers of the university. The first of them involves the use of technology based on a collegial approach to assessing the knowledge of the student being examined. The second innovation is aimed at a sharp tightening of control over the “anti-corruption stability” of teachers by the security service of the university. The third innovation applies only to the activities of top managers of the university and their direct subordinates, who are authorized to prepare for the conclusion of any transactions that potentially have a corruption component, and is based on the mechanism of individual bonuses. Finally, the introduction of the fourth innovation should ensure that the faculty members and other university staff are fully aware of the composition of corrupt practices and possible sanctions if they are detected.
Intelligent Robotic Arm Path Planning (IRAP2) Framework to Improve Work Safety in Human-Robot Collaboration (HRC) Workspace Using Deep Deterministic Policy Gradient (DDPG) Algorithm

Abstract Industrial robots are widely used in manufacturing systems. The places that humans share with robots are called human-robot collaboration (HRC) workspaces. To ensure the safety in HRC workspaces, a collision-avoidance system is required. In this paper, we regard the collision-avoidance as a problem during the robot action trajectory design and propose an intelligent robotic arm path planning (IRAP 2 ) framework. The IRAP 2 framework is based on the deep deterministic policy gradient (DDPG) algorithm because the path planning is a typical continuous control problem in a dynamic environment, and DDPG is well suited for such problems. To test the IRAP 2 framework, we have studied a HRC workspace in which the robot size is larger than humans. At first, we have applied a physics engine to build a virtual HRC workspace including digital models of a robot and a human. Using this virtual HRC workspace as the environment model, we further trained an agent model using the DDPG algorithm. The trained model can optimize the motion path of the robot to avoid collision with the human.
Pedagogical Staff and Digital Competencies: Professional Development in the Academic Digitalization

Modern university teaching staff require digital competencies, but their development in the digitalization epoch still remains understudied. The organization and content of digital training in the system of continuing pedagogical education also need a unified approach. The article focuses on digital skill development in faculty members as part of life-long learning. The authors defined the concept of digital competency and created a professional retraining program of Digital Technologies in Teaching, which they put to an evaluation test. The course lasted 72 hours and consisted of several stages and a final assessment. The research objective was to identify the level of digital competencies in the course graduates. The study involved a review of relevant scientific publications, an analysis of normative documents, and various methods of observation. The program provided a well-structured learning process and enabled the participants to create digital syllabuses of their own. It can be recommended for teaching staff of all education levels who would like to increase their digital competency.
SPIE Medical Imaging 50th anniversary: history of the Picture Archiving and Communication Systems Conference

To commemorate the SPIE Medical Imaging 50th anniversary, this article provides a brief review of the Picture Archiving and Communication Systems (PACS) and Informatics conferences. Important topics and advances, contributing researchers from both academia and industry, and key papers are noted.
A Survey on Extreme Multi-label Learning

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML.
STaSy: Score-based Tabular data Synthesis

Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based Tabular data Synthesis (STaSy) and its training strategy based on the paradigm of score-based generative modeling. Despite the fact that score-based generative models have resolved many issues in generative models, there still exists room for improvement in tabular data synthesis. Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training. Furthermore, we also conduct rigorous experimental studies in terms of the generative task trilemma: sampling quality, diversity, and time. In our experiments with 15 benchmark tabular datasets and 7 baselines, our method outperforms existing methods in terms of task-dependant evaluations and diversity. Code is available at https://github.com/JayoungKim408/STaSy.
Leveraging progressive model and overfitting for efficient learned image compression

Deep learning is overwhelmingly dominant in the field of computer vision and image/video processing for the last decade. However, for image and video compression, it lags behind the traditional techniques based on discrete cosine transform (DCT) and linear filters. Built on top of an autoencoder architecture, learned image compression (LIC) systems have drawn enormous attention in recent years. Nevertheless, the proposed LIC systems are still inferior to the state-of-the-art traditional techniques, for example, the Versatile Video Coding (VVC/H.266) standard, due to either their compression performance or decoding complexity. Although claimed to outperform the VVC/H.266 on a limited bit rate range, some proposed LIC systems take over 40 seconds to decode a 2K image on a GPU system. In this paper, we introduce a powerful and flexible LIC framework with multi-scale progressive (MSP) probability model and latent representation overfitting (LOF) technique. With different predefined profiles, the proposed framework can achieve various balance points between compression efficiency and computational complexity. Experiments show that the proposed framework achieves 2.5%, 1.0%, and 1.3% Bjontegaard delta bit rate (BD-rate) reduction over the VVC/H.266 standard on three benchmark datasets on a wide bit rate range. More importantly, the decoding complexity is reduced from O(n) to O(1) compared to many other LIC systems, resulting in over 20 times speedup when decoding 2K images.
Spread Love Not Hate: Undermining the Importance of Hateful Pre-training for Hate Speech Detection

Pre-training large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. Although this method has proven to be effective for many domains, it might not always provide desirable benefits. In this paper, we study the effects of hateful pre-training on low-resource hate speech classification tasks. While previous studies on the English language have emphasized its importance, we aim to augment their observations with some non-obvious insights. We evaluate different variations of tweet-based BERT models pre-trained on hateful, non-hateful, and mixed subsets of a 40M tweet dataset. This evaluation is carried out for the Indian languages Hindi and Marathi. This paper is empirical evidence that hateful pre-training is not the best pre-training option for hate speech detection. We show that pre-training on non-hateful text from the target domain provides similar or better results. Further, we introduce HindTweetBERT and MahaTweetBERT, the first publicly available BERT models pre-trained on Hindi and Marathi tweets, respectively. We show that they provide state-of-the-art performance on hate speech classification tasks. We also release hateful BERT for the two languages and a gold hate speech evaluation benchmark HateEval-Hi and HateEval-Mr consisting of manually labeled 2000 tweets each. The models and data are available at https://github.com/l3cube-pune/MarathiNLP .
An algorithmic approach based on generating trees for enumerating pattern-avoiding inversion sequences

We introduce an algorithmic approach based on generating tree method for enumerating the inversion sequences with various pattern-avoidance restrictions. For a given set of patterns, we propose an algorithm that outputs either an accurate description of the succession rules of the corresponding generating tree or an ansatz. By using this approach, we determine the generating trees for the pattern-classes $I_n(000, 021), I_n(100, 021)$, $I_n(110, 021), I_n(102, 021)$, $I_n(100,012)$, $I_n(011,201)$, $I_n(011,210)$ and $I_n(120,210)$. Then we use the kernel method, obtain generating functions of each class, and find enumerating formulas. Lin and Yan studied the classification of the Wilf-equivalences for inversion sequences avoiding pairs of length-three patterns and showed that there are 48 Wilf classes among 78 pairs. In this paper, we solve six open cases for such pattern classes.
A Generative Image Inpainting Model Based on Edge and Feature Self-Arrangement Constraints

At present, the image inpainting method based on deep learning has achieved a better inpainting effect than traditional methods, but the inpainting results still have problems such as local structure disorder and blurred texture when the images involving a large defect area are processed. This paper proposes a second-order generative image inpainting model based on edge and feature self-arrangement constraints. The model consists of two parts: edge repair network and image repair network. Based on the self-encoder, the edge repair network generates the edges in the defect area according to the known information of the image and improves the edge repair effect by minimizing the adversarial loss and feature matching loss. The image inpainting network fills the defect area with the edge repair result as a priori condition. On the basis of U-Net, the feature self-arrangement module (FSM) is proposed to reconstruct the coding features of a specific scale, and the reconstructed feature skips to connect the decoding layer of the same scale, and it is fused with the upper layer underlying features for decoding. Meanwhile, guide loss, adversarial loss, and reconstruction loss are introduced to narrow the difference between the repaired image and the original image. The experimental results show that the inpainting results of the proposed model have stronger structural connectivity and clearer textures and the performance of PSNR, SSIM, and mean L1 loss in the Celeba, Facade, and Places2 is better than other inpainting methods, indicating that the algorithm can produce an inpainting effect with highly connected structure, reasonable semantics, and fine details.
Improved Combined Metric for Automatic Quality Assessment of Stitched Images

An automatic quality assessment of stitched images is an essential task in image analysis and is particularly useful not only in the creation of general-purpose panoramic images but also in terrain exploration and mapping made by mobile robots and drones. In Visual Simultaneous Localization and Mapping (VSLAM) solutions, the environment maps acquired by cameras mounted on the mobile robots may be captured in dynamically changing lighting conditions and subject to some other distortions influencing the final quality of the panoramic images representing the robot’s surroundings. Such images may also be used for motion planning and visual navigation for other robots, e.g., in follow-the-leader scenarios. Another relevant application area of panoramic imaging is Virtual Reality (VR), particularly head-mounted displays, where perceived image quality is even more important. Hence, automatic quality evaluations of stitched images should be made using algorithms that are both sensitive to various types of distortions and strongly consistent with subjective quality impression. The approach presented in this paper extends the state-of-the-art metric known as the Stitched Image Quality Evaluator (SIQE) by embedding it with some additional features using the proposed new combination scheme. The developed combined metric based on a nonlinear combination of the SIQE and additional features led to a substantially higher correlation with the subjective quality scores.
68 Face Feature Points Detection Based on Cascading Convolutional Neural Network with Small Filter

Facial detection has received more and more attention in the past two decades. Due to the pose, occlusion, and illumination changes in the photo, the detection task is quite a challenge in an unconstrained environment. This paper proposed a cascaded convolutional neural network DCNNSF-CFC (Deep Convolution Neural Network with Small Filter-with Coarse-to-Fine Cascade) to localize large facial landmarks to improve the accuracy and robustness of network prediction which based on the original small filter deep convolutional neural network. Each network is trained separately to locally refine a subset of facial landmarks generated at the previous network level, and each geometric network is explicitly constrained to modify the input information of the current network. AFW, LFPW and Helen datasets were used in this study and it was proved to be more accurate and robust than DRMF, Opm, CNN and RCPR.
Breast Cancer Diagnosis with Machine Learning

An early-stage breast cancer diagnosis usually results in a high survival rate within five years. This research seeks the feasibility of applying a specific machine learning algorithm, logistic regression, to a public dataset. The result is two models, reaching accuracies of 0.965 and 0.936. It confirms that the generality of applying machine learning algorithms is beneficial to medical diagnosis. Even though this is not the first research to exploit the application of machine learning in the medical field, it still provides some valuable insights for future research.
datawizard: An R Package for Easy Data Preparation and Statistical Transformations

The {datawizard} package for the R programming language provides a lightweight toolbox to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. Therefore, it can be a valuable tool for R users and developers looking for a lightweight option for data preparation.
Breast cancer detection from ultrasound images using attention U-nets model

Breast cancer is the most common form of invasive cancer in women. In recent years, it has become standard practise to perform breast mass evaluations using ultrasound (US) imaging. US can accurately distinguish between malignant and benign breast masses when used by skilled radiologists, as compared to other medical imaging modalities such as MRI. Human domain knowledge is difficult to incorporate into the diagnosis of breast tumours because it differs greatly from person to person in terms of shape, border, curve, intensity, and other commonly used medical priors. A deep learning model that incorporates visual saliency can now be used to segment breast tumours in ultrasound images. Radiologists use the term "visual saliency," which refers to areas of an image that are more likely to be noticed. Features that prioritise spatial regions with high saliency levels are learned using the proposed method. According to validation results, tumours are more accurately identified in models that include attention layers than those without them. The salient attention model has the potential to improve medical image analysis accuracy and robustness by allowing deep learning architectures to incorporate task-specific knowledge. AUC-ROC plots show that our new model is more accurate in terms of IOU and AUC-ROC scores, dice score, precision, recall, and IOU.
Intention Prediction of a Hypersonic Glide Vehicle Using a Satellite Constellation Based on Deep Learning

Tracking of hypersonic glide vehicles (HGVs) by a constellation tracking and observation system is an important part of the space-based early warning system. The uncertainty in the maneuver intentions of HGVs has a non-negligible impact on the tracking and observation process. The cooperative scheduling of multiple satellites in an environment of uncertainty in the maneuver intentions of HGVs is the main problem researched in this paper. For this problem, a satellite constellation tracking decision method that considers the HGVs’ maneuver intentions is proposed. This method is based on building an HGV maneuver intention model, developing a maneuver intention recognition and prediction algorithm, and designing a sensor-switching strategy to improve the local consensus-based bundle algorithm (LCBBA). Firstly, a recognizable maneuver intention model that can describe the maneuver types and directions of the HGVs in both the longitudinal and lateral directions was designed. Secondly, a maneuver intention recognition and prediction algorithm based on parallel, stacked long short-term memory neural networks (PSLSTM) was developed to obtain maneuver directions of the HGV. On the basis of that, a satellite constellation tracking decision method (referred to as SS-LCBBA in the following) considering the HGVs’ maneuver intentions was designed. Finally, the maneuver intention prediction capability of the PSLSTM network and two currently popular network structures: the multilayer LSTM (M-LSTM) and the dual-channel and bidirectional neural network (DCBNN) were tested for comparison. The simulation results show that the PSLSTM can recognize and predict the maneuver directions of HGVs with high accuracy. In the simulation of a satellite constellation tracking HGVs, the SS-LCBBA improved the cumulative tracking score compared to the LCBBA, the blackboard algorithm (BM), and the variable-center contract network algorithm (ICNP). Thus, it is concluded that SS-LCBBA has better adaptability to environments with uncertain intentions in solving multi-satellite collaborative scheduling problems.
A collaborative neurodynamic optimization algorithm to traveling salesman problem

Abstract This paper proposed a collaborative neurodynamic optimization (CNO) method to solve traveling salesman problem (TSP). First, we construct a Hopfield neural network (HNN) with $$n \times n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>×</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> neurons for the n cities. Second, to ensure the convergence of continuous HNN (CHNN), we reformulate TSP to satisfy the convergence condition of CHNN and solve TSP by CHNN. Finally, a population of CHNNs is used to search for local optimal solutions of TSP and the globally optimal solution is obtained using particle swarm optimization. Experimental results show the effectiveness of the CNO approach for solving TSP.
A Hybrid Particle Whale Optimization Algorithm for Workflow Scheduling in Cloud-Fog-Mobile Computing Environment

Abstract Cloud computing is the extensively used technology these days. Due to the usage of smart devices, a huge amount of data is produced. The processing of this data in real time is a big challenge for cloud servers. Fog computing is the solution for this, but fog has its own limitation in form of storage. To overcome, this cloud-fog architecture is preferred. In cloud-fog architecture, workflow scheduling is an open research area but finding an optimal algorithm is a major challenge. Some researchers proposed meta-heuristic algorithms to solve workflow scheduling issues but they are trapped locally and fails to give the global optimal solution. To solve workflow scheduling problems, we propose the PWOA algorithm, a hybrid of Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The goal of this algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) of dependent tasks in a cloud-fog-mobile computing environment. Because it uses the features of both the standard PSO and WOA algorithms, the proposed algorithm overcomes the trapping problem also. In this article, the simulation results were compared to standard PSO and WOA algorithms using several benchmarks of four different scientific workflows (Cybershake, Epigenomics, Inspiral, Montage, and Sipht) with different numbers of tasks assigned in the proposed algorithm performed better.