Reviewerly/exHarmony huggingface dataset:
Automatic Attendance System Using Face Recognition

The project aims to develop an automated attendance system using face recognition technology. The system eliminates manual methods and provides contactless attendance tracking. It utilizes a Raspberry Pi board, camera module, and advanced image processing techniques to detect and recognize faces in real-time. Attendance data is stored in a database which is excel sheet and can be accessed remotely. The system improves efficiency, reduces errors, and promotes hygiene and safety in various environments.
Stronger Quantum Speed Limit for Mixed Quantum States

In this paper, we derive a quantum speed limit for unitary evolution for the case of mixed quantum states using the stronger uncertainty relation for mixed quantum states. This bound can be optimized over different choices of Hermitian operators for a better bound. We illustrate this with some examples and show its better performance with respect to three existing bounds for mixed quantum states.
LiteDEKR: End‐to‐end lite 2D human pose estimation network

Abstract The 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and lightweight. Here, a LiteDEKR, a 2D pose estimation method that combines lightweight and accuracy, is proposed by designing a lightweight network based on DEKR and constructing two scientifically valid loss functions. The method, constructs a multi‐instance bias regression loss that matches the true distribution of keypoint bias, improves the accuracy of bias regression, and constructs a keypoint similarity loss with the object keypoint similarity index of keypoints as the optimization objective to achieve end‐to‐end training of the network. In addition, this paper has designed a lightweight DEKR, using LitePose as the backbone network. With the optimization of the above two loss functions, LiteDEKR not only achieves lightweight but also has high accuracy. Comparative experiments on the COCO and CrowdPose datasets show that compared to the current state‐of‐the‐art Contextual Instance Decoupling, LiteDEKR achieves a similar accuracy with only 10% of its network complexity. It also shows better robustness to low‐resolution input images.
An optimized design of the pointer meter image enhancement and automatic reading system in low illumination environment

Abstract As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination environments are unclear, resulting in poor recognition of the meter reading. A low-illumination image enhancement method based on virtual exposure is proposed in this paper, improving the dark and bright areas of low-illumination images, respectively. Then the image fusion was performed based on the Laplace pyramid to obtain clear meter images. In addition, the dial area was extracted using the Hough circle transform, and the pointer’s rotation center was fitted using the least squares approach. Finally, the straight line of the pointer was extracted, and the data reading was based on the line segment detector algorithm. Case studies show the above method has good robustness in low illumination environment, with high rate, and accuracy during the image enhancement and automatic reading of the pointer meter.
Medical prediction from missing data with max-minus negative regularized dropout

Missing data is a naturally common problem faced in medical research. Imputation is a widely used technique to alleviate this problem. Unfortunately, the inherent uncertainty of imputation would make the model overfit the observed data distribution, which has a negative impact on the model generalization performance. R-Drop is a powerful technique to regularize the training of deep neural networks. However, it fails to differentiate the positive and negative samples, which prevents the model from learning robust representations. To handle this problem, we propose a novel negative regularization enhanced R-Drop scheme to boost performance and generalization ability, particularly in the context of missing data. The negative regularization enhanced R-Drop additionally forces the output distributions of positive and negative samples to be inconsistent with each other. Especially, we design a new max-minus negative sampling technique that uses the maximum in-batch values to minus the mini-batch to yield the negative samples to provide sufficient diversity for the model. We test the resulting max-minus negative regularized dropout method on three real-world medical prediction datasets, including both missing and complete cases, to show the effectiveness of the proposed method.
The role of Russian-language and English-language university educational programs for foreign students in expanding the opportunities of Russian education export

The study aims to identify ways to improve the Russian education export strategy by choosing the most optimal language model for the implementation of university educational programs for foreign students. The paper analyzes two main ways of education export in the form of English-language and Russian-language university educational programs for foreign youth. In particular, the advantages and disadvantages of the implementation of these language models were identified both from the perspective of universities and foreign students enrolled in Russian universities. In addition, options for the likely medium-term and long-term impact of the two language models on the education sector, Russia’s migration and economic policy as a whole were considered. As a result of examining various parameters of university programs for foreigners in Russian and English, it was proved that the model of Russian-language university education for foreign students allows for the most complete implementation of the Russian education export strategy in terms of the formation of a widely recognized and highly demanded brand of Russian higher education abroad, as well as in attracting a large contingent of foreign youth and creating favorable conditions for the training of foreign students in Russian universities. The scientific novelty of the paper lies in substantiating the need for priority implementation of the Russian-language model of higher education for foreign students in Russia in order to significantly expand the opportunities of Russian education export. In addition, the paper shows that the effective implementation of the Russian education export strategy based on the specified language model of educational programs requires the creation of mass pre-university training courses for foreign youth in Russian as a foreign language in the most promising countries for attracting applicants.
Power stability of different lasers and its effect on the outcome of phase-stepping shearography experiments

We examined the effect and influence of power stability of different commonly used lasers on the outcome of phase-stepping shearographic experiments. We performed power stability measurements with three different lasers (helium-neon laser, red diode laser and green frequency-doubled neodymium laser). We subsequently performed phase-stepping shearography experiments using these lasers as light sources. We found a correlation with the quality of the shearograms and the power stability of the lasers. This enables us to compare the suitability of various lasers for phase-stepping shearography experiments and to choose the best laser for this application.
Knowledge sharing-based multi-block federated learning for few-shot oil layer identification

Oil Layer Identification (OLI) plays a significant role in petroleum development, but is unavoidably negatively affected by small samples, non-reservoirs interference, and layer classes imbalance. Existing transfer learning methods partially address the problem of insufficient samples in OLI. Nevertheless, they ignore the geological differences between blocks. This paper introduces Multi-Block Federated Learning (MBFL) to train a generalized pre-trained model, which consists of a Mask Attention Network (MAN), Class Balance Module (CBM), and Dynamic Weighted Fusion (DWF). MBFL uses MAN to dynamically avoid non-reservoir interference while learning the relationship between reservoirs and non-reservoirs; MBFL uses CBM to overcome layer classes imbalance and uses DWF to optimize traditional federated learning for addressing geological differences among blocks. Experimental results demonstrate that MBFL achieves an average accuracy of 89.32%, and an average F1 score of 81.74% with a real-world dataset. The results of two public datasets demonstrate the generalization of MBFL.
Learning from the global view: Supervised contrastive learning of multimodal representation

The development of technology enables the availability of abundant multimodal data, which can be utilized in many representation learning tasks. However, most methods ignore the rich modality correlation information stored in each multimodal object and fail to fully exploit the potential of multimodal data. To address the aforementioned issue, cross-modal contrastive learning methods are proposed to learn the similarity score of each modality pair in a self-/weakly-supervised manner and improve the model robustness. Though effective, contrastive learning based on unimodal representations might be, in some cases, inaccurate as unimodal representations fail to reveal the global information of multimodal objects. To this end, we propose a contrastive learning pipeline based on multimodal representations to learn from the global view, and devise multiple techniques to generate negative and positive samples for each anchor. To generate positive samples, we apply the mix-up operation to mix two multimodal representations of different objects that have the maximal label similarity. Moreover, we devise a permutation-invariant fusion mechanism to define the positive samples by permuting the input order of modalities for fusion and sampling various contrastive fusion networks. In this way, we force the multimodal representation to be invariant regarding the order of modalities and the structures of fusion networks, so that the model can capture high-level semantic information of multimodal objects. To define negative samples, for each modality, we randomly replace the unimodal representation with that from another dissimilar object when synthesizing the multimodal representation. By this means, the model is led to capture the high-level concurrence information and correspondence relationship between modalities within each object. We also directly define the multimodal representation from another object as a negative sample, where the chosen object shares the minimal label similarity with the anchor. The label information is leveraged in the proposed framework to learn a more discriminative multimodal embedding space for downstream tasks. Extensive experiments demonstrate that our method outperforms previous state-of-the-art baselines on the tasks of multimodal sentiment analysis and humor detection.
Solving the Multi-robot task allocation with functional tasks based on a hyper-heuristic algorithm

Multi-robot task allocation (MRTA) problem is a classical problem in multi-robot systems. The most common assumption in MRTA is that all the tasks need to be completed with the time cost as small as possible. It is worth noting that some tasks may be optional in some real-world situations, i.e., the robots do not necessarily need to complete all tasks. These optional tasks do not limit the achievement of the goal, but the completion of these tasks will lead to some functional effects (e.g., making other tasks easier to be completed). Therefore, these optional tasks can be called “functional task”. Different from functional tasks, if a task must be completed, this task can be called “compulsory task”. In this paper, we study the problem where the robots need to minimize the time cost of completing all compulsory tasks and can selectively complete some functional tasks. The existence of the functional tasks greatly increases the solution space of MRTA, because the functional tasks should be firstly suitably selected and then suitably allocated. Existing related algorithms are usually based on the assumption that all tasks must be allocated. Thus, these algorithms cannot suitably deal with functional tasks. We analyze the characteristics of this problem and present a new hyper-heuristic algorithm. The low-level heuristic (LLH) in hyper-heuristic is designed to score the functional tasks by using influence diffusion model. The high-level strategy (HLS) seeks the optimal values of the key parameters in the influence diffusion model based on particle swarm optimization (PSO). Extensive simulated experiments are presented to comprehensively analyze the proposed algorithm. The proposed hyper-heuristic is compared with greedy algorithm and two meta-heuristic algorithms. Based on the simulated data, it is known that the hyper-heuristic algorithm can outperform the benchmark algorithms especially when the number of functional tasks is large.
TSGS: Two-stage security game solution based on deep reinforcement learning for Internet of Things

The lack of effective defense resource allocation strategies and reliable multi-agent collaboration mechanisms lead to the low stability of Deep Reinforcement Learning (DRL)-based security defense strategies in several Internet of Things (IoT) applications. To address the aforementioned issues and approach real-world scenarios, we construct a grid-based adversarial security scenario, propose a two-stage zero-sum security game model (including the resource allocation stage and the patrolling detection stage), and design a two-stage security game solution algorithm based on DRL for this game model, named TSGS. In the resource allocation stage, TSGS uses auxiliary action embedding and gradient approximation approach to compute the Nash Equilibrium (NE) allocation strategy, which addresses the problem of unreasonable allocation. In the patrolling detection stage, TSGS achieves team collaboration by training a multi-agent Dueling Deep-Q Network under the centralized training and decentralized execution (CTDE) framework, which solves the cooperation problem among multiple defense agents. In addition, we design and implement a multi-parameterized attacker model to make the attacker’s behaviors more realistic in the game. Finally, the validity of the TSGS is verified by detailed experimental results for several adversarial experimental scenarios. Compared with the baseline methods, the defense strategy learned by TSGS has higher utility and greater robustness. Especially, TSGS achieves efficient collaboration during the patrolling detection stage after resource allocation by making full use of real-time information and communication.
PA-Net: Plane Attention Network for real-time urban scene reconstruction

Traditional urban reconstruction methods can only output incomplete 3D models, which depict the scene regions that are visible to the moving camera. While learning-based shape reconstruction techniques make single-view 3D reconstruction possible, they are designed to handle single objects that are well-presented in the training datasets. This paper presents a novel learning-based approach for reconstructing complete 3D meshes for large-scale urban scenes in real-time. The input video sequences are fed into a localization module, which segments different objects and determines their relative positions. Each object is then reconstructed under their local coordinates to better approximate models in the training datasets. The reconstruction module is adopted from BSP-Net (Chen et al., 2020), which is capable of producing compact polygon meshes. However, major changes have been made so that unoriented objects in large-scale scenes can be reconstructed efficiently using only a small number of planes. Experimental results demonstrate that our approach can reconstruct urban scenes with buildings and vehicles using 400∼800 convex parts in 0.1∼0.5 s.
Artificial Intelligence Enabled IOT System for Football Identification in a Football Match

Due to growth of AI enabled IOT devices, their use in sports feature identification can be beneficial in terms of recognition quality. Computer Vision is a powerful tool for moving object recognition and tracking. Football identification and recognition problem can be implemented using OpenCV libraries with the help of a programming language like Java, Python etc. OpenCV has special modules that contain specific algorithms and features for moving object recognition and tracking. Internet of things provides a way to connect hardware components through software implementation. In the proposed model the automatic camera module has been implemented through IoT with the help of a servo motor and raspberry pi. The main aim of this paper is to automate the work of a cameraman in a football match by automatically tracking and recognizing the football and broadcasting it with the help of an automatic camera. The initial loss was about 1749, and after 1600+ epochs iterations, the loss reduced to 0.09 in the final model.
Innovative Technologies in the Educational Process Trends, Prospects for Development

This article examines the use of innovative technologies in the educational process, including virtual reality simulations, artificial intelligence algorithms, and other solutions. It explores their potential to revolutionize education worldwide and make it more accessible to students of all ages and backgrounds. The article also examines current trends in technology education and reflects on its future prospects. Finally, it provides recommendations for educators on how best to use these technologies in their classrooms.
Chinese Idiom Paraphrasing

Abstract Idioms are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chinese idioms are hard to be understood by children and non-native speakers. This study proposes a novel task, denoted as Chinese Idiom Paraphrasing (CIP). CIP aims to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning. Since the sentences without idioms are more easily handled by Chinese NLP systems, CIP can be used to pre-process Chinese datasets, thereby facilitating and improving the performance of Chinese NLP tasks, e.g., machine translation systems, Chinese idiom cloze, and Chinese idiom embeddings. In this study, we can treat the CIP task as a special paraphrase generation task. To circumvent difficulties in acquiring annotations, we first establish a large-scale CIP dataset based on human and machine collaboration, which consists of 115,529 sentence pairs. In addition to three sequence-to-sequence methods as the baselines, we further propose a novel infill-based approach based on text infilling. The results show that the proposed method has better performance than the baselines based on the established CIP dataset.
MENLI: Robust Evaluation Metrics from Natural Language Inference

Abstract Recently proposed BERT-based evaluation metrics for text generation perform well on standard benchmarks but are vulnerable to adversarial attacks, e.g., relating to information correctness. We argue that this stems (in part) from the fact that they are models of semantic similarity. In contrast, we develop evaluation metrics based on Natural Language Inference (NLI), which we deem a more appropriate modeling. We design a preference-based adversarial attack framework and show that our NLI based metrics are much more robust to the attacks than the recent BERT-based metrics. On standard benchmarks, our NLI based metrics outperform existing summarization metrics, but perform below SOTA MT metrics. However, when combining existing metrics with our NLI metrics, we obtain both higher adversarial robustness (15%–30%) and higher quality metrics as measured on standard benchmarks (+5% to 30%).
A participatory modelling approach to cognitive mapping of the socio-environmental system of sandy anthropogenic shores in the Netherlands

Sandy Anthropogenic Shores (SAS) are coasts formed or heavily modified by moving large amounts of dredged sand from offshore towards the land. Subsequently, natural processes such as waves, wind, and currents are spreading the sand, where wind can transport sand into the foredune area and reinforce the foredune for long-term coastal safety. Besides improving safety, more expansive beaches and artificial lagoons, which may be part of a SAS design, provide new space for human activities and animal and plant habitats. The landscape of SAS is influenced by humans who manage and utilise the shore for various objectives, including flood safety, recreation, and ecological functions. Consequently, the sustainable management of multifunctional and multi-actor systems like SAS becomes challenging and requires an inter- and transdisciplinary approach to analyse the trade-offs between various socio-environmental functions. Although numerous studies of SAS have been conducted, they have had a sectoral, disciplinary approach and mainly focused on the natural aspects of SAS. In this study, we applied participatory modelling to explore the role of social systems (management activities and their interactions with natural systems) in SAS to broaden insight beyond natural systems and improve management and social acceptance of managerial decisions. We conducted several interviews and workshops with multiple stakeholders. We discussed different management aims and activities in SAS, the essential socio-environmental drivers of the managerial activities, their interactions, and the managerial indicators for analysing the various SAS functions. The elicited knowledge was structured in the form of a collective cognitive map (CCM) developed with stakeholders. We used the designed cognitive map for a qualitative assessment of the influence of some managerial decisions on trade-offs between the multiple functions of SAS. The developed CCM provides suitable ground to start the discussion with multiple stakeholders on the design of SAS as a new coastal management approach.
A Case for Synthetic Data in Regulatory Decision‐Making in Europe

Regulators are faced with many challenges surrounding health data usage, including privacy, fragmentation, validity, and generalizability, especially in the European Union, for which synthetic data may provide innovative solutions. Synthetic data, defined as data artificially generated rather than captured in the real world, are increasingly being used for healthcare research purposes as a proxy to real-world data (RWD). Currently, there are barriers particularly challenging in Europe, where sharing patient's data is strictly regulated, costly, and time-consuming, causing delays in evidence generation and regulatory approvals. Recent initiatives are encouraging the use of synthetic data in regulatory decision making and health technology assessment to overcome these challenges, but synthetic data have still to overcome realistic obstacles before their adoption by researchers and regulators in Europe. Thus, the emerging use of RWD and synthetic data by pharmaceutical and medical device industries calls regulatory bodies to provide a framework for proper evidence generation and informed regulatory decision making. As the provision of data becomes more ubiquitous in scientific research, so will innovations in artificial intelligence, machine learning, and generation of synthetic data, making the exploration and intricacies of this topic all the more important and timely. In this review, we discuss the potential merits and challenges of synthetic data in the context of decision making in the European regulatory environment. We explore the current uses of synthetic data and ongoing initiatives, the value of synthetic data for regulatory purposes, and realistic barriers to the adoption of synthetic data in healthcare.