Reviewerly/exHarmony huggingface dataset:
Continuous-variable entanglement dynamics in Lorentzian environment

We address the non-Markovian entanglement dynamics for bimodal continuous variable quantum systems interacting with two independent structured reservoirs. We derive an analytical expression for the entanglement of formation without performing the Markov and the secular approximations. We observe a variety of qualitative features such as entanglement sudden death, dynamical generation, and protection for two types of Lorentzian spectral densities, assuming the two modes initially excited in a twin-beam state. Our quantitative analysis shows that these cases with different reservoir spectrum, the environmental temperature and the initial amount of entanglement differ significantly in these qualitative features.
Enabling Secure Data Exchange through the IOTA Tangle for IoT Constrained Devices

Internet-of-Things (IoT) and sensor technologies have enabled the collection of data in a distributed fashion for analysis and evidence-based decision making. However, security concerns regarding the source, confidentiality and integrity of the data arise. The most common method of protecting data transmission in sensor systems is Transport Layer Security (TLS) or its datagram counterpart (DTLS) today, but exist an alternative option based on Distributed Ledger Technology (DLT) that promise strong security, ease of use and potential for large scale integration of heterogeneous sensor systems. A DLT such as the IOTA Tangle offers great potential to improve sensor data exchange. This paper presents L2Sec, a cryptographic protocol which is able to secure data exchanged over the IOTA Tangle. This protocol is suitable for implementation on constrained devices, such as common IoT devices, leading to greater scalability. The first experimental results evidence the effectiveness of the approach and advocate for the integration of an hardware secure element to improve the overall security of the protocol. The L2Sec source code is released as open source repository on GitHub.
Dataset of Indian and Thai banknotes with annotations

Multinational banknote detection in real time environment is the open research problem for the research community. Several studies have been conducted for providing solution for fast and accurate recognition of banknotes, detection of counterfeit banknotes, and identification of damaged banknotes. The State-of art techniques like machine learning (ML) and deep learning (DL) are dominating the traditional methods of digital image processing technique used for banknote classification. The success of the ML or DL projects heavily depends on size and comprehensiveness of dataset used. The available datasets have the following limitations: 1. The size of existing Indian dataset is insufficient to train ML or DL projects [1], [2]. 2. The existing dataset fail to cover all denomination classes [1]. 3. The existing dataset does not consists of latest denomination [3]. 4. As per the literature survey there is no public open access dataset is available for Thai banknotes. To overcome all these limitations we have created a total 3000 image dataset of Indian and Thai banknotes which include 2000 images of Indian banknotes and 1000 images of Thai banknotes. Indian banknotes consist of old and new banknotes of 10, 20, 50, 100, 200, 500 and 2000 rupees and Thai banknotes consist of 20, 50, 100, 500 and 1000 Baht.
Robust color image watermarking using multiple fractional-order moments and chaotic map

Abstract Robust watermarking is an effective method and a promising solution for securing and protecting the copyright of digital images. Moments and moment invariants have become popular tools for robust watermarking due to their geometric invariance and favorable capability of image description. Many moments-based robust watermarking schemes have been proposed. However, there is a challenging problem of these schemes that should be addressed. One of these problems is to improve both imperceptibility and robustness. In contrast, the other problem, most of these schemes used inefficient, traditional computation methods of the moments, resulting in an inaccurate and inefficient performance of the watermarking schemes. To overcome these challenges, in this paper, we propose a novel robust color image-watermarking algorithm based on new multiple fractional multi-channel orthogonal moments, fractional-order exponent moments (MFrEMs), fractional-order polar harmonic transforms (MFrPHTs), and fractional-order radial harmonic Fourier moments (MFrRHFMs). Firstly, highly accurate fractional new multi-channel orthogonal moments are computed for the host color images. Then, more stable and accurate coefficients of fractional new multi-channel orthogonal moments are selected. Finally, a robust color image watermarking approach for multiple watermarks images is proposed based on MFrEMs, MFrPHTs, and MFrRHFMs using a 1D Sine chaotic map. The experimental results demonstrate that the proposed approach provides robustness against various attacks and better imperceptibility than the existing methods.
Automated Diagnosis of Cervical Intraepithelial Neoplasia in Histology Images via Deep Learning

Artificial intelligence has enabled the automated diagnosis of several cancer types. We aimed to develop and validate deep learning models that automatically classify cervical intraepithelial neoplasia (CIN) based on histological images. Microscopic images of CIN3, CIN2, CIN1, and non-neoplasm were obtained. The performances of two pre-trained convolutional neural network (CNN) models adopting DenseNet-161 and EfficientNet-B7 architectures were evaluated and compared with those of pathologists. The dataset comprised 1106 images from 588 patients; images of 10% of patients were included in the test dataset. The mean accuracies for the four-class classification were 88.5% (95% confidence interval [CI], 86.3-90.6%) by DenseNet-161 and 89.5% (95% CI, 83.3-95.7%) by EfficientNet-B7, which were similar to human performance (93.2% and 89.7%). The mean per-class area under the receiver operating characteristic curve values by EfficientNet-B7 were 0.996, 0.990, 0.971, and 0.956 in the non-neoplasm, CIN3, CIN1, and CIN2 groups, respectively. The class activation map detected the diagnostic area for CIN lesions. In the three-class classification of CIN2 and CIN3 as one group, the mean accuracies of DenseNet-161 and EfficientNet-B7 increased to 91.4% (95% CI, 88.8-94.0%), and 92.6% (95% CI, 90.4-94.9%), respectively. CNN-based deep learning is a promising tool for diagnosing CIN lesions on digital histological images.
Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images

Bosniak renal cyst classification has been widely used in determining the complexity of a renal cyst. However, it turns out that about half of patients undergoing surgery for Bosniak category III, take surgical risks that reward them with no clinical benefit at all. This is because their pathological results reveal that the cysts are actually benign not malignant. This problem inspires us to use recently popular deep learning techniques and study alternative analytics methods for precise binary classification (benign or malignant tumor) on Computerized Tomography (CT) images. To achieve our goal, two consecutive steps are required-segmenting kidney organs or lesions from CT images then classifying the segmented kidneys. In this paper, we propose a study of kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for efficiently extracting intra-slice and inter-slice features. Our models are trained and validated on the public data set from Kidney Tumor Segmentation (KiTS19) challenge in two different training environments. As a result, all experimental models achieve high mean kidney Dice scores of at least 95% on the KiTS19 validation set consisting of 60 patients. Apart from the KiTS19 data set, we also conduct separate experiments on abdomen CT images of four Thai patients. Based on the four Thai patients, our experimental models show a drop in performance, where the best mean kidney Dice score is 87.60%.
Many oil wells, one evil: comprehensive assessment of toxic metals concentration, seasonal variation and human health risk in drinking water quality in areas surrounding crude oil exploration facilities in rivers state, Nigeria

Background: Oil and natural gas extraction have produced environmental pollution at levels that affect reproductive health of indigenous populations. Accordingly, polluted drinking water from physical, chemical and heavy metals can result in serious health problems, like anemia, kidney failure, immunosuppression, neurological impairments, gastrointestinal as well as respiratory irritation, skeletal system abnormalities, liver inflammation, liver cancer, cardiovascular diseases after chronic exposure and other cancer diseases with negative health effects. These diseases types remain associated to high amounts of heavy metal elements such as lead, chromium, zinc, copper, cadmium, manganese as well as nickel etc. Objectives: Compare differences in water quality parameters in the study area (determine the level of pollutions in the different sites). Methodology: The investigation made use of standard analytical methodologies. All sampling, conservation, transportation as well as analysis followed the usual APHA procedures (2012). To prevent degradation of the organic substances, all obtained samples were transferred to the laboratory, while keeping in an icebox. Results: Result shows that during wet season, the mean values obtained for water quality parameters were significantly lower in site 9 compared with that obtained in other sites (p&lt;0.05) with the exemptions of temperature, DO, BOD, COD, acidity, TH, TDS, K, Mg, Zn, Mn, Cd, Pb, Cu, Cr, NH3, NO2, NO3, Ni though slightly lower in most cases in site 9 were not significantly different (p&gt;0.05) and both alkalinity and SO4 which were significantly higher in site 9 than site 1 (p&lt;0.05). Result obtained during dry season reveals that there is no remarkable difference in pH, acidity, Pb and Ni between the nine sites (p&gt;0.05) while other water quality parameters were significantly lower in site 9 than other sites excluding Cl and Mg which were both significantly higher in site 9 than site 8 (p&lt;0.05). Conclusion: To guarantee quality groundwater supply for various purposes in Nigeria's core Niger Delta region, extra efforts must be taken to fully understand hydrogeochemical features and its suitability. Thus, this study will aid in the development of a quantitative understanding of the effects of diverse causes on groundwater level fluctuations in any aquifer around the world. Also, this analysis reinforces a valuable resource for researchers, activists and public officials seeking to help enhance community awareness, planning and performance. The verdicts would remain a valuable guideline for policymakers, the Ministry of Water Resources and development practitioners, as this highlights the requirement for suitable approaches toward mitigating toxic element of water resources contamination in the core Niger Delta toward safeguarding health of the public from carcinogenic as well as non-carcinogenic risks.
Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Secondly, the cloud model is used to quantitatively describe the natural language and uncertainty in a large amount of assessment information. The cloud model generator is used for sub-indicators and achieves an effective and flexible conversion between linguistic information and quantitative values. The cloud model of indicators are presented by integrating the corresponding sub-indicators. The weights of indicators are determined by the entropy method based on the cloud model and possibility degree matrix, which eliminates the judgment of decision-makers and has great power for handling practical problems with unknown weight information. Finally, a fuzzy TOPSIS method based on the cloud model is proposed to rank the satisfaction of online education of different colleges. The proposed method is compared with other existing methods to shown its merits. The experimental result is consistent with the proportion of students who accept online education in the post-epidemic era. According to the second questionnaire, as the qualitative evaluation of the cloud model of indicators increases, the qualitative evaluation of satisfaction of different types of colleges will also increase. It indicates that the method proposed in this paper is practical.
Resampling algorithms based on sample concatenation for imbalance learning

Resampling is the widely used method for imbalance learning. Most existing resampling methods use various techniques in the original sample space to rebalance imbalanced datasets, but they may cause loss of valuable information or aggravate interclass overlap. In this paper, sample concatenation, i.e., concatenating two samples with the same labels into one sample, is introduced into imbalance learning, and a resampling algorithm based on sample concatenation (Re-SC) is proposed. Re-SC transforms an imbalanced training dataset in the original sample space into a concatenated dataset in a new sample space. In the transformation process, Re-SC considers both the distribution of the original dataset and that of the majority samples, thereby alleviating the loss of valuable samples and reducing the class overlapping region. Furthermore, an ensemble resampling algorithm based on sample concatenation (EnRe-SC) for imbalanced data is also proposed. EnRe-SC can reduce the negative effect of removing part of the samples from the majority class. Experiments were conducted on UCI and KEEL imbalanced datasets to evaluate the performance of the proposed methods. The results verify the effectiveness of the proposed methods.
Visual-semantic graph neural network with pose-position attentive learning for group activity recognition

Video-based group activities typically contain interactive contexts among diverse visual modalities between multiple persons, and semantic relationships between individual actions. Nevertheless, majority of the existing methods for recognizing group activity either captures the relationships among different persons by utilizing a solely RGB modality or neglect to exploit the label hierarchies between individual actions and the group activity. To tackle these issues, we propose a visual-semantic graph neural network, with pose-position attentive learning (VSGNN-PAL), for group activity recognition. Specifically, we first extract the individual-level appearance and motion representations from RGB and optical-flow inputs, to build a bi-modal visual graph. Two attentive aggregators are further proposed to integrate both the pose and position information to measure the relevance scores between persons, and dynamically refine the representation of each visual node from both modality-specific and cross-modal perspectives. To model a semantic hierarchy from a label space, we construct a semantic graph based on the linguistic embeddings of individual actions and group activity labels. We further employ a bi-directional mapping learning scheme, to integrate the label-relation-aware semantic context into the visual representations. Besides, a global reasoning module is introduced to progressively generate the group-level representations with the scene description maintained. Furthermore, we formulate a semantic-preserving loss, to maintain the consistency between the learned high-level representations and the semantics of the ground-truth labels. Experimental results on three group activity benchmarks demonstrate that the proposed method achieves state-of-the-art performance.
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels

Noisy training set usually leads to the degradation of generalization and robustness of neural networks. In this paper, we propose using a theoretically guaranteed noisy label detection framework to detect and remove noisy data for Learning with Noisy Labels (LNL). Specifically, we design a penalized regression to model the linear relation between network features and one-hot labels, where the noisy data are identified by the non-zero mean shift parameters solved in the regression model. To make the framework scalable to datasets that contain a large number of categories and training data, we propose a split algorithm to divide the whole training set into small pieces that can be solved by the penalized regression in parallel, leading to the Scalable Penalized Regression (SPR) framework. We provide the non-asymptotic probabilistic condition for SP R to correctly identify the noisy data. While SPR can be regarded as a sample selection module for standard supervised training pipeline, we further combine it with semi-supervised algorithm to further exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework. Our code and pretrained models are released at https://github.com/Yikai-Wang/SPR-LNL.
Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interacting variables and many wide and flat local basins. To alleviate this issue, this paper proposes a differential elite learning particle swarm optimization (DELPSO) by differentiating the two guiding exemplars as much as possible to direct the update of each particle. Specifically, in this optimizer, particles in the current swarm are divided into two groups, namely the elite group and non-elite group, based on their fitness. Then, particles in the non-elite group are updated by learning from those in the elite group, while particles in the elite group are not updated and directly enter the next generation. To comprise fast convergence and high diversity at the particle level, we let each particle in the non-elite group learn from two differential elites in the elite group. In this way, the learning effectiveness and the learning diversity of particles is expectedly improved to a large extent. To alleviate the sensitivity of the proposed DELPSO to the newly introduced parameters, dynamic adjustment strategies for parameters were further designed. With the above two main components, the proposed DELPSO is expected to compromise the search intensification and diversification well to explore and exploit the solution space properly to obtain promising performance. Extensive experiments conducted on the widely used CEC 2017 benchmark set with three different dimension sizes demonstrated that the proposed DELPSO achieves highly competitive or even much better performance than state-of-the-art PSO variants.
Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency

Solar energy has been receiving increasing attention due to the wide range of functionality it now supports and the promise of reduced environmental pollution. However, there are a few ongoing challenges, one of which relates to regular maintenance, especially for those high roof-mounted solar panels whose efficiency might have been degraded because of dust and dirt particle deposits. This paper is aimed at developing an autonomous drone which is equipped with a camera, a GPS sensor, a transceiver, and a pump conical tank to hold water with lotus effects, for the purpose of maintenance cleaning of solar panels which have been mounted on the roof top of buildings. The proposed system considers the combination of MobileNet and VGG-16 CNN techniques together for object detection and recognition that will enable assessment of solar panels, alongside the lotus effect physical technique that will enable maintenance of the solar panels. This would also put out of danger those human lives who have previously risked carrying out such high installation inspections and maintenance. A pump tank is designed and fabricated using a 3D printer to hold water with the lotus effect and is fitted on the drone. The proposed intelligent drone has been prototyped and deployed on-site at Taif University. The experimental results confirm the ability to detect dust and dirt particle deposits on solar panels while flying autonomously before proceeding to maintain them. The cleaning process with the use of the lotus effect has led to an increase in the power efficiency of the solar panels by approximately 31%, while the accuracy of the MobileNet framework for detecting particle deposits on the solar panels and thereafter making maintenance decisions rose to approximately 99%.
MobileFaceSwap: A Lightweight Framework for Video Face Swapping

Advanced face swapping methods have achieved appealing results. However, most of these methods have many parameters and computations, which makes it challenging to apply them in real-time applications or deploy them on edge devices like mobile phones. In this work, we propose a lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by dynamically adjusting the model parameters according to the identity information. In particular, we design an efficient Identity Injection Module (IIM) by introducing two dynamic neural network techniques, including the weights prediction and weights modulation. Once the IDN is updated, it can be applied to swap faces given any target image or video. The presented IDN contains only 0.50M parameters and needs 0.33G FLOPs per frame, making it capable for real-time video face swapping on mobile phones. In addition, we introduce a knowledge distillation-based method for stable training, and a loss reweighting module is employed to obtain better synthesized results. Finally, our method achieves comparable results with the teacher models and other state-of-the-art methods.
What senior academics can do to support reproducible and open research: a short, three-step guide

Increasingly, policies are being introduced to reward and recognise open research practices, while the adoption of such practices into research routines is being facilitated by many grassroots initiatives. However, despite this widespread endorsement and support, as well as various efforts led by early career researchers, open research is yet to be widely adopted. For open research to become the norm, initiatives should engage academics from all career stages, particularly senior academics (namely senior lecturers, readers, professors) given their routine involvement in determining the quality of research. Senior academics, however, face unique challenges in implementing policy changes and supporting grassroots initiatives. Given that-like all researchers-senior academics are motivated by self-interest, this paper lays out three feasible steps that senior academics can take to improve the quality and productivity of their research, that also serve to engender open research. These steps include changing (a) hiring criteria, (b) how scholarly outputs are credited, and (c) how we fund and publish in line with open research principles. The guidance we provide is accompanied by material for further reading.