Reviewerly/exHarmony huggingface dataset:
Self-Supervised Blind Image Deconvolution via Deep Generative Ensemble Learning

Blind image deconvolution (BID) is about recovering a latent image with sharp details from its blurred observation generated by the convolution with an unknown smoothing kernel. Recently, deep generative priors from untrained neural networks (NNs) have emerged as a promising deep learning approach for BID, with the benefit of being free of external training samples. However, existing untrained-NN-based BID methods may suffer from under-deblurring or overfitting. In this paper, we propose an ensemble approach to better exploit the priors from untrained NNs for BID, which aggregates the deblurring results of multiple untrained NNs for improvement. To enjoy both the effectiveness and computational efficiency in ensemble learning, the untrained NNs are designed with a specific shared-base and multi-head architecture. In addition, a kernel-centering layer is proposed for handling the shift ambiguity among different predictions during ensemble, which also improves the robustness of kernel prediction to the setting of the kernel size parameter. Extensive experiments show that the proposed approach noticeably outperforms both exiting dataset-free methods and dataset-based methods.
Siamese-Based Twin Attention Network for Visual Tracking

Recently, object tracking have achieved remarkable progress in terms of both efficiency and accuracy. However, exiting methods still cannot satisfy challenging tasks under complicated scenarios, such as occlusion, scale variations, and etc. To this end, we propose a novel Siamese-based Twin Attention Network for visual tracking. First, a multi-branch fusion module is presented. By leveaging the fusion scheme, we merge the low-level features with the high-level features extracted from different convolution layers. Then, the representation ability of the target can be enhanced effectively. Second, to fully capture the contextual information in the tracking process, we introduced a global context module into the search branch. Third, to attain robust performance, a saliency mine scheme is employed in the proposed network. Specifically, the self-attention operation is utilized to capture the contextual information from the spatial and channel domain, while the cross-attention operation is to enrich the contextual information relevance by fusing the features between the template and search region. By utilizing these schemes, our tracker can cope well with different challenging scenes. Extensive experiments were conducted on several popular benchmarks, including VOT2016, VOT2018, VOT2019, VOT2021, OTB2013, OTB2015, GOT10k, LaSOT, and NFS. The results demonstrate that the proposed method is effective and achieves competitive results.
A Lightweight and Accurate UAV Detection Method Based on YOLOv4

At present, the UAV (Unmanned Aerial Vehicle) has been widely used both in civilian and military fields. Most of the current object detection algorithms used to detect UAVs require more parameters, and it is difficult to achieve real-time performance. In order to solve this problem while ensuring a high accuracy rate, we further lighten the model and reduce the number of parameters of the model. This paper proposes an accurate and lightweight UAV detection model based on YOLOv4. To verify the effectiveness of this model, we made a UAV dataset, which contains four types of UAVs and 20,365 images. Through comparative experiments and optimization of existing deep learning and object detection algorithms, we found a lightweight model to achieve an efficient and accurate rapid detection of UAVs. First, from the comparison of the one-stage method and the two-stage method, it is concluded that the one-stage method has better real-time performance and considerable accuracy in detecting UAVs. Then, we further compared the one-stage methods. In particular, for YOLOv4, we replaced MobileNet with its backbone network, modified the feature extraction network, and replaced standard convolution with depth-wise separable convolution, which greatly reduced the parameters and realized 82 FPS and 93.52% mAP while ensuring high accuracy and taking into account the real-time performance.
The effectiveness of android-based budiran game assisted by smart apps creator 3 to improve science learning outcomes of fourth graders in theme 1

Educational games are learning educational tools, where these tools can encourage students to think creatively and perform actions with fellow students by playing games in a learning activity. This research aims to analyze the effectiveness of an android-based budiran game assisted by SAC3 to improve science learning outcomes of fourth-graders in theme 1. Budiran stands for bunyi dan indra pendengaran. This game is educative in the form of an android-based game made by SAC3. It was developed with Borg &amp; Gall's R&amp;D model, adapted from Sugiyono with seven stages. The game's effectiveness in the fourth graders' science learning outcome was done with pretest and posttest control group design. The researchers took the experimental group of fourth-graders from Public Primary School Bendanpete 2 and Public Primary School Ngetuk 3. Then, the researcher took fourth-graders from Public Primary School 1 and 3 Bendanpete as the control group. The researcher obtained the data from the test. The data analysis included an n-gain test and t-test with a significant level of 0.05. The results showed improvement in the posttest score of the experimental group, higher than the control group's posttest score. The N-gain posttest of the experimental group is 64%, categorized as moderate. The t-test score on the posttest obtained t-count = 8.803, with significant level 0.000 &lt; ɑ with ɑ = 0.05. Thus, the developed game effectively improved the science learning outcomes of fourth-graders on theme 1.
A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors

Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compile quality-aware features since they treat images from the human visual system's point of view. To prove the efficiency of the proposed method, it was compared to 16 state-of-the-art NR-IQA techniques on five large benchmark databases, i.e., CLIVE, KonIQ-10k, SPAQ, TID2013, and KADID-10k. It was demonstrated that the proposed method is superior to the state-of-the-art in terms of three different performance indices.
GRAPE: Grammatical Algorithms in Python for Evolution

GRAPE is an implementation of Grammatical Evolution (GE) in DEAP, an Evolutionary Computation framework in Python, which consists of the necessary classes and functions to evolve a population of grammar-based solutions, while reporting essential measures. This tool was developed at the Bio-computing and Developmental Systems (BDS) Research Group, the birthplace of GE, as an easy to use (compared to the canonical C++ implementation, libGE) tool that inherits all the advantages of DEAP, such as selection methods, parallelism and multiple search techniques, all of which can be used with GRAPE. In this paper, we address some problems to exemplify the use of GRAPE and to perform a comparison with PonyGE2, an existing implementation of GE in Python. The results show that GRAPE has a similar performance, but is able to avail of all the extra facilities and functionality found in the DEAP framework. We further show that GRAPE enables GE to be applied to systems identification problems and we demonstrate this on two benchmark problems.
Can a Machine Generate a Meta-Review? How Far Are We?

A meta-review usually written by the editor of a journal or the area/program chair in a conference is a summary of the peer-reviews and a concise interpretation of the editors/chairs decision. Although the task closely simulates a multi-document summarization problem, automatically writing reviews on top of human-generated reviews is something very less explored. In this paper, we investigate how current state-of-the-art summarization techniques fare on this problem. We come up with qualitative and quantitative evaluation of four radically different summarization approaches on the current problem. We explore how the summarization models perform on preserving aspects and sentiments in original peer reviews and meta-reviews. Finally, we conclude with our observations on why the task is challenging, different from simple summarization, and how one should approach to design a meta-review generation model. We have provided link for our git repository https://github.com/PrabhatkrBharti/MetaGen.git so as to enable readers to replicate the findings.
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification

A large-scale labeled dataset is a key factor for the success of supervised deep learning in computer vision. However, a limited number of annotated data is very common, especially in ophthalmic image analysis, since manual annotation is time-consuming and labor-intensive. Self-supervised learning (SSL) methods bring huge opportunities for better utilizing unlabeled data, as they do not need massive annotations. With an attempt to use as many as possible unlabeled ophthalmic images, it is necessary to break the dimension barrier, simultaneously making use of both 2D and 3D images. In this paper, we propose a universal self-supervised Transformer framework, named Uni4Eye, to discover the inherent image property and capture domain-specific feature embedding in ophthalmic images. Uni4Eye can serve as a global feature extractor, which builds its basis on a Masked Image Modeling task with a Vision Transformer (ViT) architecture. We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images. Besides, we design a dual-branch multitask decoder module to simultaneously perform two reconstruction tasks on the input image and its gradient map, delivering discriminative representations for better convergence. We evaluate the performance of our pre-trained Uni4Eye encoder by fine-tuning it on six downstream ophthalmic image classification tasks. The superiority of Uni4Eye is successfully established through comparisons to other state-of-the-art SSL pre-training methods.
Efficient Bayesian Uncertainty Estimation for nnU-Net

The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong baseline for medical image segmentation. However, despite its extraordinary performance, nnU-Net does not supply a measure of uncertainty to indicate its possible failure. This can be problematic for large-scale image segmentation applications, where data are heterogeneous and nnU-Net may fail without notice. In this work, we introduce a novel method to estimate nnU-Net uncertainty for medical image segmentation. We propose a highly effective scheme for posterior sampling of weight space for Bayesian uncertainty estimation. Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use. Additionally, we boost the segmentation performance over the original nnU-Net via marginalizing multi-modal posterior models. We applied our method on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved uncertainty estimation over a range of baseline methods. The proposed method further strengthens nnU-Net for medical image segmentation in terms of both segmentation accuracy and quality control.
Security Best Practices: A Critical Analysis Using IoT as a Case Study

Academic research has highlighted the failure of many Internet of Things (IoT) product manufacturers to follow accepted practices, while IoT security best practices have recently attracted considerable attention worldwide from industry and governments. Given current examples of security advice, confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. We explore a surprising lack of clarity, and void in the literature, on what (generically) best practice means, independent of identifying specific individual practices or highlighting failure to follow best practices. We consider categories of security advice, and analyze how they apply over the lifecycle of IoT devices. For concreteness in discussion, we use iterative inductive coding to code and systematically analyze a set of 1,013 IoT security best practices, recommendations, and guidelines collated from industrial, government, and academic sources. Among our findings, of all analyzed items, 68% fail to meet our definition of an (actionable) practice, and 73% of all actionable advice relates to the software development lifecycle phase, highlighting the critical position of manufacturers and developers. We hope that our work provides a basis for the community to better understand best practices, identify and reach consensus on specific practices, and find ways to motivate relevant stakeholders to follow them.
Fisher-Pitman Permutation Tests Based on Nonparametric Poisson Mixtures with Application to Single Cell Genomics

This article investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to adapt to complicated unspecified structures of count data and also consistent against their corresponding ANOVA-type alternatives; the latter is a result in parallel to classic claims made by Robinson. The studied methods are then applied to a single-cell RNA-seq data obtained from different cell types from brain samples of autism subjects and healthy controls; empirically, they unveil genes that are differentially expressed between autism and control subjects yet are missed using common tests. For justifying their use, rate optimality of NPMLEs is also established in settings similar to nonparametric Gaussian (Wu and Yang) and binomial mixtures (Tian, Kong, and Valiant; Vinayak et al.). Supplementary materials for this article are available online.
On the current state of information, computer and telecommunication support of the educational process of higher education in the Russian Federation

Over the past few years, the processes of digitalization have covered all the main areas of the modern state and society. In modern Russian science, many publications have been devoted to the political, social, legal and economic issues of the introduction of up-to-date information and communication technologies and digitalization, and a number of thesis papers have been defended. However, the topic continues to increase its relevance due to the new challenges. Apparently, since March 2020, after the introduction of lockdown in Russia, the Covid-19 pandemic has changed the tools for the implementation of educational services in the field of higher education forever. The mass introduction of distance education into the practice of teaching in higher educational institutions in Russia in the spring of 2020 has shown its advantages and disadvantages, especially for humanitarian areas of training and correspondence/part-time education.
From Distortion Manifold to Perceptual Quality: a Data Efficient Blind Image Quality Assessment Approach

Though current no-reference image quality assessment (NR-IQA) approaches have achieved impressive performance gain thanks to deep learning techniques, it is claimed that the risk of over-fitting exists. To improve model generalization ability, most of the current researches incorporate mass data to train or tune the data-driven models. However, the process of image data collection and quality label annotation is quite time-consuming and labour-intensive. Therefore, in this paper, we explore an alternative solution to promote model generalizability but with relatively small fractions of training data. Compared with previous approaches which make effort to approximate the whole complex image distribution, we propose to explicitly learn an image distortion manifold first, which lies in a much lower dimension space and also representative in capturing general degradation patterns. We then project the images to their perceived quality from the learned manifold to obtain quality predictions. Since the manifold embeds general distortion features despite of varying image contents, it can be learned with relatively small amount of samples. In order to learn the manifold and quality projection, we introduce a two-branched network to learn both low level distortions and high level semantics. We also propose a simple but efficient training framework, composing of a masked labelling strategy and a gradual weighting curriculum to fulfill the task. Thanks to the learned distortion manifold, the proposed model achieves superior generalizability compared with previous models. Extensive experiments demonstrate its effectiveness in terms of training with limited data, testing on large scale images, and with unseen types of distorted images.
Skeleton-based deep pose feature learning for action quality assessment on figure skating videos

Most of the existing Action Quality Assessment (AQA) methods for scoring sports videos have deeply researched how to evaluate the single action or several sequential-defined actions that performed in short-term sport videos, such as diving, vault, etc. They attempted to extract features directly from RGB videos through 3D ConvNets, which makes the features mixed with ambiguous scene information. To investigate the effectiveness of deep pose feature learning on automatically evaluating the complicated activities in long-duration sports videos, such as figure skating and artistic gymnastic, we propose a skeleton-based deep pose feature learning method to address this problem. For pose feature extraction, a spatial–temporal pose extraction module (STPE) is built to capture the subtle changes of human body movements and obtain the detail representations for skeletal data in space and time dimensions. For temporal information representation, an inter-action temporal relation extraction module (ATRE) is implemented by recurrent neural network to model the dynamic temporal structure of skeletal subsequences. We evaluate the proposed method on figure skating activity of MIT-skate and FIS-V datasets. The experimental results show that the proposed method is more effective than RGB video-based deep feature learning methods, including SENet and C3D. Significant performance progress has been achieved for the Spearman Rank Correlation (SRC) on MIT-Skate dataset. On FIS-V dataset, for the Total Element Score (TES) and the Program Component Score (PCS), better SRC and MSE have been achieved between the predicted scores against the judge's ones when compared with SENet and C3D feature methods.
Improved semi-quantum key distribution with two almost-classical users

Semi-quantum key distribution (SQKD) protocols attempt to establish a shared secret key between users, secure against computationally unbounded adversaries. Unlike standard quantum key distribution protocols, SQKD protocols contain at least one user who is limited in their quantum abilities and is almost “classical” in nature. In this paper, we revisit a mediated semi-quantum key distribution protocol, introduced by Massa et al. (Experimental quantum cryptography with classical users, 2019. arXiv preprint arXiv:1908.01780 ), where users need only the ability to detect a qubit, or reflect a qubit; they do not need to perform any other basis measurement; nor do they need to prepare quantum signals. Users require the services of a quantum server which may be controlled by the adversary. In this paper, we show how this protocol may be extended to improve its efficiency and also its noise tolerance. We discuss an extension which allows more communication rounds to be directly usable; we analyze the key-rate of this extension in the asymptotic scenario for a particular class of attacks and compare with prior work. Finally, we evaluate the protocol’s performance in a variety of lossy and noisy channels.
CNN-based first quantization estimation of double compressed JPEG images

Multiple JPEG compressions leave artifacts in digital images: residual traces that could be exploited in forensics investigations to recover information about the device employed for acquisition or image editing software. In this paper, a novel First Quantization Estimation (FQE) algorithm based on convolutional neural networks (CNNs) is proposed. In particular, a solution based on an ensemble of CNNs was developed in conjunction with specific regularization strategies exploiting assumptions about neighboring element values of the quantization matrix to be inferred. Mostly designed to work in the aligned case, the solution was tested in challenging scenarios involving different input patch sizes, quantization matrices (both standard and custom) and datasets (i.e., RAISE and UCID collections). Comparisons with state-of-the-art solutions confirmed the effectiveness of the presented solution demonstrating for the first time to cover the widest combinations of parameters of double JPEG compressions.
Modeling Horizontal Single Axis Solar Tracker Upon Sun-Earth Geometric Relationships

Many investments and commitments have recently been set to use renewable energy source, overcome energy crisis and align with climate target. Solar power development and deployment make investment in power generation sustainability. The goal of this study is harvesting energy by rotating solar panel toward the sun direction. Astronomical formula is derivate to calculate the sun altitude and azimuth depending on given latitude, longitude coordination. The photovoltaic (PV) panels rotate horizontally and track the sun direction in 9 positions regarding to their actual time and calculated azimuth angle. Partial shaded effectiveness that produces between the adjacent panels due to PV panel’s inclination is calculate accordingly. The total increment of power production from fix to tracked panel structure is 17.3% per day. The extra power generation is distributed over the period between solar noon times.