Reviewerly/exHarmony huggingface dataset:
Learning to Predict Decomposed Dynamic Filters for Single Image Motion Deblurring

This paper tackles the large motion variation problem in the single image dynamic scene deblurring task. Although fully convolutional multi-scale-based designs have recently advanced the state-of-the-art in single image motion deblurring. However, these approaches usually utilize vanilla convolution filters, which are not adapted to each spatial position. Consequently, it is hard to handle large motion blur variations at the pixel level. In this work, we propose Decomposed Dynamic Filters (DDF), a highly effective plug-and-play adaptive operator, to fulfill the goal of handling large motion blur variations across different spatial locations. In contrast to conventional dynamic convolution-based methods, which only predict either weight or offsets of the filter from the local feature at run time, in our work, both the offsets and weight are adaptively predicted from multi-scale local regions. The proposed operator comprises two components: 1) the offsets estimation module and 2) the pixel-specific filter weight generator. We incorporate the DDF into a lightweight encoder-decoder-based deblurring architecture to verify the performance gain. Extensive experiments conducted on the GoPro, HIDE, Real Blur, SIDD, and DND datasets demonstrate that the proposed method offers significant improvements over the state-of-the-art in accuracy as well as generalization capability. Code is available at: https://github.com/ZHIQIANGHU2021/DecomposedDynamicFilters .
Truly Unsupervised Image-to-Image Translation with Contrastive Representation Learning

Image-to-image translation is a classic image generation task that attempts to translate an image from the source domain to an analogous image in the target domain. Recent advances in deep generative networks have shown remarkable capabilities in translating images among different domains. Most of these models either require pixel-level (with paired input and output images) or domain-level (with image domain labels) supervision to help the translation task. However, there are practical situations where the required supervisory information is difficult to collect and one would need to perform truly unsupervised image translation on a large number of images without paired image information or domain labels. In this paper, we present a truly unsupervised image-to-image translation model that performs the image translation task without any extra supervision. The crux of our model is an embedding network that extracts the domain and style information of the input style (or reference) image with contrastive representation learning and serves the translation module that actually carries out the translation task. The embedding network and the translation module can be integrated together for training and benefit from each other. Extensive experimental evaluation has been performed on various datasets concerning both cross-domain and multi-domain translation. The results demonstrate that our model outperforms the best truly unsupervised image-to-image translation model in the literature. In addition, our model can be easily adapted to take advantage of available domain labels to achieve a performance comparable to the best supervised image translation methods when all domain labels are known or a superior performance when only some domain labels are known.
DualBLN: Dual Branch LUT-Aware Network for Real-Time Image Retouching

The 3D Lookup Table (3D LUT) is an efficient tool for image retouching tasks, which models non-linear 3D color transformations by sparsely sampling them into a discrete 3D lattice. We propose DualBLN (Dual Branch LUT-aware Network) which innovatively incorporates the data representing the color transformation of 3D LUT into the real-time retouching process, which forces the network to learn the adaptive weights and the multiple 3D LUTs with strong representation capability. The estimated adaptive weights not only consider the content of the raw input but also use the information of the learned 3D LUTs. Specifically, the network contains two branches for feature extraction from the input image and 3D LUTs, to regard the information of the image and the 3D LUTs, and generate the precise LUT fusion weights. In addition, to better integrate the features of the input image and the learned 3D LUTs, we employ bilinear pooling to solve the problem of feature information loss that occurs when fusing features from the dual branch network, avoiding the feature distortion caused by direct concatenation or summation. Extensive experiments on several datasets demonstrate the effectiveness of our work, which is also efficient in processing high-resolution images. Our approach is not limited to image retouching tasks, but can also be applied to other pairwise learning-based tasks with fairly good generality. Our code is available at https://github.com/120326/DualBLN .
CSIE: Coded Strip-Patterns Image Enhancement Embedded in Structured Light-Based Methods

When a coded strip-patterns image (CSI) is captured in a structured light system (SLs), it often suffers from low visibility at low exposure settings. Besides degrading the visual perception of the CSI, this poor quality also significantly affects the performance of 3D model reconstruction. Most of the existing image-enhanced methods, however, focus on processing natural images but not CSI. In this paper, we propose a novel and effective CSI enhancement (CSIE) method designed for SLs. More concretely, a bidirectional perceptual consistency (BPC) criterion, including relative grayscale (RG), exposure, and texture level priors, is first introduced to ensure visual consistency before and after enhancement. Then, constrained by BPC, the optimization function estimates solutions of illumination with piecewise smoothness and reflectance with detail preservation. With well-refined solutions, CSIE results can be achieved accordingly and further improve the details performance of 3D model reconstruction. Experiments on multiple sets of challenging CSI sequences show that our CSIE outperforms the existing used for natural image-enhanced methods in terms of 2D enhancement, point clouds extraction (at least 17% improvement), and 3D model reconstruction.
Interpretative Structural Modeling Analyzes the Hierarchical Relationship between Mid-Air Gestures and Interaction Satisfaction

Mid-air gestures as a new form of human–computer interaction has a wide range of satisfaction factors, for which the primary and secondary relationships and hierarchical relationships between factors are unclear. By examining usability definitions, collecting satisfaction questionnaires and user interviews, 30 observed variables were obtained and a scale was developed. A total of 310 valid questionnaires were collected, and six latent variables were summarized through factor analysis. The matrix quantitative analysis of latent variables based on interpretative structural model theory was used to construct a hierarchical model of influencing factors of satisfaction with mid-air gestures. The study shows that the influencing factors of mid-air gesture satisfaction can be divided into three levels. The first layer of attractiveness is the direct influencing factor on the surface and the goal of mid-air gesture design. In the second layer, Simplicity and Efficiency, Simplicity and Tiredness, and Tiredness and Friendliness interact with each other. Simplicity positively affects Friendliness, and Efficiency positively affects Tiredness. The third layer, Intuitiveness is the root layer influencing factor, which affects Simplicity. This study provides a theoretical basis for the design of mid-air gesture so that it can be designed and selected more objectively.
Towards Real-Time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-Decoder Architecture

In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation varies from image to image. Recent methods adopt deep neural networks to recover clean scenes from snowy images directly. However, due to the paradox caused by the variation of complex snowy degradation, achieving reliable High-Definition image desnowing performance in real time is a considerable challenge. We develop a novel Efficient Pyramid Network with asymmetrical encoder-decoder architecture for real-time HD image desnowing. The general idea of our proposed network is to utilize the multi-scale feature flow fully and implicitly to mine clean cues from features. Compared with previous state-of-the-art desnowing methods, our approach achieves a better complexity-performance trade-off and effectively handles the processing difficulties of HD and Ultra-HD images. The extensive experiments on three large-scale image desnowing datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the PSNR metric from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29 dB to 30.87 dB on the SRRS test dataset. The source code is available at https://github.com/Owen718/Towards-Real-time-High-Definition-Image-Snow-Removal-Efficient-Pyramid-Network .
PERSONAL VIRTUAL VOICE DESKTOP ASSISTANT AND INTELLIGENT DECISION MAKER

Virtual Desktop Assistants (VDAs) are computer programs designed to automate and assist with various tasks on a user's desktop or laptop computer.With advancements in artificial intelligence and machine learning, VDAs have become more intuitive and conversational, allowing users to interact with them in a more natural way.The current state of the art in VDAs is characterized by continued development and improvement in their capabilities and performance, including integration with other software applications and devices, natural language processing, and more efficient use of data and processing power.However, VDAs still face challenges, such as difficulties in understanding natural language inputs in noisy or cluttered environments, and the need for significant amounts of data and processing power.The future of VDAs looks promising, with continued research and development expected to lead to even more advanced and effective virtual desktop assistants.Virtual Desktop Assistants (VDAs) have revolutionized the way we interact with technology.With the use of artificial intelligence and machine learning, VDAs have become more advanced and intuitive, allowing users to interact with them in a more natural and conversational manner.The current state of the art in VDAs includes integration with other devices and software applications, improved natural language processing capabilities, and more efficient use of data and processing power.Despite these advances, VDAs still face challenges, such as understanding natural language inputs in noisy or cluttered environments, and the need for vast amounts of data and processing power.However, with continued research and development, the future of VDAs looks bright, and it is expected that they will continue to play a crucial role in shaping the way we interact with technology.
A Knowledge Sharing Framework for Human Capital Development in Pentecostal Religious Organizations

In this paper, a theoretical framework for human capital development in Pentecostal Religious Organizations through knowledge sharing is provided. The framework conceptualizes that religious organizations should prioritize launching a knowledge management initiative and appointing a knowledge management team or manager to conduct a knowledge audit and design an appropriate knowledge management plan as explicated. Knowledge management is the process of creating, sharing, using, and managing the knowledge and information of an organization or group. It involves identifying, capturing, organizing, storing, and sharing knowledge and information in a way that allows individuals and teams to access and use it effectively to improve decision-making, problem-solving, and innovation. The knowledge management plan should focus on codifying tacit and explicit knowledge to ensure it can be easily accessed, understood and transferred, and should be connected to the organizational strategy for human capital development in community services by religious organizations. The study has practical implications for religious organizations looking to improve their organizational effectiveness and contribute to the human capital development of its members as well as host communities.
Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and the main challenge has been to achieve a balance between search efficiency and the quality of the mined rules. As a novel swarm intelligence algorithm, the water wave optimization (WWO) algorithm has been widely used for combinatorial optimization problems, with the disadvantage that it tends to fall into local optimum solutions and converges slowly. In this paper, a novel hybrid ARM method based on WWO with Levy flight (LWWO) is proposed. The proposed method improves the solution of WWO by expanding the search space through Levy flight while effectively increasing the search speed. In addition, this paper employs the hybrid strategy to enhance the diversity of the population in order to obtain the global optimal solution. Moreover, the proposed ARM method does not generate frequent items, unlike traditional algorithms (e.g., Apriori), thus reducing the computational overhead and saving memory space, which increases its applicability in real-world business cases. Experiment results show that the performance of the proposed hybrid algorithms is significantly better than that of the WWO and LWWO in terms of quality and number of mined rules.
Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments

Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviours autonomously. Deep Interactive Reinforcement 2 Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choose actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use, which causes a duplicate process at the same state for a revisit. In this paper, we present Broad-Persistent Advising (BPA), an approach that retains and reuses the processed information. It not only helps trainers give more general advice relevant to similar states instead of only the current state, but also allows the agent to speed up the learning process. We tested the proposed approach in two continuous robotic scenarios, namely a cart pole balancing task and a simulated robot navigation task. The results demonstrated that the agent's learning speed increased, as evidenced by the rising reward points of up to 37%, while maintaining the number of interactions required for the trainer, in comparison to the DeepIRL approach.
Far from Asymptopia: Unbiased High-Dimensional Inference Cannot Assume Unlimited Data

Inference from limited data requires a notion of measure on parameter space, most explicit in the Bayesian framework as a prior. Here we demonstrate that Jeffreys prior, the best-known uninformative choice, introduces enormous bias when applied to typical scientific models. Such models have a relevant effective dimensionality much smaller than the number of microscopic parameters. Because Jeffreys prior treats all microscopic parameters equally, it is from uniform when projected onto the sub-space of relevant parameters, due to variations in the local co-volume of irrelevant directions. We present results on a principled choice of measure which avoids this issue, leading to unbiased inference in complex models. This optimal prior depends on the quantity of data to be gathered, and approaches Jeffreys prior in the asymptotic limit. However, this limit cannot be justified without an impossibly large amount of data, exponential in the number of microscopic parameters.
Pengaruh Perhatian Orangtua terhadap Hasil Belajar Siswa Bidang Studi PAI di SMP Negeri 01 Koto Besar Kabupaten Dharmasraya

The background of this research is that there are parents who have various kinds of busyness, one of which is work which causes a student to pay less attention to how learning problems are experienced by children at school. and there are also parents who are reluctant to attend school invitations because the student's behavior causes students to lack interest in learning so that the learning outcomes they get are not good. This study aims to determine the effect of parental attention on student learning outcomes at SMPN 01 Koto Besar.This research belongs to the correlative quantitative research, namely to see the effect of two different variables. The population in this study were class VIII students, totaling 104 students. The research sample was 27 students who were taken using the Total Sampling technique. Data was collected using a Likert scale instrument and then processed using the Statistical Product and Service Solution (SPSS) Version 20 hypothesis test.Based on the results of the correlation analysis, it is known that if the pcount value is greater than the ptable, it can be said to be correlated. In this study the ptable was 0.381, the pcount value in this study was 0.371 greater than the ptable 0.381, so it can be said that parental attention and learning outcomes are correlated. The variable size of parents' attention to the learning outcome variable is calculated by D = r2 × 100%, a D of 32% is obtained. So it can be concluded that the influence of parental attention on student learning outcomes in SMP 1 Negeri Koto Besar is 32%, it can be said that Ha is accepted and H0 is rejected and there is a significant relationship between parental attention to student learning outcomes in SMP 1 Negeri Koto Big.
Single-photon switches, beam splitters, and circulators based on the photonic Aharonov-Bohm effect

Single-photon devices such as switches, beam splitters, and circulators are fundamental components to construct photonic integrated quantum networks. In this paper, two V-type three-level atoms coupled to a waveguide are proposed to simultaneously realize these functions as a multifunctional and reconfigurable single-photon device. When both the two atoms are driven by the external coherent fields, the difference in the phases of the coherent driving induces the photonic Aharonov-Bohm effect. Based on the photonic Aharonov-Bohm effect and setting the two-atom distance to match the constructive or destructive interference conditions among photons travelling along different paths, a single-photon switch is achieved since the incident single photon can be controlled from complete transmission to complete reflection by adjusting the amplitudes and phases of the driving fields. When properly changing the amplitudes and phases of the driving fields, the incident photons are split equally into multiple components as a beam splitter operated with different frequencies. Meanwhile, the single-photon circulator with reconfigurable circulation directions can also be obtained.
A Solidity implementation of TAVS

We present a Solidity smart contract implementation of the TAVS e-voting protocol. The Two Authorities Electronic Voting Scheme (TAVS) is a voting scheme that achieves universal verifiability with a reduced time-complexity both for the elector and the voting system. TAVS security derives from the RSA cryptosystem it employs, and the assumption of two entities that do not share information. We present a Solidity implementation which replaces one of these entities with an immutable smart contract in Ethereum based networks. By doing so, our implementation extends the security properties of TAVS and achieves a higher degree of resilience, verifiability, and availability. We open source the code of the implementation.
An improvement of efficient nonlinear color image interpolator: Theory and FPGA implementation

In this paper, a new proposal for a nonlinear edge-preserving interpolator and hardware implementation is presented. As a new idea for a color image interpolator, our proposal is focusing on the interpolated pixel and we tried to adjust it in order to have better quality. To evaluate the effectiveness of the proposed idea for preserving images, we implemented it using different color images and we evaluated using different evaluation measurements. Then, we compared our new proposal with the traditional nonlinear Edge preserving interpolator. The obtained results confirm that our proposed Edge preserving is better than the old interpolator. It also demonstrates consistent image quality performance among a variety of images. The hardware implementation based on FPGA shows that we are able to gain image quality without increasing the size of the circuit once implemented in hardware. We show that our proposed interpolator for Edge preserving improves considerably the image quality and represents a fast solution when implemented in hardware. Despite a small increase in FPGA resources, we obtain an average improvement of the image quality of about 35.75% using the NCD metric.
Transformer Fault Synthetic Diagnosis Method Based on Fusion of Multi-Neural Networks and Evidence Theory in Cloud Computing

Abstract As the core equipment of power system, the failure of transformer will result in paralysis of the whole system. Therefore, timely diagnosis of transformer fault types is the most important task to ensure the stable operation of power system. Through the cloud computing method, the neural network is combined with the advantages of evidence theory, by comparing the chromatographic data of the transformer and the electrical test data. A comprehensive diagnosis method of transformer fault based on the fusion of multi neural network and evidence theory based on cloud computing is proposed. The fault diagnosis performance of the transformer is measured by the inspection of the fault transformer. The results show that compared with the traditional single data comparison, this method can improve the reliability and accuracy of diagnosis by comparing many kinds of data.
Abstracting Instance Information and Inter-Label Relations for Sparse Multi-Label Classification

In this paper, for sparse multi-label data, based on inter-instance relations and inter-label correlation, a Sparse Multi-Label Kernel Gaussian Neural Network (SMLKGNN) framework is proposed. Double insurance for the sparse multi-label datasets is constructed with bidirectional relations such as inter-instance and inter-label. When instance features or label sets are too sparse to be extracted effectively, we argument that the inter-instance relations and inter-label correlation can supplement and deduce the relevant information. Meanwhile, to enhance the explainable of neural network, Gaussian process is adopted to simulate the real underlying distribution of multi-label dataset. Besides, this paper also considers that contributions of different features have different effects on the experimental results, thus self-attention is leveraged to balance various features. Finally, the applicability of the algorithm is verified in three sparse datasets, and the generalization performance is also validated in three groups of benchmark datasets.