Reviewerly/exHarmony huggingface dataset:
Least squares support vector regression for complex censored data

Least squares support vector regression (LS-SVR) is a robust machine learning algorithm for small sample data. Its solution is derived from solving a set of linear equations, making the calculation process straightforward. In order to overcome the difficulties of the regression estimations when the responses are subject to interval censoring or left truncation and right censoring, two LS-SVR methods are proposed. For interval-censored data, one can easily estimate the regression functions by combining the imputation techniques and LS-SVR for right-censored data. For left-truncated and right-censored data, a weight is used to reduce the effects of truncation and censoring on the LS-SVR procedure. Simulation results show that the proposed methods can reduce regression error and yield high accuracy and stability.
FloLPIPS: A Bespoke Video Quality Metric for Frame Interpolation

Video frame interpolation (VFI) serves as a useful tool for many video processing applications. Recently, it has also been applied in the video compression domain for enhancing both conventional video codecs and learning-based compression architectures. While there has been an increased focus on the development of enhanced frame interpolation algorithms in recent years, the perceptual quality assessment of interpolated content remains an open field of research. In this paper, we present a bespoke full reference video quality metric for VFI, FloLPIPS, that builds on the popular perceptual image quality metric, LPIPS, which captures the perceptual degradation in extracted image feature space. In order to enhance the performance of LPIPS for evaluating interpolated content, we re-designed its spatial feature aggregation step by using the temporal distortion (through comparing optical flows) to weight the feature difference maps. Evaluated on the BVI-VFI database, which contains 180 test sequences with various frame interpolation artefacts, FloLPIPS shows superior correlation performance (with statistical significance) with subjective ground truth over 12 popular quality assessors. To facilitate further research in VFI quality assessment, our code is publicly available at https://danielism97.github.io/F1oLPIPS.
Opening the Black Box of Learned Image Coders

End-to-end learned lossy image coders (LICs), as opposed to hand-crafted image codecs, have shown increasing superiority in terms of the rate-distortion performance. However, they are mainly treated as black-box systems and their interpretability is not well studied. In this paper, we show that LICs learn a set of basis functions to transform input image for its compact representation in the latent space, as analogous to the orthogonal transforms used in image coding standards. Our analysis provides insights to help understand how learned image coders work and could benefit future design and development.
Block-Level Rate Control for Learnt Image Coding

Learnt image coding (LIC) methods recently offered state-of-the-art efficiency by training separate models for individual bitrate which apparently was impractical. Variable-rate coding with a single or very few LIC models was emerged and mostly implemented to process a whole image directly (e.g., a single control rate-distortion factor $\lambda$ for a given image to approach target rate). This work provides a novel block-level rate control by applying the UnEqual Rate Allocation (UERA) to nonoverlapped image blocks, which basically exploits the spatial heterogeneousness of the underlying content. Such block-level UERA is enabled by modeling the rate-distortion (R-D) function of each block, by which we optimize block-wise $\lambda$s to maximize the overall R-D performance. Experiments show that our method can accurately adapt a wide range of bitrates by a single model, and provide almost identical performance as the solutions using multiple rate-specific models. Additionally, such block-level LIC significantly reduces the consumption of peak running memory and computational complexity, which is attractive for practical implementations.
Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression

End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. The core idea is to learn a non-linear transformation, modeled as a deep neural network, mapping input image into latent space, jointly with an entropy model of the latent distribution. The decoder is also learned as a deep trainable network, and the reconstructed image measures the distortion. These methods enforce the latent to follow some prior distributions. Since these priors are learned by optimization over the entire training set, the performance is optimal in average. However, it cannot fit exactly on every single new instance, hence damaging the compression performance by enlarging the bit-stream. In this paper, we propose a simple yet efficient instance-based parameterization method to reduce this amortization gap at a minor cost. The proposed method is applicable to any end-to-end compressing methods, improving the compression bitrate by 1% without any impact on the reconstruction quality.
Substitutional Neural Image Compression

We describe Substitutional Neural Image Compression (SNIC), a general approach for enhancing any neural image compression model, that requires no data or additional tuning of the trained model. It boosts compression performance toward a flexible distortion metric and enables bit-rate control using a single model instance. The key idea is to replace the image to be compressed with a substitutional one that outperforms the original one in a desired way. Finding such a substitute is inherently difficult for conventional codecs, yet surprisingly favorable for neural compression models thanks to their fully differentiable structures. With gradients of a particular loss back-propogated to the input, a desired substitute can be efficiently crafted iteratively. We demonstrate the effectiveness of SNIC, when combined with various neural compression models and target metrics, in improving compression quality and performing bit-rate control measured by rate-distortion curves.
Lung cancer detection from histopathological lung tissue images using deep learning

Lung cancer is a disease in which the growth of cells in the lung goes out of control. This disease can be lethal if the treatment to stop the growth of cells is not given to the patient in its early stages. Hence, it is very crucial to correctly recognize lung cancer in less time. Using the traditional method where each tissue is observed by a medical practitioner is time-consuming as well as error-prone; moreover, the practitioner should be very skilled. All these problems can be solved by using automated methods to detect lung cancer. In this chapter different deep learning models and techniques are used to detect lung cancer using histopathological images. The accuracy achieved by these models is very high and takes negligible time to give the results. Using a pretrained ResNet model combined with a support vector machine accuracy of 98.57% is achieved on the test data.
The synergy between governance and trade openness in promoting female economic inclusion in Sub-Saharan Africa

The debate on the need for Sub-Saharan African (SSA) countries to increase female participation in the economic sector has intensified the coming into force of the African Continental Free Trade Area (AfCFTA) and good governance. This study investigates the joint effects of governance (comprising of political, economic and institutional governance) and trade openness on female economic participation in SSA. The study employs panel data of 42 countries in SSA for the period 1996–2020. The empirical strategy is the dynamic System Generalized Method of Moments (SGMM) estimation technique. The findings reveal that the single effect of trade openness on female economic participation is necessary but not sufficient. Hence, complementing trade openness with good governance further enhances female economic participation in SSA. In general, the joint effect of trade openness and good governance should be a concern for policy makers to promote female economic inclusion.
Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service

Because of the rapid increase of data in the cloud of Amazon Web Service (AWS), the traditional methods for analyzing this data are not good and inappropriate, so unconventional methods of analysis have been proposed by many data scientists such as concurrent/ parallel techniques to meeting the requirements of performance and scalability entailed in such big data analyses. In this paper we are used Hadoop Map Reduce system that contains Hadoop Distributed File System (HDFS) and Hadoop cluster. We optimized it by combining it with five efficient Data Mining (DM) algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Correlative Naïve Bayes classifier (CNB), and Fuzzy CNB (FCNB) for strong analytics of cloud big data. The proposed system applied on product review data that taken form the cloud of AWS. The Evaluation of Hadoop Map Reduce done with important benchmarks as Mean Absolute Percentage Error (MPAE), Root Mean Square Error (RMSE), and runtime for word count, sort, inverted index. Also, the evaluation of DM models with Hadoop Map Reduce system done by using accuracy, sensitivity, specificity, memory, and running time. Experiments have shown that FCNB is effective in addressing the problem of big data.
Self-supervised clustering with assistance from off-the-shelf classifier

Deep clustering outperforms conventional clustering by mutually promoting representation learning and cluster assignment. However, most existing deep clustering methods suffer from two major drawbacks. Firstly, most cluster assignment methods are highly dependent on the intermediate target distribution generated by a handcrafted nonlinear mapping function. Secondly, the clustering results can be easily guided towards wrong direction by the misassigned samples in each cluster. The existing deep clustering methods are incapable of discriminating such samples. These facts largely limit the possible performance that deep clustering methods can reach. To address these issues, a novel Self-Supervised Clustering (SSC) framework is constructed, which boosts the clustering performance by classification in an unsupervised manner. Fuzzy theory is used to score the membership of each sample to the clusters in terms of probability in each training epoch, which evaluates the intermediate clustering result certainty of each sample. The most reliable samples can be selected with the help of a sample selection method according to the membership and enhanced by data augmentation method. These augmented data are employed to fine-tune an off-the-shelf deep network classifier with the labels provided by the clustering in a self-supervised way. The classification results of the original dataset are used as the target distribution to guide the training process of the deep clustering model. The proposed framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of the powerful classifier. Extensive experiments indicate that the proposed framework remarkably outperforms state-of-the-art deep clustering methods on four benchmark datasets.
Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.
Motion Estimation And Filtered Prediction For Dynamic Point Cloud Attribute Compression

In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-based inter-coding scheme for color attribute compression. The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction. The proposed block-based motion estimation scheme consists of an initial motion search that exploits geometric and color attributes, followed by a motion refinement that only minimizes color prediction error. To further improve color prediction, we propose a vertex-domain low-pass graph filtering scheme that can adaptively remove noise from predictors computed from motion estimation with different accuracy. Our experiments demonstrate significant coding gain over state-of-the-art coding methods.
How to improve the accuracy of clustering algorithms

Clustering is an important data analysis technique. However, due to the diversity of datasets, each clustering algorithm is unable to produce satisfactory results on some particular datasets. In this paper, we propose a clustering optimization method called HIAC (Highly Improving the Accuracy of Clustering algorithms). By introducing gravitation, HIAC forces objects in the dataset to move towards similar objects, making the ameliorated dataset more friendly to clustering algorithms (i.e., clustering algorithms can produce more accurate results on the ameliorated dataset). HIAC is independent of clustering principle, so it can optimize different clustering algorithms. In contrast to other similar methods, HIAC is the first to adopt the selective-addition mechanism, i.e., only adding gravitation between valid-neighbors, to avoid dissimilar objects approaching each other. In order to identify valid-neighbors from neighbors, HIAC introduces a decision graph, from which the naked eye can observe a clear division threshold. Additionally, the decision graph can assist HIAC in reducing the negative effects that improper parameter values have on optimization. We conducted numerous experiments to test HIAC. Experiment results show that HIAC can effectively ameliorate high-dimensional datasets, Gaussian datasets, shape datasets, the datasets with outliers, and overlapping datasets. HIAC greatly improves the accuracy of clustering algorithms, and its improvement rates are far higher than that of similar methods. The average improvement rate is as high as 253.6% (except maximum and minimum). Moreover, its runtime is significantly shorter than that of most similar methods. More importantly, with different parameter values, the advantages of HIAC over similar methods are always maintained. The code of HIAC is available at https://github.com/qiqi12/HIAC.
Dynamic Multi-Attention Dehazing Network with Adaptive Feature Fusion

This paper proposes a Dynamic Multi-Attention Dehazing Network (DMADN) for single image dehazing. The proposed network consists of two key components, the Dynamic Feature Attention (DFA) module, and the Adaptive Feature Fusion (AFF) module. The DFA module provides pixel-wise weights and channel-wise weights for input features, considering that the haze distribution is always uneven in a degenerated image and the value in each channel is different. We propose an AFF module based on the adaptive mixup operation to restore the missing spatial information from high-resolution layers. Most previous works have concentrated on increasing the scale of the model to improve dehazing performance, which makes it difficult to apply in edge devices. We introduce contrastive learning in our training processing, which leverages both positive and negative samples to optimize our network. The contrastive learning strategy could effectively improve the quality of output while not increasing the model’s complexity and inference time in the testing phase. Extensive experimental results on the synthetic and real-world hazy images demonstrate that DMADN achieves state-of-the-art dehazing performance with a competitive number of parameters.
The Mediating Role of Sustainable Dynamic Capabilities in the Effect of Social Customer Relationship Management on Sustainable Competitive Advantage: A Study on SMEs in Saudi Arabia

This study aims at investigating the mediating role of sustainable dynamic capabilities in the effect of social customer relationship management on sustainable competitive advantage using data gathered from a convenience sample consisted of 243 managers of SMEs in Saudi Arabia via an electronic questionnaire. Using structural equation modeling by SmartPLS3.0, the study pointed out social customer relationship management is positively related to both sustainable competitive advantage and sustainable dynamic capabilities. The latter is positively associated to sustainable competitive advantage and hence, significantly mediated the effect of social customer relationship management on sustainable competitive advantage. The study contributes to the literature through filling a research gap as research on the relationship between s-CRM and dynamic capabilities in Saudi Arabia is still scarce, confirming the importance of social customer relationship management in sustaining both dynamic capabilities and competitive advantage. SMEs are required to invest in CRM and build sustainable resources and capabilities to elevate sustainable competitive advantage.
Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices

Tiny machine learning (TinyML) has become an emerging field according to the rapid growth in the area of the internet of things (IoT). However, most deep learning algorithms are too complex, require a lot of memory to store data, and consume an enormous amount of energy for calculation/data movement; therefore, the algorithms are not suitable for IoT devices such as various sensors and imaging systems. Furthermore, typical hardware accelerators cannot be embedded in these resource-constrained edge devices, and they are difficult to drive real-time inference processing as well. To perform the real-time processing on these battery-operated devices, deep learning models should be compact and hardware-optimized, and hardware accelerator designs also have to be lightweight and consume extremely low energy. Therefore, we present an optimized network model through model simplification and compression for the hardware to be implemented, and propose a hardware architecture for a lightweight and energy-efficient deep learning accelerator. The experimental results demonstrate that our optimized model successfully performs object detection, and the proposed hardware design achieves 1.25× and 4.27× smaller logic and BRAM size, respectively, and its energy consumption is approximately 10.37× lower than previous similar works with 43.95 fps as a real-time process under an operating frequency of 100 MHz on a Xilinx ZC702 FPGA.
Aquila Optimizer with Parallel Computation Application for Efficient Environment Exploration

In the multiple fields covered by Artificial Intelligence (AI), robotic path planning is undoubtedly one of the issues that cover a wide range of research lines. This paper introduces recently developed Aquila Optimization algorithm specifically configured for Multi-Robot space exploration. The framework is a unique combination of both deterministic Coordinated Multi-robot Exploration (CME) and a swarm based Aquila Optimizer (AO), combinely known as Coordinated Multi-robot Exploration Aquila Optimizer (CME-AO). The proposed hybrid strategy also incorporates a novel parallel communication protocol, to improve multi-robot space exploration process while simultaneously minimizing both the computation complexity and time. This ensures acquisition of a optimal collision-free path in a barrier-filled environment via generating a finite map. The architecture starts by determining the cost and utility values of neighbouring cells around the robot using deterministic CME. Aquila Optimization technique is then incorporated to increase the overall solution accuracy. Algorithm validity and effectiveness was then validated utilizing different condition environment whose relative complexity was varied by varying parameters such as exploration space dimension and obstacle size, number and relative orientation. A perspective analysis is then performed to compare the performance of the proposed CME-AO algorithm with latest contemporary algorithms such as conventional CME and CME-WO (CME augmented Whale Optimizer). Results indicate efficacy of the proposed algorithm as it presents two distinct advantages a) enhanced map exploration in cluttered environment and b) significantly reduced computation complexity and execution time. This makes the suggested methodology particularly suitable for on-board utilization in an obstacle-cluttered environment, where other contemporary CME based techniques either fails (stuck locally) or takes longer exploration time.
Real-Time Collision Avoidance Trajectory Planner Using Generalized Vector Explicit Guidance

In this paper, a new real-time collision avoidance scheme is proposed using Generalized Vector Explicit Guidance (GENEX) law, an optimal guidance algorithm that can simultaneously achieve design specifications on miss distance and final UAV–target relative orientation. Low computation, closed-form expression, and optimal derivation are the key features that make it a potential candidate for real-time applications. The guidance law has been tested through simulations for the two-dimensional scenario, and the results are very satisfactory.
Low-Complexity Quantum Channel Phase Estimation

Quantum communication suggests the supremacy of secure communications over classic counterparts. However, engineering quantum communication systems in practice is still in the early stage, particularly dealing with the unique features of quantum mechanical systems and challenging implementation of quantum circuitry. In this paper, we uniquely explore the quantum phase estimation of the quantum communication channel under the constraint of low-complexity quantum circuitry. Taking advantage of unique multiple computational bases for a quantum system to form quantum pilot signals, only extremely simple quantum circuitry is required together with statistical analysis algorithm in classic computations to successfully accomplish quantum channel phase estimation.