Reviewerly/exHarmony huggingface dataset:
An Adaptive Covariance Scaling Estimation of Distribution Algorithm

Optimization problems are ubiquitous in every field, and they are becoming more and more complex, which greatly challenges the effectiveness of existing optimization methods. To solve the increasingly complicated optimization problems with high effectiveness, this paper proposes an adaptive covariance scaling estimation of distribution algorithm (ACSEDA) based on the Gaussian distribution model. Unlike traditional EDAs, which estimate the covariance and the mean vector, based on the same selected promising individuals, ACSEDA calculates the covariance according to an enlarged number of promising individuals (compared with those for the mean vector). To alleviate the sensitivity of the parameters in promising individual selections, this paper further devises an adaptive promising individual selection strategy for the estimation of the mean vector and an adaptive covariance scaling strategy for the covariance estimation. These two adaptive strategies dynamically adjust the associated numbers of promising individuals as the evolution continues. In addition, we further devise a cross-generation individual selection strategy for the parent population, used to estimate the probability distribution by combing the sampled offspring in the last generation and the one in the current generation. With the above mechanisms, ACSEDA is expected to compromise intensification and diversification of the search process to explore and exploit the solution space and thus could achieve promising performance. To verify the effectiveness of ACSEDA, extensive experiments are conducted on 30 widely used benchmark optimization problems with different dimension sizes. Experimental results demonstrate that the proposed ACSEDA presents significant superiority to several state-of-the-art EDA variants, and it preserves good scalability in solving optimization problems.
Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models

Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the survival rate. In this study, the performance of a wide variety of deep learning-based architectures is evaluated for automatic tumor segmentation of colorectal tissue samples. The proposed approach highlights the utility of incorporating convolutional neural network modules and transfer learning in the encoder part of a segmentation architecture for histopathology image analysis. A comparative and extensive experiment was conducted on a challenging histopathological segmentation task to demonstrate the effectiveness of incorporating deep modules in the segmentation encoder-decoder network as well as the contributions of its components. Experimental results demonstrate that shared DenseNet and LinkNet architecture is promising, achieves the state-of-the-art performance, and outperforms other methods with a dice similarity index of 82.74%±1.77, accuracy of 87.07%±1.56, and f1-score value of 82.79%±1.79.
EvoKG

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, is crucial for many applications to provide intelligent services. However, despite the prevalence of real-world data that can be represented as TKGs, most methods focus on reasoning over static knowledge graphs, or cannot predict future events. In this paper, we present a problem formulation that unifies the two major problems that need to be addressed for an effective reasoning over TKGs, namely, modeling the event time and the evolving network structure. Our proposed method EvoKG jointly models both tasks in an effective framework, which captures the ever-changing structural and temporal dynamics in TKGs via recurrent event modeling, and models the interactions between entities based on the temporal neighborhood aggregation framework. Further, EvoKG achieves an accurate modeling of event time, using flexible and efficient mechanisms based on neural density estimation. Experiments show that EvoKG outperforms existing methods in terms of effectiveness (up to 77% and 116% more accurate time and link prediction) and efficiency.
DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion With Deep Association

In the recent literature, on the one hand, many 3D multi-object tracking (MOT) works have focused on tracking accuracy and neglected computation speed, commonly by designing rather complex cost functions and feature extractors. On the other hand, some methods have focused too much on computation speed at the expense of tracking accuracy. In view of these issues, this paper proposes a robust and fast camera-LiDAR fusion-based MOT method that achieves a good trade-off between accuracy and speed. Relying on the characteristics of camera and LiDAR sensors, an effective deep association mechanism is designed and embedded in the proposed MOT method. This association mechanism realizes tracking of an object in a 2D domain when the object is far away and only detected by the camera, and updating of the 2D trajectory with 3D information obtained when the object appears in the LiDAR field of view to achieve a smooth fusion of 2D and 3D trajectories. Extensive experiments based on the typical datasets indicate that our proposed method presents obvious advantages over the state-of-the-art MOT methods in terms of both tracking accuracy and processing speed. Our code is made publicly available for the benefit of the community.
Cloud Feature Extraction and Fluctuation Pattern Recognition Based Ultrashort-Term Regional PV Power Forecasting

Regional photovoltaic(PV) power forecasting provides a foundation for grid management and trading in the power markets. To tackle the deficiency of conventional regional PV power modeling methods, such as the problem of challenging to select useful meteorological information and the problem that a single model cannot fully learn the complex and diverse fluctuation characteristics of power curves, an ultrashort-term regional PV power forecasting framework assembled by fusing fluctuation pattern recognition (FPR) and deep learning modeling under a fluctuation pattern prediction (FPP) model was proposed. First of all, the fluctuation characteristics of the regional PV power curves were extracted, including sampling loss area, mean value, standard deviation, and third derivative. Then, a K-means algorithm based FPR model was established to obtained three kinds of patterns. FPP model based on convolutional neural network is used to predict future PV power fluctuation patterns with historical satellite images as input. Finally, the convolutional autoencoder is used to extract cloud distribution features, and a long- and short-term memory network based on the combination of cloud distribution features and historical output is proposed to construct prediction models of the three models. Through the analysis of the simulation data, the prediction method proposed in this article has higher prediction accuracy than the traditional prediction model.
Image dehazing via enhancement, restoration, and fusion: A survey

Haze usually causes severe interference to image visibility. Such degradation on images troubles both human observers and computer vision systems. To seek high-quality images from degraded ones, a large number of image dehazing algorithms have been proposed from different perspectives like image enhancement, restoration, and fusion. Especially in recent years, with the rapid development of deep learning, CNN-based methods have already dominated the mainstream of image dehazing and gained significant progress on benchmark datasets. This paper firstly presents a comprehensive survey of existing image dehazing methods, and then conducts both qualitative and quantitative comparisons among representative methods, from classic methods to recent advanced approaches. We expect the literature survey and benchmark analysis could help readers better understand the advantages and limitations of existing dehazing methods. Moreover, a discussion on possible trends in single image dehazing is put forward to innovate further works.
Improving embedded knowledge graph multi-hop question answering by introducing relational chain reasoning

Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires reasoning over the multi-hop relational chain preserved in KG to arrive at the right answer. Despite recent successes, the existing works on answering multi-hop complex questions still face the following challenges: (i) The absence of an explicit relational chain order reflected in user-question stems from a misunderstanding of a user’s intentions. (ii) Incorrectly capturing relational types on weak supervision of which dataset lacks intermediate reasoning chain annotations due to expensive labeling cost. (iii) Failing to consider implicit relations between the topic entity and the answer implied in structured KG because of limited neighborhoods size constraint in subgraph retrieval-based algorithms. To address these issues in multi-hop KGQA, we propose a novel model herein, namely Relational Chain based Embedded KGQA (Rce-KGQA), which simultaneously utilizes the explicit relational chain revealed in natural language question and the implicit relational chain stored in structured KG. Our extensive empirical study on three open-domain benchmarks proves that our method significantly outperforms the state-of-the-art counterparts like GraftNet, PullNet and EmbedKGQA. Comprehensive ablation experiments also verify the effectiveness of our method on the multi-hop KGQA task. We have made our model’s source code available at github: https://github.com/albert-jin/Rce-KGQA .
Exploiting Semi-Tensor Product Compressed Sensing and Hybrid Cloud for Secure Medical Image Transmission

With the development of telemedicine diagnosis technology, the collection, storage, and transmission of medical data has become a pivotal problem. To solve these problems, in terms of semi-tensor product compressed sensing (STP-CS) and hybrid cloud, a new medical data transmission framework is presented in this article, which can ensure the efficiency, confidentiality, and verifiability of data transmission. According to the edge detection, the authentication information is embedded into the insignificant area of the medical image to protect the authenticity of the data. STP-CS is utilized to measure and encrypt the medical image, which improves the efficiency of data transmission. In order to make the image data more secure, an adaptive cyclic shift diffusion algorithm based on chaotic sequence is used to effectively diffuse the measurement results. In addition, we also apply encoding for the quantized measurement value to be used as tamper proof authentication. The simulation results prove that the presented medical data transmission scheme can effectively improve the image reconstruction effect, transmission efficiency, and security.
Three Things Everyone Should Know About Vision Transformers

After their initial success in natural language processing, transformer architectures have rapidly gained traction in computer vision, providing state-of-the-art results for tasks such as image classification, detection, segmentation, and video analysis. We offer three insights based on simple and easy to implement variants of vision transformers. (1) The residual layers of vision transformers, which are usually processed sequentially, can to some extent be processed efficiently in parallel without noticeably affecting the accuracy. (2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to a higher resolution and to other classification tasks. This saves compute, reduces the peak memory consumption at fine-tuning time, and allows sharing the majority of weights across tasks. (3) Adding MLP-based patch pre-processing layers improves Bert-like self-supervised training based on patch masking. We evaluate the impact of these design choices using the ImageNet-1k dataset, and confirm our findings on the ImageNet-v2 test set. Transfer performance is measured across six smaller datasets.
Deep learning: A primer for dentists and dental researchers

Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes. Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study. In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project. Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects. This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.
Liver Cancer Algorithm: A novel bio-inspired optimizer

This paper introduces a new bio-inspired optimization algorithm named the Liver Cancer Algorithm (LCA), which mimics the liver tumor growth and takeover process. It uses an evolutionary search approach that simulates the behavior of liver tumors when taking over the liver organ. The tumor’s ability to replicate and spread to other organs inspires the algorithm. LCA algorithm is developed using genetic operators and a Random Opposition-Based Learning (ROBL) strategy to efficiently balance local and global searches and explore the search space. The algorithm’s efficiency is tested on the IEEE Congress of Evolutionary Computation in 2020 (CEC’2020) benchmark functions and compared to seven widely used metaheuristic algorithms, including Genetic Algorithm (GA), particle swarm optimization (PSO), Differential Evolution (DE), Adaptive Guided Differential Evolution Algorithm (AGDE), Improved Multi-Operator Differential Evolution (IMODE), Harris Hawks Optimization (HHO), Runge–Kutta Optimization Algorithm (RUN), weIghted meaN oF vectOrs (INFO), and Coronavirus Herd Immunity Optimizer (CHIO). The statistical results of the convergence curve, boxplot, parameter space, and qualitative metrics show that the LCA algorithm performs competitively compared to well-known algorithms. Moreover, the versatility of the LCA algorithm extends beyond mathematical benchmark problems. It was also successfully applied to tackle the feature selection problem and optimize the support vector machine for various biomedical data classifications, resulting in the creation of the LCA-SVM model. The LCA-SVM model was evaluated in a total of twelve datasets, among which the MonoAmine Oxidase (MAO) dataset stood out, showing the highest performance compared to the other datasets. In particular, the LCA-SVM model achieved an impressive accuracy of 98.704% on the MAO dataset. This outstanding result demonstrates the efficacy and potential of the LCA-SVM approach in handling complex datasets and producing highly accurate predictions. The experimental results indicate that the LCA algorithm surpasses other methods to solve mathematical benchmark problems and feature selection.
Exploring Quantitative Evaluations of the Creativity of Automatic Poets

The purpose of this chapter is twofold: to show the practical applications of theoretical evaluation measures designed to capture the degree of creativity of a program, and to use the results to evaluate an effort to develop an automatic Spanish poet. Existing efforts in the development of automatic poets are described, and the implications of their particular architectures for the evaluation issues discussed are considered.
Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors – including NVIDIA, Intel, AMD, and IBM – have architectural road maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. We use sequentially equivalent implementations to maintain iso-accuracy between parallel and sequential DL models. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling – sometimes encouraged by restricted GPU memory – NVLink is less important.
Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs

Visual designs can be complex in modern data visualization systems, which poses special challenges for explaining them to the non-experts. However, few if any presentation tools are tailored for this purpose. In this study, we present Narvis, a slideshow authoring tool designed for introducing data visualizations to non-experts. Narvis targets two types of end-users: teachers, experts in data visualization who produce tutorials for explaining a data visualization, and students, non-experts who try to understand visualization designs through tutorials. We present an analysis of requirements through close discussions with the two types of end-users. The resulting considerations guide the design and implementation of Narvis. Additionally, to help teachers better organize their introduction slideshows, we specify a data visualization as a hierarchical combination of components, which are automatically detected and extracted by Narvis. The teachers craft an introduction slideshow through first organizing these components, and then explaining them sequentially. A series of templates are provided for adding annotations and animations to improve efficiency during the authoring process. We evaluate Narvis through a qualitative analysis of the authoring experience, and a preliminary evaluation of the generated slideshows.
Nonlinear Dimensionality Reduction With Missing Data Using Parametric Multiple Imputations

Dimensionality reduction (DR) aims at faithfully and meaningfully representing high-dimensional (HD) data into a low-dimensional (LD) space. Recently developed neighbor embedding DR methods lead to outstanding performances, thanks to their ability to foil the curse of dimensionality. Unfortunately, they cannot be directly employed on incomplete data sets, which become ubiquitous in machine learning. Discarding samples with missing features prevents their LD coordinates computation and deteriorates the complete samples treatment. Common missing data imputation schemes are not appropriate in the nonlinear DR context either. Indeed, even if they model the data distribution in the feature space, they can, at best, enable the application of a DR scheme on the expected data set. In practice, one would, instead, like to obtain the LD embedding with the closest cost function value on average with respect to the complete data case. As the state-of-the-art DR techniques are nonlinear, the latter embedding results from minimizing the expected cost function on the incomplete database, not from considering the expected data set. This paper addresses these limitations by developing a general methodology for nonlinear DR with missing data, being directly applicable with any DR scheme optimizing some criterion. In order to model the feature dependences, an HD extension of Gaussian mixture models is first fitted on the incomplete data set. It is afterward employed under the multiple imputation paradigms to obtain a single relevant LD embedding, thus minimizing the cost function expectation. Extensive experiments demonstrate the superiority of the suggested framework over alternative approaches.
Intrinsic Image Decomposition with Step and Drift Shading Separation

Decomposing an image into the shading and reflectance layers remains challenging due to its severely under-constrained nature. We present an approach based on illumination decomposition that recovers the intrinsic images without additional information, e.g., depth or user interaction. Our approach is based on the rationale that the shading component contains the step and drift channels simultaneously. We decompose the illumination into two channels: the step shading, corresponding to the sharp shading changes due to cast shadow or abrupt shape changes; the drift shading, accounting for the smooth shading variations due to gradual illumination changes or slow shape changes. Due to such transformation of turning the conventional assumption that shading has smoothness as reasonable prior, our model has the advantages in handling real images, especially with the cast shadows or strong shape edges. We also apply a much stricter edge classifier along with a reinforcement process to enhance our method. We formulate the problem using a two-parameter energy function and split it into two energy functions corresponding to the reflectance and step shading. Experiments on the MIT dataset, the IIW dataset and the MPI Sintel dataset have shown the success of our approach over the state-of-the-art methods.
Categorizing Security for Security Management and Information Resource Management

There are various definitions and dimensions of security, and there is no comprehensive taxonomy of security.The existing classifications of security are fragmented, scattered, and divergent, binging challenges in the management of security and the management of information resources about security.This research aims to study the problems of security categorization in existing knowledge organization systems, and to develop a comprehensive taxonomy of security.Through thematic analysis of the literature about security, we found that, despite the various definitions and dimensions of security, there is a common feature of security.That is, security is expressed in this pattern: subject wants to protect object against source of insecurity using certain methods.Through facet analysis, we identified four facets of security -subject/scope of security, object of protection, source of insecurity, and method of security.By nesting the four facets to reveal the content of comprehensive security terms (such as national security, human security), we can build comprehensive taxonomies of security for various user groups.This paper develops a tetra-facet model of security, and demonstrates the application of thematic analysis and facet analysis to solve a complex problem of security categorization.