Reviewerly/exHarmony huggingface dataset:
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method

As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images and video places new demands on the image processing pipeline. In this paper, we consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method. The core components of LLFormer are the axis-based multi-head self-attention and cross-layer attention fusion block, which significantly reduces the linear complexity. Extensive experiments on the new dataset and existing public datasets show that LLFormer outperforms state-of-the-art methods. We also show that employing existing LLIE methods trained on our benchmark as a pre-processing step significantly improves the performance of downstream tasks, e.g., face detection in low-light conditions. The source code and pre-trained models are available at https://github.com/TaoWangzj/LLFormer.
A new multi-target tracking algorithm for a large number of orbiting objects

This paper presents the filter for Hypothesised and Independent Stochastic Populations (HISP), a multi-object joint detection/tracking algorithm derived from a recent estimation framework for stochastic populations, in the context of Space Situational Awareness. Designed for multi-object estimation problems where the data association between tracks and collected observations is moderately ambiguous, the HISP filter has a linear complexity with the number of objects and the number of observations. Because of its scalable complexity, the HISP filter is a promising solution for the construction of a large-scale catalogue of Resident Space Objects. We illustrate the HISP filter on a challenging surveillance scenario built from real data for 115 satellites of PlanetLabs’ Dove constellation, and simulated observations collected from two sensors with limited coverage and measurement noise, in the presence of false positives and missed detection.
High throughput hardware architecture for accurate semi-global matching

As the most important step of a stereo vision system, stereo matching, which finds the correspondences in stereo image pairs, requires high-quality real-time depth computation. In this paper, a high throughput and high accuracy full-pipeline hardware architecture with disparity and row parallelism is proposed. In the semi-global aggregation stage, to improve the accuracy in discontinuous regions, adaptive weighted path costs are adopted, and, five aggregation paths are used without consuming external memory resources. Finally, L-R check, spike removal and disparity confidence check handle the outlier regions to enhance the disparity map. Disparity map quality evaluations on Middlebury benchmark and real-world outdoor scenery show that the proposed implementation is able to generate high quality disparity maps. The proposed hardware architecture is implemented on a Stratix V FPGA, which results in a throughput of 1280 × 960/197 fps with 64 disparity levels at 156 MHz. Compared with the latest work, we can get 1.48 times speedup and use 19% more memory with much less computation resources (34.5% less ALUTs and 46.1% less Regis-ters).
Multisource Domain Attribute Adaptation Based on Adaptive Multikernel Alignment Learning

For attribute-based zero-shot learning (ZSL), the attribute classifiers learned previously on the training images may not be usable for the testing images due to that the training and testing images may follow different data distributions. Since domain adaptation learning can effectively perform knowledge transfer under the circumstance of different data distributions, we proposed a novel ZSL method, referred to as multisource domain attribute adaptation based on adaptive multikernel alignment learning (A-MKAL), from the point of view of classifier adaptation. Considering there may be a large difference between object classes, we adopt the clustering method to group the training images according to the class–class correlation measured by the whitened cosine similarity, thus multiple source domains are created. The created multiple source domains are then combined into one weighted source domain to participate in the distribution discrepancy match across domains. In order to adapt the attribute classifier learned on the well-defined source domains to the target domain (the training image set), we designed the A-MKAL by applying the centered kernel alignment to align the attribute kernel matrix and the kernel function of adaptive multiple kernel learning. Experiments on Shoes, OSR, and AWA datasets show that, compared with state-of-the-art methods, our proposed method yields more accurate classification.
Efficient identity-based broadcast encryption with keyword search against insider attacks for database systems

Public key encryption with keyword search (PEKS) is a promising technique for data confidentiality and utilization. In cloud database systems, the user (sender) could want to share his data with multiple users (receivers), while the ciphertext complexity increases linearly with the number of receivers if directly employing PEKS. Also, the security of PEKS is weakly defined in presence of outsider attacks, where the untrusted server infers the keyword in trapdoor by insider attacks. Therefore, how to reduce the size of the ciphertext and resist insider attacks remain unresolved. In this paper, we propose the first efficient identity-based broadcast encryption with keyword search against insider attacks (IBEKS-IA) for cloud database systems. We build the IBEKS-IA framework to provide data retrieval for multiple receivers and resist insider attacks. Based on this framework, we construct an IBEKS-IA scheme featuring with constant size ciphertext, which is proved to be semantically secure against the chosen keyword attack and the server keyword guessing attack. We further give an extended version featuring with both the constant size ciphertext and constant size online trapdoor. Performance evaluation presents the practicality of the proposal in the cloud database systems.
Mood-aware visual question answering

The concept of Visual Question Answering (VQA) has recently attracted the attention of many researchers in the field of machine learning. Different attention models have been proposed in VQA for the purpose of addressing the need to focus on local regions of an image. This paper proposes the concept of Mood-Aware Visual Question Answering (MAVQA) using novel long short term memory (LSTM) and convolutional neural network (CNN) attention models that combine the local image features, the question and the mood detected from the particular region of the image to produce a mood-based answer using a pre-processed image dataset. The attention mechanisms serve to enable the VQA model to only focus on parts of the image that are relevant to both the detected mood and the key words in the question. The irrelevant parts of the image are ignored, thus improving classification accuracy by reducing the chances of predicting wrong answers. Whereas previous efforts have utilized CNN mostly for the embedding of images and text, we formulate a CNN attention algorithm for the image, question and mood. The more direct convolutional attention operation is more efficient and effective, when the number of views and kernel length are optimized, than the winding recurrent LSTM attention operation. The experimental results prove that MAVQA is effectively mood-aware, and the accuracy levels of our LSTM attention model are well within the range of previous conventional VQA benchmarks, while our novel CNN attention model outperforms the previous baselines in several instances. The additional attention on the mood does not only improve classification accuracy but also substantially contributes towards the analysis and comprehension of image features, a key development in modern artificial intelligence.
Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features

Automated diagnosis of pathological brain not only reduces the diagnostic error significantly but also improves the patient's quality of life, thereby addressing the sustainability issues. The last few decades have witnessed an intensive research on binary classification of brain magnetic resonance (MR) images. Multiclass classification of pathological brain MR images is a more challenging task and the literature on this problem is still in its infancy. In this paper, we propose a new automated diagnosis system to classify the brain MR images into five different categories. Texture features within MR images play a significant role in accurate and efficient pathological brain detection. This work presents the extraction of such vital texture features by calculating the entropy over the curvelet subbands. Two faster and simpler strategies of fast curvelet transform are separately employed for feature extraction and the derived features are termed as FCEntF-I and FCEntF-II. The features are finally subjected to kernel extreme learning machine (K-ELM) for classification. The effectiveness of the proposed scheme is evaluated on multiclass as well as binary brain MR datasets. Comparisons with state-of-the-art methods indicate the superiority of the proposed scheme. The discriminatory potential of FCEntF-I and FCEntF-II features is found better than its counterparts.
Constrained niching using differential evolution

The work presented here involves development and detailed investigations of niching methods for multimodal optimization of constrained functions. There is a lack of investigations in the literature on constrained multimodal optimization, hence a number of constrained niching algorithms have been developed here that leverage existing differential evolution-based niching methods with a feasibility rules-domination selection procedure. Furthermore, a suite of 18 benchmark functions has been developed and are presented in this paper; nine newly developed functions are incorporated with nine existing functions from the literature. Optimization results on these analytical functions using the constrained niching algorithms are presented, with analysis provided on the ability to locate multiple global optima, the convergence speed and constraint handling nature of the methods. The differential evolution strategy is also investigated, with SHADE and L-SHADE strategies considered. Finally, a dimensionality study also compares against the only other known constrained niching algorithm. Results indicate that all of the algorithms developed and tested generally perform well for low dimensional, low modality problems, but that local neighbourhood-based methods show the best results across the suite of functions tested. When high-dimensional problems are considered, using the L-SHADE strategy yields excellent results. An accompanying supplementary data file is provided with the manuscript.
Fully Convolutional Video Captioning with Coarse-to-Fine and Inherited Attention

Automatically generating natural language description for video is an extremely complicated and challenging task. To tackle the obstacles of traditional LSTM-based model for video captioning, we propose a novel architecture to generate the optimal descriptions for videos, which focuses on constructing a new network structure that can generate sentences superior to the basic model with LSTM, and establishing special attention mechanisms that can provide more useful visual information for caption generation. This scheme discards the traditional LSTM, and exploits the fully convolutional network with coarse-to-fine and inherited attention designed according to the characteristics of fully convolutional structure. Our model cannot only outperform the basic LSTM-based model, but also achieve the comparable performance with those of state-of-the-art methods
A new fast algorithm to compute continuous moments defined in a rectangular region

In this research, we have developed a new algorithm to compute the moments defined in a rectangular region. By applying the recurrent formulas, symmetry properties, and particularly the parallelized matrix operations, our proposed computational method can improve the efficiency of computing Legendre, Gegenbauer, and Jacobi moments extensively with highly satisfied accuracy. To verify this new computational algorithm, the image reconstructions from the higher orders of Legendre, Gegenbauer, and Jacobi moments are performed on a testing image sized at 1024 × 1024 with very encouraging results. It took only a few seconds to compute moments and conduct the image reconstructions from the 1000-th order of the Legendre, Gegenbauer, and Jacobi moments with the PSNR values up to 45. By utilizing our new algorithm, image analysis and recognition applications using the higher orders of moments defined in a rectangular region in the range of milliseconds will be possible.
Design and Implementation of a Machine Learning-Based Authorship Identification Model

In this paper, a novel approach is presented for authorship identification in English and Urdu text using the LDA model with n -grams texts of authors and cosine similarity. The proposed approach uses similarity metrics to identify various learned representations of stylometric features and uses them to identify the writing style of a particular author. The proposed LDA-based approach emphasizes instance-based and profile-based classifications of an author’s text. Here, LDA suitably handles high-dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing, and the inherent ambiguity of the Urdu language. A large corpus has been used for performance testing of the presented approach. The results of experiments show superiority of the proposed approach over the state-of-the-art representations and other algorithms used for authorship identification. The contributions of the presented work are the use of cosine similarity with n -gram-based LDA topics to measure similarity in vectors of text documents. Achievement of overall 84.52% accuracy on PAN12 datasets and 93.17% accuracy on Urdu news articles without using any labels for authorship identification task is done.
An investigation on adaptive HTTP media streaming Quality-of-Experience (QoE) and agility using cloud media services

Web services is software entity allows machine to machine communication, operates as standalone unit and interoperability over network using standard web technologies such as Hypertext transfer protocol (HTTP) and eXtensible markup language (XML). With the rapid growth of cloud platform, infrastructure and emergence of multiple Digital transformation technologies, more and more conventional applications are transformed into web-based services. Service-based approach creates tremendous impact on multimedia content storage, retrieval, delivery and communication. With cloud infrastructure and service, get economically viable, real time, high resolution multimedia content on-the-go anywhere and everywhere using adaptive http streaming approach. This paper presents convergence of adaptive http streaming from conventional infrastructure to cloud enabled model and presents the advancement and architecture of adaptive http streaming delivery as a web-based service delivery approach. The detailed performance evaluation of conventional and cloud enabled service-based approaches were presented, and the metrics were compared from real-time experiment. By analyzing performance indices and characteristics, cloud enabled multimedia service delivery approach poses advantages, gives promising result and better user experience compare to the conventional adaptive HTTP streaming approach.
Parallel membership queries on very large scientific data sets using bitmap indexes

Summary Many scientific applications produce very large amounts of data as advances in hardware fuel computing and experimental facilities. Managing and analyzing massive quantities of scientific data is challenging as data are often stored in specific formatted files, such as HDF5 and NetCDF, which do not offer appropriate search capabilities. In this research, we investigated a special class of search capability, called membership query , to identify whether queried elements of a set are members of an attribute. Attributes that naturally have classification values appear frequently in scientific domains such as category and object type as well as in daily life such as zip code and occupation. Because classification attribute values are discrete and require random data access, performing a membership query on a large scientific data set creates challenges. We applied bitmap indexing and parallelization to membership queries to overcome these challenges. Bitmap indexing provides high performance not only for low cardinality attributes but also for high cardinality attributes, such as floating‐point variables, electric charge, or momentum in a particle physics data set, due to compression algorithms such as Word‐Aligned Hybrid. We conducted experiments, in a highly parallelized environment, on data obtained from a particle accelerator model and a synthetic data set.
Image To Latex with DenseNet Encoder and Joint Attention

Mathematical formula structural analysis usually converts mathematical formulas in images into Latex codes. It has been named as Image2Latex by OpenAi. At present, many researchers use the model in the field of image captioning for image2latex and have achieved good results. In this paper, we propose some improvements to the baseline model which is a sequence-to-sequence model used in image caption. We improve the encoder by employing densely connected convolutional network (DenseNet) because it can strengthen feature extraction and facilitate gradient propagation. We propose to use a more effective joint attention mechanism which include both spatial attention and channel-wise attention to solve this problem. We conducted experiments on the dataset im2latex-100k. Experimental results showed that our model improved the performance of formula analysis.
Depth of Field Rendering Using Multilayer-Neighborhood Optimization

Depth of field (DOF) is utilized widely to deliver artistic effects in photography. However, existing post-processing techniques for rendering DOF effects introduce visual artifacts such as color leakage, blurring discontinuity, and the partial occlusion problems which limit the application of DOF. Traditionally, occluded pixels are ignored or not well estimated although they might make key contributions to images. In this paper, we propose a new filtering approach which takes approximated occluded pixels into account to synthesize the DOF effects for images. In our approach, images are separated into different layers based on depth. Besides, we utilize adaptive PatchMatch method to estimate the intensities of occluded pixels, especially in the background region. We again propose a new multilayer-neighborhood optimization to estimate occluded pixels contributions and render the images. Finally, we apply gathering filter to achieve the rendered images with elite DOF effects. Multiple experiments have shown that our approach can handle color leakage, blurring discontinuity and partial occlusion problem while providing high-quality DOF rendering effects.
Fractional-Order Integration Based Fusion Model for Piecewise Gamma Correction Along With Textural Improvement for Satellite Images

Fractional-order integration (FOI) and its beauty of optimally ordered adaptive filtering for image quality enhancement are latently too valuable to be casually dismissed. With this motivation, a new Riemann-Liouville fractional-order calculus-based spatial-masking methodology is proposed in this paper in association with counterbalanced piecewise gamma correction (PGC). A generalized FOI-based mask is also suggested. This mask is negatively augmented with the original image for harvesting texture-based benefits. PGC is just employed through a constructive association of both kinds of reciprocally dual gamma values (γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = γ and γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> = 1/γ, ∀γ > 1), which leads to optimally desired enhancement when applied in a weighted counter-correction manner. Efficiently improved and recently proposed opposition-based learning inspired sine-cosine algorithm is employed in this paper, along with a newly framed fitness function. This fitness function is devised in a novel manner by taking care of textural as well as non-textural details of the images. In this paper, especially for dark images, 130% increment is achieved over the input contrast along with the simultaneous 147% increment in the discrete entropy level and 22.8% increment in the sharpness content. Also, brightness and colorfulness are reported with 130% and 196.4% increased with respect to the input indices, respectively. In addition, the textural improvement is advocated in terms of desired comparative reduction of gray-level co-occurrence matrix-based metrics, namely, correlation, energy, and homogeneity, which are suppressed by 25.6%, 72.5%, and 21.8%, respectively. This performance evaluation underlines the excellence and robustness for imparting proper texture as well as edge preserved (or efficiently restored) image quality improvement.
Modeling and controlling of quadrotor aerial vehicle equipped with a gripper

Arm mounted unmanned aerial vehicles provide more feasible and attractive solution to manipulate objects in remote areas where access to arm mounted ground vehicles is not possible. In this research, an under-actuated quadrotor unmanned aerial vehicle model equipped with gripper is utilized to grab objects from inaccessible locations. A dual control structure is proposed for controlling and stabilization of the moving unmanned aerial vehicle along with the motions of the gripper. The control structure consists of model reference adaptive control augmented with an optimal baseline controller. Although model reference adaptive control deals with the uncertainties as well as attitude controlling of unmanned aerial vehicle, baseline controller is utilized to control the gripper, remove unwanted constant errors and disturbances during arm movement. The proposed control structure is applied in 6-degree-of-freedom nonlinear model of a quadrotor unmanned aerial vehicle equipped with gripper having (2 degrees of freedom) robotic limb; it is applicable for the simulations to desired path of unmanned aerial vehicle and to grasp object. Moreover, the efficiency of the presented control structure is compared with optimal baseline controller. It is observed that the proposed control algorithm has good transient behavior, better robustness in the presence of continuous uncertainties and gripper movement involved in the model of unmanned aerial vehicle.
Ans2vec: A Scoring System for Short Answers

Automatic scoring is a complex task in computational linguistics, particularly in an educational context. Sentences vectors (sent2vec) approaches affirmed their prosperity recently as favorable models for sentence representation. In this research, we propose an efficient and uncomplicated short answer grading model named Ans2vec. Skip-thought vector approach is used to convert both model and student’s answers into meaningful vectors to measure the similarity between them. Ans2vec model achieves promising results on three different benchmarking data sets. For Texas data set; Ans2vec achieves the best Pearson correlation value (0.63) compared to all related systems.