Reviewerly/exHarmony huggingface dataset:
Robust patchmatch HDR image reconstruction for deghosting

Aiming at the deghosting problem of High Dynamic Range (HDR) imaging technology, different from the existing algorithms of simply eliminating moving objects or only using a separate pre-registration based on homography transformation or optical flow, we propose an integrated HDR imaging scheme including Registration and Matching based on Patch (HDR-RMP) for deghosting in complex dynamic scenes. In our scheme, the combination of Affine Transformation (AT) and Normalization Mutual Information (NMI) has an implicit registration effect on the input images. Furthermore, NMI and Structural Similarity Index Measurement (SSIM) are fit to establish the final optimal function including the strategies of registration and matching, and the registration accuracy is promoted by this way, thereby the ability to remove ghosts is improved and the time complexity is reduced simultaneously. Finally, the accuracy of image matching is enhanced. The results show that the deghosting effect of HDR-RMP algorithm is remarkable, especially in dynamic scenes with large and mass moving objects, which not only improves the fusion quality, but also reduces the algorithmic complexity.
An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining

Opinion summarization recapitulates the opinions about a common topic automatically.The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text.The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining.This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory (RNN-LSTM) deep learning model for short and exact synopsis using seq2seq model.It presents a paradigm shift with regard to the way extractive summaries are generated.Novel algorithms for word extraction using assertions are proposed.The semantic framework is well-grounded in this research facilitating the correct decision making process after reviewing huge amount of online reviews, considering all its important features into account.The advantages of the proposed solution provides greater computational efficiency, better inferences from social media, data understanding, robustness and handling sparse data.Experiments on the different datasets also outperforms the previous researches and the accuracy is claimed to achieve more than the baselines, showing the efficiency and the novelty in the research paper.The comparisons are done by calculating accuracy with different baselines using Rouge tool.
Mental Health Analysis of Indians During Pandemic

The Covid-19 pandemic has severely affected many countries around the globe in terms of physically as well as mentally. During the initial months of the pandemic have reported India’s deficient cases, but eventually the cases were proliferated as the time progress. The government’s decision to impose a lockdown without warning has a wide-ranging impact, affecting everyone from low-wage workers to huge corporations. As a result, there is a negative impact on people’s mental health and emotions. The people had suffered from depressions, anxiety, fatigue and so forth. Many wide varieties of the people had expressed their thoughts, viewpoints, and their mental conditions in the form of tweets over the Twitter, a social media platform. Hence, in this paper, we have statistically analysed the data of tweeted tweets to elicit the meaningful insights. The data was analysed using the unsupervised clustering strategy–K-means and LDA–and the results were reinforced and validated using the pre-trained supervised classification approach–Text to Text transformer. The anticipated data depicted that the fear was the most common state of mind at the end of the lockdown, followed by joy, anger, and sadness. Furthermore, the deduced insights will be highly beneficial in decision-making process when such an epidemic or pandemic situation re-surges.
Sensor Model-Based Trajectory Optimization for UAVs Using Nonlinear Model Predictive Control

In this paper, we present an approach to enhance the overall detection performance in the field of unmanned aerial reconnaissance with imaging sensors. We achieve this task by generating optimized reference trajectories for UAVs through a recurring process of sequential path planning and trajectory optimization with nonlinear model predictive control. These trajectories are setpoints for the movement of the UAV in a spatio-temporally varying potential field, which represents the detection performance. We utilize known sensor performance models to map the sensor position dependent detection performance under the impact of numerous influencing factors. These are perceptual, sensor platform and environmental aspects as well as mission objectives. Our approach can be briefly summarized as follows: first, a discrete sensor footprint planning is performed via coverage path planning in the area to be reconnoitered. For each footprint, a perception map is created, which is a state-dependent representation of the detection performance in the UAV's flight plane. The environmental state vector is mapped to the perception map via the sensor performance model. Next, in a cyclic manner, a fan-shaped path planning is conducted starting from the current UAV position. Along these paths, the spatio-temporal detection performance is determined utilizing the perception maps. The cost for trajectory generation is then calculated for each path element using nonlinear model predictive control. A downstream evaluation of the path-dependent detection performance and the associated trajectory cost is used to determine the optimal path, which serves as a setpoint when generating the next section of the reference trajectory. This process is repeated until the end of the recon area is reached. We evaluate our approach conducting a series of simulated reconnaissance tasks for vehicle detection with a fixed wing UAV. In an area reconnaissance scenario, our approach shows an improvement in detection performance of about 5% compared to a primitive trajectory benchmark. Even for a route reconnaissance scenario, a slight improvement of about 0.6% compared to an already very high absolute benchmark value is measured.
The Dawn of Energy Efficient Computing: Optically Accelerating the Fast Fourier Transform Core

We present a novel approach in optical processing by accelerating Fourier transform through integration of silicon photonics and free-space optics. We introduce Fourier transform based multiply and accumulate operations, that require less number of operations compared to the traditional multiply and accumulate operations used in computer architecture.
Supervised pre-training for improved stability in deep reinforcement learning

Deep reinforcement learning (DRL) technology has been actively studied with the recent advances in deep learning. As a result, the researchers are continuously improving performance and expanding the applications. However, recent literature reports that the performance of DRL is sensitive to the various design choices, e.g., the neural network initialization. Accordingly, it makes DRL hard to obtain a stable performance, which degrades reproducibility. Therefore, we propose a supervised pre-training method for both policy and value networks to improve stability. We pre-train the policy network to maximize the initial entropy and pre-train the value network to bias the distribution to a specific value. The experiments are conducted on tasks with discrete action space where it is hard to control the initial entropy. Through the experiments, the effectiveness of the proposed method in terms of stability and performance is validated.
Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi-coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real-world optimization problem, and four well-established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.
Applying Density-based Clustering for Bloodstain Pattern Analysis

Bloodstain Pattern Analysis (BPA) is used by forensic officers to analyse bloodstains left at crime scenes. As a consequence, it has a crucial role in the investigations of bloody crimes. Currently, BPA activities are performed manually by leading to a slow and potentially imprecise analysis of the crime scenes. In order to overcome these issues, recently, some software tools have been developed in order to support forensic investigators in performing BPA activities in a more objective and fast way. However, only few approaches in literature use artificial intelligence methodologies to achieve this goal. Starting from this consideration, this paper presents a new intelligent tool based, for the first time, on the Density-Based Spatial Clustering of Application with Noise (DBSCAN) for supporting BPA activities. As shown in a case study, the proposed intelligent tool produces results that match with the ground truth.
Decoding Distorted Two-dimensional Barcodes using Combinatorial Optimization

2D codes are widely used as a shortcut from analog media to online information, and it has been used for authentication of boarding passes of airplanes and trains. Especially in recent years, 2D codes have become popular as a means of cashless payment. However, they are subject to distortion when printed on non-rigid materials, such as papers and clothes. Although general 2D code decoders correct uniform distortion such as perspective distortion, it is difficult to correct non-uniform and irregular distortion of the 2D code itself. This paper proposes a method that can decode 2D codes if they are distorted irregularly and non-uniformly. The proposed method formulates a problem of recognizing the relative position of edges constituting 2D code modules as a combinatorial optimization problem, and locates all modules to reconstruct 2D codes by solving the problem. Experimental results showed that the proposed method decodes the irregularly distorted 2D codes without the help of auxiliary patterns embedded in 2D codes.
A Secured Data Processing Model for Monitoring the Student’s Performance Using Decentralized Application

In a competitive world mostly in the education sector, many students are struggling to find their exact role and strength. To enrich the capacity of each student, this proposed system will help in monitoring their growth in their interested area. The major focus of the proposed system is to prove the knowledge from their own perspective. The detailed description of enhancing their growth is determined in such a way that each processing is open to the student and the organization by using a decentralized application. The entire system is developed using the blockchain process where the dataset is focused on the student database where each outcome is compared with the previous document used by the ledger and smart contract. Here, an application is designed on the basis of system processing to calculate their strength and weakness in their own area of interest. For example, if a student is concentrating on placement then he/she should involve in the training sessions and practical sessions so that the student dataset consisting of evaluation on the scorecard is stored in the blockchain ledger where everyone can view and send the suggestions to improve the score of next time. Though this process helps in making their interesting area a better one and a successful one, a smart application will be created so that a detailed analysis can be made in an efficient way. The only thing is end-user needs to show interest in a specific way. The graph is generated so that the end-user can utilize the processed data for their future use. This gives the betterment on making the student to follow regularly to meet the success in their path.
Optimal Branch Location for Cost-effective Inference on Branchynet

Deep Neural Networks (DNNs) are very popular in many machine learning domains. To achieve higher accuracy, DNNs have become deeper and larger. However, the improvement in accuracy comes with the price of the longer inference time and energy consumption. The marginal cost to increase a unit of accuracy has become higher as the accuracy itself is rising.The Branchynet, known as early exits, is an architecture to address increasing marginal cost for improving accuracy. The Branchynet adds extra side classifiers to a DNN model. The inference on a significant portion of the samples can exit from the network earlier via these side branches if they already have high confidence in the results.The Branchynet requires manually tuning the learning hyperparameters, e.g., the locations of branches and the confidence threshold for early exiting. The effectiveness of this manual tuning dramatically impacts the efficiency of the tuned networks. To the best of our knowledge, there are no efficient algorithms to find the best branch location, which is a trade-off between the accuracy and inference time on the Branchynet.We propose an algorithm to find the optimal branch locations for the Branchynet. We formulate the problem of finding the optimal branch location for the branchynet as an optimization problem, and prove that the branch placement problem is an NPcomplete problem. We then derive dynamic programming that runs in pseudo-polynomial time and solves the branch placement problem optimally.We also implement our algorithm and solve the branch placement problems on four types of VGG networks. The experiment results indicate that our dynamic programming can find the optimal branch locations for generating the maximum number of correct classifications within a given time budget. We also run the four VGG models on a GeForce RTX-3090 GPU with the branch combination found by the dynamic programming. The experiment results show that our dynamic programming accurately predicts the number of correct classifications and the execution time on the GPU.
A Simple Approach to Balance Task Loss in Multi-Task Learning

In multi-task learning, the training losses of different tasks are varying. There are many works to handle this situation and we classify them into five categories. In this paper, we propose a Balanced Multi-Task Learning (BMTL) framework. Different from existing studies which rely on task weighting, the BMTL framework proposes to transform the training loss of each task to balance different tasks based on an intuitive idea that tasks with larger training losses will receive more attention during the optimization procedure. We analyze the transformation function and derive necessary conditions as well as some properties. The proposed BMTL framework is very simple and it can be combined with most multi-task learning models. Empirical studies show the state-of-the-art performance of the proposed BMTL framework.
Locally Linear Embedding Based on t-Distribution Grasshopper Optimization Algorithm

Locally linear embedding (LLE) is a classical nonlinear dimensionality reduction algorithm for manifold learning in the field of machine learning (ML). The main idea of LLE is to pursue the linear isomorphism in the algebraic relationship between high-dimensional spatial data and low-dimensional spatial data after dimensionality reduction, and obtain the projection points of low-dimensional space by solving two optimization sub-problems. The optimization part of LLE is usually solved by gradient descent (GD) method. however, GD method has many disadvantages, such as easy to fall into the trap of local minimum, the closer to the optimal solution, the easier it is to show sawtooth effect. In order to overcome the above difficulties, in this paper, an LLE based on t-distribution grasshopper optimization algorithm has been proposed, it uses t-distribution grasshopper optimization algorithm which is a gradient independent swarm intelligence algorithm to replace GD for parameter optimization. Via numerical experiment, the effectiveness of the proposed algorithm has been proved.
A Hybrid Watermarking Scheme Using Walsh Hadamard Transform and SVD

With the development and advent of Internet communications and multimedia technology, it is of high importance to secure the digital media, including audio, video, speech, and images. In fact, watermarking technique provides an efficient solution to this problem. However, for an effective watermarking of data, several important requirements need to be met, such as transparency, low computational cost, robustness, and security. In this work, a robust hybridized medical images watermarking approach based on Walsh Hadamard Transform (WHT ) and singular value decomposition (S VD) is presented. Both of the host image and the watermark images were modified with singular values obtained by the (S VD) approach. The simulation results demonstrate that this proposed watermarking framework is highly robust and achieves high peak signal to noise ratio (PS NR) and high structure similarity index (S S IM).
MFHI: Taking Modality-Free Human Identification as Zero-Shot Learning

Human identification is an important topic in event detection, person tracking, and public security. There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification. Typically, existing methods predominantly classify a queried image to a specific identity in an image gallery set ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I2I</i> ). This is seriously limited for the scenario where only a textual description of the query or an attribute gallery set is available in a wide range of video surveillance applications ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A2I</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I2A</i> ). However, very few efforts have been devoted towards modality-free identification, i.e., identifying a query in a gallery set in a scalable way. In this work, we take an initial attempt, and formulate such a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> odality- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> ree <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> uman <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> dentification (named MFHI) task as a generic zero-shot learning model in a scalable way. Meanwhile, it is capable of bridging the visual and semantic modalities by learning a discriminative prototype of each identity. In addition, the semantics-guided spatial attention is enforced on visual modality to obtain interpretable representations with both high global category-level and local attribute-level discrimination. Finally, we design and conduct an extensive group of experiments on two common challenging identification tasks, including face identification and person re-identification, demonstrating that our method outperforms a wide variety of state-of-the-art methods on modality-free human identification.
DCTResNet: Transform Domain Image Deblocking for Motion Blur Images

Pixel recovery with deep learning has shown to be very effective for a variety of low-level vision tasks like image super-resolution, denoising, and deblurring. Most existing works operate in the spatial domain, and there are few works that exploit the transform domain for image restoration tasks. In this paper, we present a transform domain approach for image deblocking using a deep neural network called DCTResNet. Our application is compressed video motion deblur, where the input video frame has blocking artifacts that make the deblurring task very challenging. Specifically, we use a block-wise Discrete Cosine Transform (DCT) to decompose the image into its low and high-frequency sub-band images and exploit the strong sub-band specific features for more effective deblocking solutions. Since JPEG also uses DCT for image compression, using DCT sub-band images for image deblocking helps to learn the JPEG compression prior to effectively correct the blocking artifacts. Our experimental results show that both PSNR and SSIM for DCTResNet perform more favorably than other state-of-the-art (SOTA) methods, while significantly faster in inference time.
Efficient Local Image Descriptors Learned With Autoencoders

Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval. In this paper, we focus on learning local image descriptors in an unsupervised way, using autoencoders and variational autoencoders. We perform a thorough comparative analysis of these two approaches along with an in-depth analysis of the most relevant hyperparameters to guide their optimal selection. In addition to this analysis, we give insights into the difficulties and the importance of selecting right evaluation techniques during the unsupervised learning of the local image descriptors. We explore the extent to which a simple perceptual metric during training can predict the performance on tasks such as patch matching, retrieval and verification. Finally, we propose an improvement to the encoder architecture that yields significant savings in memory complexity, especially in single-image tasks. As a proof of concept, we integrate our descriptor into an inpainting algorithm and illustrate its results when applied to the virtual restoration of master paintings. The source code required to reproduce the presented results has been made available as a repository on GitHub (<uri>https://github.com/nimpy/local-img-descr-ae</uri>).