Reviewerly/exHarmony huggingface dataset:
A Catalog of Source Code Metrics – A Tertiary Study

Context: A large number of source code metrics are reported in the literature. It is necessary to systematically collect, describe and classify source code metrics to support research and practice. Objective: We aim to utilize existing secondary studies to develop a catalog of source code metrics together with their descriptions. The catalog will also provide information about which units of code (e.g., operators, operands, lines of code, variables, parameters, code blocks, or functions) are used to measure the internal quality attributes and the scope on which they are collected. Method: We conducted a tertiary study to identify secondary studies reporting source code metrics. We have classified the source code metrics according to the measured internal quality attributes, the units of code used in the measures, and the scope at which the source code metrics are collected. Results: From 711 secondary studies, we identified 52 relevant secondary studies. We reported 423 source code metrics together with their descriptions and the internal quality attributes they measure. Source code metrics predominantly incorporate function as a unit of code to measure internal quality attributes. In contrast, several source code metrics use more than one unit of code when measuring internal quality attributes. Nearly 51% of the source code metrics are collected at the class scope, while almost 12% and 15% of source code metrics are collected at module and application levels, respectively. Conclusions: Researchers and practitioners can use the extensive catalog to assess which source code metrics meet their individual needs based on the description and classification scheme presented.
EDCGAN: Encoder Decoder based Conditional GAN for SAR to Optical image translation ✱

High-resolution optical images are heavily utilized in various remote sensing applications. The optical images cannot reflect the actual ground information in cloudy conditions. SAR images are used to solve this for their ability to see through clouds. But SAR images are usually available with coarser resolutions. So, there is a need to produce an optical image from a SAR image to overcome bad weather and poor resolution in a single go. In this paper, a novel deep learning architecture named EDCGAN is proposed. The proposed architecture is an encoder-decoder-based conditional GAN that uses multi-scale attentive discrimination to get accurate SAR to RGB image translation. In addition, we have used residual connections, spatial & channel-wise attention for better feature representation. A set of extensive experimentations show that this architecture outperforms the existing state-of-the-art method in terms of PSNR, SSIM, and FSIM_c values for the WHU-SENCity dataset.
Efficient-PSP-Net: A Lightweight Model for the Segmentation of Liver and Tumor in CT Scans

The segmentation of the liver and tumors present in the liver is an essential task for the diagnosis of liver cancer and surgical treatment planning. While most of the works were developed with the goal of improving segmentation accuracy, there is a high demand for the development of lightweight architectures to deploy in clinical settings with limited computational resources. Developing such resource-efficient models while maintaining the performance of the state-of-the-art architectures is challenging due to the high class imbalance and small observable changes between tumorous and non-tumorous regions, which would generally require extensive and complex deep learning architectures. The contributions of this paper are twofold; First, this paper proposes to encode information extracted at different scales using a “Pyramid Pooling Module” (PPM) architecture. While different variants of pyramidal architectures are widely used in the context of scene parsing in computer vision, this paper explores the use of the PPM architecture for the segmentation of liver and tumor. Second, the paper proposes using the popular lightweight “Efficient-Net” as the backbone network of PSP-Net for feature extraction, thereby reducing the overall model size while achieving relatively similar performance to state-of-the-art architectures. The performance of the proposed model is evaluated on the MICCAI 2017 LiTS dataset. The model achieved a DSC of 95.06% and 79.08% for liver and tumor, respectively, with only 1.76M parameters.
Quaternion Factorized Simulated Exposure Fusion

Image Fusion maximizes the visual information at each pixel location by merging content from multiple images in order to produce an enhanced image. Exposure Fusion, specifically, fuses a bracketed exposure stack of poorly lit images to generate a properly illuminated image. Given a single input image, exposure fusion can still be employed on a ‘simulated’ exposure stack, leading to direct single image contrast and low-light enhancement. In this work, we present a novel ‘Quaternion Factorized Simulated Exposure Fusion’ (QFSEF) method by factorizing an input image into multiple illumination consistent layers. To this end, we use an iterative sparse matrix factorization scheme by representing the image as a two-dimensional pure quaternion matrix. Theoretically, our representation is based on the dichromatic reflection model and accounts for the two scene illumination characteristics by factorizing each progressively generated image into separate specular and diffuse components. We empirically prove the advantages of our factorization scheme over other exposure simulation methods by using it for the low-light image enhancement task. Furthermore, we provide three exposure fusion strategies which can be used with our simulated stack and provide a comprehensive performance analysis. Finally, in order to validate our claims, we show extensive qualitative and quantitative comparisons against relevant state-of-the-art solutions on multiple standard datasets along with relevant ablation analysis to support our proposition. Our code and data are publicly available for easy reproducibility and reference. 1
Enhanced Kernelized Conditional Spatial Fuzzy C Means Algorithm for Noisy Brain MRI Tissue Segmentation

This paper proposes an Enhanced Kernelized Conditional Spatial Fuzzy C Means (EKCSFCM) clustering algorithm targeted at segmentation of Brain Magnetic Resonance (MR) image data. The approach amalgamates kernel mapping with a spatially relevant fuzzy clustering paradigm for image segmentation. A technique for rank-based consideration of spatial information is introduced and is used along with spatial tuning parameters for controlling neighborhood effects during data point clustering. The kernelized approach and the spatial parameters handle intensity inhomogeneities and strengthen the performance of the algorithm by improving resiliency against noise and other aberrations. For analyzing the robustness of the approach, four volumes of brain MR data, each comprising 71 images are used. These images are also corrupted with noise and inhomogeneities for evaluating the efficiency of different approaches in noisy image segmentation. The experimentation results along with the qualitative and quantitative inferences verify the robustness of the algorithm across a wide variety of test cases.
A Hybrid Rigid and Non-Rigid Motion Approximation for Generating Realistic Listening Behavior Videos

The use of generated behavioral videos, particularly in a controlled fashion, is an emerging research direction in multi-modal interaction research. Apart from speaking-behavior, listening-behavior understanding is an important problem. Listening behaviors play a crucial role in many important dyadic conversations in day-to-day life. The interviewer uses cues from the listener’s behavior to judge a given candidate. A recent study has demonstrated the use of one-shot deep-fake generation models like the first-order motion model (FOMM) to transfer the behavior of an actor onto any single facial image for listening behavior understanding. Such an experiment requires the creation of multiple motion-transfer videos for different participants. FOMM, compared to other DeepFake models, provides a unique option of transferring any actor’s motion to a target source image without training multiple models for every participant. In addition, the unsupervised keypoint learning process allows FOMM to generalize to different kinds of motions. However, to generate high-quality output, it makes a first-order approximation of any motion. This limits its application, especially for transformations consisting of multiple rigid bodies and non-rigid body-based motions, especially for facial movements. As a result, these models often generate artifacts around the lip and eye regions. To circumvent this issue, in our work we have proposed a hybrid model which can approximate a given motion using both first-order and zero-order motion. Compared to the FOMM, our approach gives the network the flexibility to decide on the type of approximation it wants to make for a given keypoint. Results show that our model not only prevents distortion for non-rigid body motion but also generates better quality output when compared with FOMM.
MuSense: Sensor Data Fusion-inspired Intelligent Music Improvisation Framework in 5G-Internet of Music Things

This article focuses on the emerging music composition, reconstruction, and improvisation in the Internet of Things (IoT). In the current internet era, musical composition and reconstruction schemas are largely dependent on the IoT-based paradigm where human beings can arrange musical compositions through musical things, such as, remotely arranged singing and musical instruments, smart rhythmic behaviors, and intelligent autotuning systems. We have renamed the music composition, sharing, and recommendation phenomenon in the IoT paradigm as the Internet of Music Things (IoMT). We present a fifth-generation (5G) technology-enabled IoMT framework to explore remote music collaboration, multisensory music information fusion, and socially-desirable music composition schema due to its capability of providing ultra-low latency, prominent bandwidth, assured Quality of Services, and lively orchestrated network environment. We incorporate the system performance metrics with the proposed 5G-IoMT in terms of the data transmission latency and potential energy dissipation. We moreover illustrate a Generative Adversarial Network-inspired intelligent music improvisation strategy as a case study for audience interest.
Low Resource Degraded Quality Document Image Binarization – Domain Adaptation is the Way

Usually, image binarization plays a crucial role in automatic analysis of degraded documents from their captured images. However, this binarization task is often difficult due to a number of reasons including the high similarity between noisy background and faded foreground pixels. The study presented here is particularly focused on binarization of images of low-resource degraded quality documents based on a set of recently collected image samples of several rare, ancient and severely degraded quality printed documents of Bangla, the 2nd and 5th most popular script of India and the world respectively. This new collection of degraded document image samples will henceforth be referred as ’ISIDDI2’ and it consists of 139 images of Bangla old document pages. Samples of ’ISIDDI’, another existing database of degraded Bangla document image samples, have also been used in the present study. A novel deep architecture based on attention UNET++ with dilated convolution operation is proposed for this binarization task. The model is optimized using human vision perceptible distance reciprocal distortion (DRD) loss. Since the binarization ground truth of samples of both ’ISIDDI2’ and ’ISIDDI’ are not available, the proposed network has been trained using samples of DIBCO and H-DIBCO datasets and an unsupervised domain adaptation (DA) module is employed for adaptation of the proposed architecture to the degradation patterns of ’ISIDDI2’ or ’ISIDDI’ samples. The proposed binarization strategy includes certain post-processing operation based on a modified k-neighbourhood based approach for recovery of broken characters. Results of our extensive experimentation show that the proposed binarization strategy has improved the binarization output of state-of-the-art methods on both ISIDDI2 and ISIDDI datasets. Also, its performance on well-known DIBCO samples is satisfactory.
Cross-Domain Class-Contrastive Learning: Finding Lower Dimensional Representations for Improved Domain Generalization

Domain Generalization (DG) requires a model to learn a hypothesis from multiple distributions that generalizes to an unseen distribution. Recent explorations show that, for neural networks, the choice of hyper-parameters and model architecture significantly affects DG performance, and making the right choice is non-trivial. In this paper, we show evidence suggesting that the models that perform better at DG, might be implicitly learning a low dimensional representation in the feature space. Furthermore, we take forward this idea and employ explicit feature learning to improve DG. To this end, we propose a DG specific supervised contrastive loss. We show how this performance improvement correlates to reduced dimensionality of the representation. Our work establishes new state-of-the-art on five different DG benchmarks, compared against over two dozen existing approaches in DomainBed. We show how this performance improvement correlates to reduced dimensionality of the representation.
Towards Robust Handwritten Text Recognition with On-the-fly User Participation

Long-term OCR services aim to provide high-quality output to their users at competitive costs. It is essential to upgrade the models because of the complex data loaded by the users. The service providers encourage the users who provide data where the OCR model fails by rewarding them based on data complexity, readability, and available budget. Hitherto, the OCR works include preparing the models on standard datasets without considering the end-users. We propose a strategy of consistently upgrading an existing Handwritten Hindi OCR model three times on the dataset of 15 users. We fix the budget of 4 users for each iteration. For the first iteration, the model directly trains on the dataset from the first four users. For the rest iteration, all remaining users write a page each, which service providers later analyze to select the 4 (new) best users based on the quality of predictions on the human-readable words. Selected users write 23 more pages for upgrading the model. We upgrade the model with Curriculum Learning (CL) on the data available in the current iteration and compare the subset from previous iterations. The upgraded model is tested on a held-out set of one page each from all 23 users. We provide insights into our investigations on the effect of CL, user selection, and especially the data from unseen writing styles. Our work can be used for long-term OCR services in crowd-sourcing scenarios for the service providers and end users.
A Globally-Connected and Trainable Hierarchical Fine-Attention Generative Adversarial Network based Adversarial Defense

Deep Neural Network (DNN) inferences have been proven highly susceptible to carefully engineered adversarial perturbations, presenting a pivotal hindrance to real-world Computer Vision tasks. Most of the existing defenses have poor generalization ability due to their dependence on relatively limited Adversarial Examples (AE). Furthermore, the existing adversarial training necessitates continually retraining a target network with the sort of attack required to be repelled. The defense strategies that are primarily based on processing the perturbed image eventually fall short when pitted against constantly developing threats. Protection of DNN against adversarial attacks remains a difficult challenge on challenging datasets such as Fashion MNIST and CIFAR10. This paper proposes a GAN-based two-stage adversarial training model named Globally Connected and Trainable Hierarchical Fine Attention (GCTHFA). The first stage of the proposed GCTHFA GAN is to create a reconstructed image that is a purified version of an adversarial example. The proposed approach has used a trainable and globally linked attention map to teach the Generator about the different types of representations an image might have in different convolutional layers located at different levels in a network. The discriminator’s reliance on feature vectors produced by transfer learning eliminates the traditional dependency on standard image pixels. The second step involves adversarial training of a target classifier to provide resistance to such attacks. Extensive testing on the MNIST, Fashion MNIST, and CIFAR10 datasets with different classifiers and attacks show that the proposed model can handle adversarial attack settings for various target models. The proposed model uses only one type of adversarial training, with no requirement for retraining based on the type of attack.
Depth estimation using Stereo Light Field Camera✱

Light field imaging has emerged as a new modality, enabling to capture the angular and spatial information of a scene. This additional angular information is used to estimate the depth of a 3-D scene. The continuum of virtual view-points in light field data efficiently handles occlusion and provides a robust depth estimate for smaller distances. However, a narrow baseline in a light field camera limits the depth estimation for larger distances. To have an efficient occlusion handling and increase the operating distances, we proposed a novel disparity based stereo light field depth estimation method. First, segments are obtained in central sub-aperture of left view and then estimate the disparity vector of these segments using left camera sub-aperture images. This handles occlusion efficiently. Then stereo disparity at boundaries of these segments exploiting the epipolar geometry inherent in a light field data. Finally this stereo disparity at boundaries is propagated to other pixels and normalized. We provided a synthetic stereo light field data-set having inherent characteristic of a light field. We have tested our approach on a variety of real-world scenes captured with Lytro Illum camera and also on synthetic images. The proposed method outperforms several state-of-the-art algorithms.
Design of a System and Method for Optimal selection of Tumor Slice using Linear Ultrasound Imaging for Histopathology

In excision biopsy, a tumor mass is surgically removed from the body. Subsequently, it is sliced at an appropriate location and investigated microscopically through a process called histopathology. Any bias in tumor slicing severely influences histopathology outcomes, such as if malignant foci do not appear in the sliced location, then, the tumor would be accidentally reported non-malignant. The standard approach adopted to solve this challenge by a histopathologist is to overcome this bias by slicing at multiple locations for their investigation. Till now, this process has been manual, time-consuming, and error-prone. We aim to design a system and develop a data-driven deep learning approach to assist histopathologists by providing them with a representative slice location for reporting to increase their efficiency and accuracy. We have developed a low cost linear gantry scanner that can acquire images and integrated with a deep learning model to predict the optimal slice representative of pathology in a tumor mass. We achieve an F1 score of 0.97 and an accuracy of 97.5% in predicting an optimal slice using this approach.
People Detection and Tracking Using Ground LiDAR

People detection and tracking are crucial issues in various fields, such as surveillance, security, and intelligent transportation systems. This paper presents a people detection and tracking method using light detection and ranging (LiDAR) set in an environment. People detection is achieved using a one-dimensional convolutional neural network (1D-CNN) together with the background subtraction method. Regions of interest are detected based on the background subtraction method, and people are detected in those regions using 1D-CNN. The detected people are tracked using the interacting multimodel estimator; people positions, velocities, and behaviors, such as stopping, walking, and suddenly rushing out, are estimated. Simulation and real-world experiments are conducted using a Velodyne 32-layer LiDAR. The experimental results show that the people tracker conjunction with people detection using both the 1D-CNN and background subtraction method enables accurate multipeople tracking.
Design of Intelligent Manufacturing Training Platform Combining Virtual and Real

Through the combination of virtual and real, taking typical product production and processing as the main task, using virtual reality simulation technology, the intelligent manufacturing production line is presented in virtual software, so that researchers have a conceptual understanding of the mechanical structure, electrical structure and production mode of the production line. . Establish a multi-disciplinary and modular practice system to provide targeted practical content for engineering training for researchers of different professions, help everyone better understand and master the relevant processes, processes and operating specifications in real production, and achieve in-depth learning. Effectively improve engineering practice ability, innovative spirit and engineering quality.
Application of Computer Network Technology in Human Movement Recognition

With the rapid development of computer vision technology, human motion recognition technology has occupied an important position in this field. It has important practical value and research value in security protection, advanced human-computer interaction, video search analysis and sports analysis. This paper mainly studies the application of computer network technology in human movement recognition. In this paper, the human body structure is decomposed firstly, and the efficiency of human motion recognition can be improved by decomposing the human body structure. Then, this paper uses STFT technology to study and analyze the extraction of human motion features, and uses SVM classifier to classify and identify the motion. In this paper, after modifying firmware and recompiling system, action data is obtained from common PC and commercial wireless network card, and action recognition is carried out. It can be seen from the experimental results that the accuracy rate of human movement recognition based on computer network proposed in this paper is 96.70%, which can effectively identify and describe human movements.
Hybrid Models That Combine Machine Learning and Simulations

Simulation experts are now well acquainted with machine learning (ML) techniques, using them to find patterns in data that can later be turned into rules of a simulation or enabling their simulated entities to adapt and learn. In the other direction, ML experts occasionally make use of simulated data to create controlled experiments in which learning algorithms can be evaluated. In this article, we go beyond these typical uses by focusing on current opportunities that have the potential to bring the two research communities together. These opportunities can be realized in areas where the potential of hybrid ML/simulation methods has not been fully attained yet. Such applications also motivate the development of innovative methods, for example, to combine the accuracy of ML with the interpretability of simulation models. Using select examples from our interdisciplinary team, this article reflects on opportunities in applications and techniques to promote productive conversations across research areas.