Reviewerly/exHarmony huggingface dataset:
The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling

In the era of big data and ubiquitous internet connectivity, user feedback data plays a crucial role in product development and improvement. However, extracting valuable insights from the vast pool of unstructured text data found in user feedback presents significant challenges. In this paper, we propose an innovative approach to tackle this challenge by combining the Contextualized Topic Model (CTM) and the Masked and Permuted Pre-training for Language Understanding (MPNet) model. Our approach aims to create a more accurate and context-aware topic model that enhances the understanding of user experiences and opinions. To achieve this, we first search for the optimal number of topics, focusing on generating distinguishable, general, and unique topics. Next, we perform hyperparameter optimization to fine-tune the model and maximize coherence metrics. The result is an exceptionally effective model that outperforms established topic modeling methods, including LSI, NMF, LDA, HDP, NeuralLDA, ProdLDA, ETM, and the default CTM, achieving the highest coherence CV score of 0.7091. In this study, the combination of CTM and MPNet has proven highly effective in the context of user feedback topic modeling. This model excels in generating coherent, distinguishable, and highly relevant user feedback topics, capturing the nuanced nature of user feedback data. The topics generated from this model include ’Music and Audio Streaming,’ ’Application Performance,’ ’Banking, Financial Services, and Customer Support,’ ’User Experience,’ ’Other Topics,’ ’Application Content,’ and ’Application Features.’ Our contributions include a powerful tool for developers to gain deeper insights, prioritize actions, and enhance user satisfaction by incorporating feedback into future product iterations. Furthermore, we introduce a new dataset as an open-source resource for further exploration and validation of user feedback analysis techniques and general natural language processing applications. With our proposed approach, we strive to drive business success, improve user experiences, and inform data-driven decision-making processes, ultimately benefiting both developers and users alike.
Multi-Party Audit and Regulatory Mechanism for P2P Electricity Transaction Based on Distributed Traceable Linkable Group Signature

Peer-to-Peer (P2P) electricity transaction is an emerging power trading model, which can effectively solve the drawbacks of traditional power trading that requires the exist of intermediaries. As the application spreads, P2P electricity transaction faces some regulatory issues. At present, some existing regulatory models are based on centralized trusteeship centers. There is no distributed regulatory mechanism, and no dynamic addition and deletion mechanism to prevent malicious collusion of regulators. In this article, we propose a multi-party audit and regulatory mechanism for P2P electricity transaction, which is based on a dynamic distributed traceable linkable group signature (DDT-LGS) scheme. This solution realizes the distributed trace and audit for P2P electricity transaction, and further realizes the dynamic permission of regulators to join and exit. We give the system architecture of multi-party audit and regulatory mechanism, formally define the algorithm process, and give a specific DDT-LGS construction. Security and performance analysis shows that the proposed scheme holds strong security, more comprehensive functions and better performance, that is suitable for P2P power trading scenarios.
Dilated CNN Design Approach for Extracting Multi-Scale Features in Radar Emitter Classification

Radar emitter classification plays an increasingly significant role in the electronic reconnaissance system. Due to many convolutional neural network (CNN)-based approaches suffer from insufficient spatial receptive fields and inadequate feature representation, the classification accuracy is poor in low signal-to-noise ratio (SNR) conditions. Therefore, in this paper, we stress the importance of multi-scale dilated convolutions for target feature extraction, and propose two novel CNN architecture design approaches called multi-scale dilated residual network (MDRN). By combining multi-scale dilated convolutions with residual architecture, MDRN not only has a larger receptive field, but also can learn more diverse features, thereby improving the ability to process time-frequency images (TFI) under high-noise energy conditions. Moreover, compared with the original residual model, MDRN does not increase any parameter complexity or floating-point operations per second (FLOPS). Experiments on the TFI classification task show that the proposed MDRN has superior performance over state-of-the-art CNN models.
YOLOAL: Focusing on the Object Location for Detection on Drone Imagery

Object detection in drone-captured scenarios, which can be considered as a task of detecting dense small objects, is still a challenge. Drones navigate at different altitudes, causing significant changes in the size of the detected objects and posing a challenge to the model. Additionally, it is necessary to improve the ability of the object detection model to rapidly detect small dense objects. To address these issues, we propose YOLOAL, a model that emphasizes the location information of the objects. It incorporates a new attention mechanism called the Convolution and Coordinate Attention Module (CCAM) into its design. This mechanism performs better than traditional ones in dense small object scenes because it adds coordinates that help identify attention regions in such scenarios. Furthermore, our model uses a new loss function combined with the Efficient IoU (EIoU) and Alpha-IoU methods that achieve better results than the traditional approaches. The proposed model achieved state-of-the-art performance on the VisDrone and DOTA datasets. YOLOAL reaches an AP50 (average accuracy when Intersection over Union threshold is 0.5) of 63.6% and an mAP (average of 10 IoU thresholds, ranging from 0.5 to 0.95) of 40.8% at a real-time speed of 0.27 seconds on the VisDrone dataset, and the mAP on the DOTA dataset even reaches 39% on an NVIDIA A4000.
A Real-time Background Replacement System Based on Estimated Depth for AR Applications

Recent technological advances in Virtual Reality (VR) and Augmented Reality (AR) enable users to experience a high-quality virtual world. The AR technology is attracting attention in various fields and is also used in the entertainment field such as museums. However, the existing AR technology generally requires specialized sensors such as Light Detection And Ranging (LiDAR) sensors and feature points, which require cost in terms of time and money. The authors have proposed a real-time background removal method and an AR system based on the estimated depth of the captured image to provide a virtual space experience using mobile devices such as smartphones. This paper describes an AR virtual space system that dynamically changes the replaced background based on motion information transmitted from the user's device.
God, Home, and Thinking in the Place: What Kind of Pantheism Did Thoreau Endorse?

Scholars have drawn on Thoreau’s thoughts and writings as a basis for environmental virtue ethics. His ecological vision of Home and ‘self-in-the-place’ have also inspired ecological restoration projects. However, a connection between his idea of God, Home, and what I call “thinking in the place” requires further exploration. In this chapter, I reconstruct several strands of his thought, seeking to answer the question: What kind of pantheism did Thoreau endorse? I will discuss his ideas about the restorative power of nature, his commitment to ‘living deliberately,’ his appreciation of simplicity, his advocacy of walking as a practice for peaceful living, and his disdain for materialistic living as an obstacle to the enjoyment of true happiness. The aim of the chapter is to inquire into the religious and ecological significance of pantheism in Thoreau’s writings by analyzing the articulations between his visions of God, the Home, and Self. Thoreau’s religious vision was deeply contextualized in certain episodes of enlightenment or ecological conversion that he commonly referred to in his writings as his ecstasies. These experiences accompanied him throughout his life and may help shed light on the kind of pantheism he espoused.
How to build a MATLAB demonstrator solving dynamical systems in real-time, with audio output and MIDI control

This paper explains and provides code to synthesize and control, in real-time, the audio signals produced by a dynamical system. The code uses only the Matlab programming language. It can be controlled with an external MIDI (Musical Instrument Data Interface) device, such as a MIDI keyboard or wind controller, or with mouse-operated sliders. In addition to the audio output, the demonstrator computes and displays the amplitude and fundamental frequency of the signal, which is useful to quantify the dynamics of the model. For the sake of this example, it is a type of Van der Pol oscillator, but more complex systems can be handled. The demonstrator holds potential for pedagogical and preliminary research applications, for various topics related to dynamical systems: direct and inverse bifurcations, transient effects such as dynamical bifurcations, artifacts introduced by integration schemes, and above all, the dynamics of self-sustained musical instruments.
“Galchonok” magazine as a space for children’s creativity

Introduction. The forms of work with readers’ creativity in pre-revolutionary children’s publications were not very diverse – the children’s literary and art magazine “Galchonok” (1911–1913) became an exception in this regard, providing readers with the opportunity to participate in the magazine’s creative projects. The purpose of the study is to analyze the editorial strategy of the Galchonok journal in terms of working with children's creativity. Innovative forms and principles of organizing and activating the creative activity of readers are examined, and the features of the communicative behavior of the editorial staff are simultaneously identified. Materials and methods. The contents of the “Mailbox” section have been analyzed. Conclusions about the creative attitudes of the editors are made on the basis of essays about art published in the magazine, articles and notes with practical creative advice. The forms of publication of readers’ works, creative competitions and joint projects, as well as editorial comments to them, are considered in detail. Result and Discussion. Public feedback on the works sent by readers and the playful communicative behavior of the editors contributed to the formation on the pages of “Galchonok” of a cross-cutting narrative dedicated to the creative activity of readers. It was extrapolated to other materials of the magazine, ensuring the interaction of fictional plots with appeals to the real activity of readers. At the same time, the organization of this activity was based not only on competitions for the best works, but also on projects in which the works of readers were given formality and conceptualization of a collective work. Conclusion. The participation of readers in the creation of magazine materials led to the formation of a specific creative environment based on the principles of communication and play. The findings can add to the consideration of the continuity between the pre-revolutionary and Soviet stages of the development of children’s journalism in terms of interaction with the creativity of readers.
Generation of Multi-Layered QR Codes with Efficient Compression

In this modern data-centric era, securing information that is transferred over the internet is a serious concern. Various cryptographic and stenographic methods play a vital role in this purpose. In this work, we develop a multi-layered steganographic system capable of hiding payloads in different hidden layers inside QR codes. Furthermore, each layer is compressed and encrypted separately to secure the payload data. Hence, we use the QR (Quick Response) Code as the cover medium to achieve and implement the above principles. In the proposed multi-layered QR Code approach, the top layer works as a standard QR Code while the layers beneath it act as private data. The secret is efficiently compressed to accommodate more information within the same space provided, thereby increasing the holding capacity of the QR code. The data in each layer are retrieved using different methods according to the type of compression used during the encoding process of the specified layer. The proposed model can store up to nine hidden layers of data. Various robustness tests are carried out to validate the efficiency of the hidden layers in the QR code and to check whether the hidden layers can withstand alterations without any data loss. Importantly, the resulting QR code works identically to a normal version upon scanning with any typical QR code scanner.
Spoken Language Identification Using Linear Frequency Residual Cepstral Coefficients

This paper aims to identify the spoken language of the person given the utterances using Linear Frequency Residual Cepstral Coefficients (LFRCC). Experiments were performed on statistically meaningful and computationally challenging VoxLingua107 database. For the fair comparison of the efficiency and use-fullness, LFRCC, Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) were extracted and considered as features. We have used one classifier, namely, Residual Network (ResNet). With this classifier, we achieved EER of 10.19%, 9.46%, and 9.37% for ResNet on MFCC, LFCC, and LFRCC, respectively. Additionally, we improved overall performance using score-level and feature-level fusion of the said feature sets on the said classifier. This helped us achieve EER of 7.4875% on the score-level fusion of MFCC and LFRCC and 7.01% on the feature-level fusion of LFRCC and MFCC. In this paper, we discuss how linear prediction (LP) residual captures pitch source harmonic information, melodic, and prosody information, which is found to be helpful in Spoken Language Identification (SLID).
Handling Small Disjuncts and Class Skew Using Sequential Ellipsoidal Partitioning

Data irregularities, such as small disjuncts, class skew and imbalance, and outliers significantly affect the performance of classifiers. In this paper, we focus on identifying small disjuncts, which hitherto, has been addressed mainly by rule-based or inductive algorithms. Small disjuncts have been identified as distribution-based irregularities which provide significant learning, although they cover a subset of examples in the training set, which may be considered as being rare. Such samples are more error-prone than large disjuncts. Eliminating small disjuncts by removal or pruning is seen to affect the learning of the classifier adversely. Widely used non-rule-based learning algorithms like SVM, kNN, Logistic Regression, and Neural networks perform poorly in the presence of small disjuncts in the dataset. In this paper, a novel Sequential Ellipsoidal Partitioning method is proposed to identify small disjuncts in the dataset. This method is a supervised classifier that iteratively partitions the dataset into Minimum Volume Ellipsoids that contain points of the same label; this is performed based on the idea of Reduced Convex Hulls. By allowing an ellipsoid that contains points of one label to contain a few points of the other, such small disjuncts may be identified. As we discuss, the proposed technique is agnostic of underlying data distributions and is applicable as a supervised classifier when the datasets are highly skewed and imbalanced even. We demonstrate the performance of the approach using a few publicly available datasets.
Domain Adapted Few-Shot Learning for Breast Histopathological Image Classification

The inaccessibility of high numbers of annotated data and imbalance dataset’s classes is a common phenomenon in medical microscopic image datasets. Also, collecting data from the same domain is a challenging task in microscopic imaging (e.g. same magnification factor, staining method, cancer type, imaging modality). A Conventional Deep Learning model with lack of labeled training data and domain shift could result in a misdiagnosis for such datasets. To overcome this problem, we formulated a domain adaptive few-shot learning (DA-FSL) problem and measured the perception capability of DA-FSL benchmark for medical histopathological image classification (especially for breast histopathological imaging). DA-FSL model overcomes a few-shot learning problem with domain shift by training the network on available source domain data but being able to test on target domain data with less number of available samples. DA-FSL models are validated on two publicly available breast cancer histopathological image datasets i.e., BreakHis and BreastCancer_IDC_Grade (BCIG). The presented DA-FSL framework has shown promising outcomes on both datasets. For performance evaluation we compared DA-FSL framework with other transfer learning and few-shot learning methods with different settings.
3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation

Accurate localization and volumetric quantification of post-operative glioblastoma are of profound importance for clinical applications like post-surgery treatment planning, monitoring of tumor regrowth, and radiotherapy map planning. Manual delineation consumes more time and error prone thus automated 3-D quantification of brain tumors using deep learning algorithms from MRI scans has been used in recent years. The shortcoming with automated segmentation is that it often over-segments or under-segments the tumor regions. An interactive deep-learning tool will enable radiologists to correct the over-segmented and under-segmented voxels. In this paper, we proposed a network named Attention-SEV-Net which outperforms state-of-the-art network architectures. We also developed an interactive graphical user interface, where the initial 3-D segmentation of contrast-enhanced tumor can be interactively corrected to remove falsely detected isolated tumor regions. Attention-SEV-Net is trained with BraTS-2021 training data set and tested on Uppsala University post-operative glioblastoma dataset. The methodology outperformed state-of-the-art networks like U-Net, V-Net, Attention U-Net and Residual U-Net. The mean dice score achieved is 0.6682 and the mean Hausdorff distance-95 got is 8.96 mm for the Uppsala University dataset.
Transfer Learning: Kernel-Based Domain Adaptation with Distance-Based Penalization

This paper introduces a novel approach to address the challenges of transfer learning, which aims to efficiently train a classifier for a new domain using supervised information from similar domains. Traditional transfer learning methods may fail to maintain the discriminative features of the target domain due to the scarcity of labelled data and the use of irrelevant source domain data distribution subspace, resulting in poor metrics. To overcome these challenges, the proposed approach, called KDADP, transforms the data distribution of both the source and target domains into a lower-dimensional subspace while preserving their discriminatory information. The KDADP model maximizes between-class variance and minimizes within-class variance with L1 penalization, enabling the recovery of the most useful characteristics and reducing the model’s complexity. Experimental results on three real-world domain adaptation datasets demonstrate that the proposed KDADP model significantly improves classification performance and outperforms state-of-the-art primitive, shallow, and deeper domain adaptation methods.
A Novel Graph Representation Learning Approach for Visual Modeling Using Neural Combinatorial Optimization

The human visual system is a complex network of neurons that employs robust mechanisms to perceive and interpret the environment. Despite significant advancements in computer vision technologies in recent years, they still need to improve compared to human abilities, particularly in recognizing faces and interpreting scenes. As a result, there is a growing interest in understanding the underlying mechanisms of human vision. Artificial Intelligence (AI) systems, specifically computer vision models, represent the assigned task using a learned or systematically generated vector space known as the Latent Space. However, the field of brain representation space remains relatively less explored. Despite significant progress, a research gap exists in generating an optimal representation space for human visual processing. While graph-based representations have been proposed to better capture inter-region relationships in visual processing, learning an optimal graph representation from limited data remains a challenge, especially when there is no ground truth. Due to the lack of labeled data, supervised learning approaches are less preferred. The present study introduces a novel method for graph-based representation of the human visual processing system, utilizing Neural Combinatorial Optimization(NCO). We have obtained an accuracy of 60% from our proposed framework, which is comparable to other methods for eight class classification in Visual Modelling.
Annihilation of Image Stegogram Through Deep Texture Extraction Based Sterilization

This work presents a deep feature-based image sterilization algorithm that effectively mutilates stego-secrets concealed within lossless cover images using any steganography scheme without oppressing the perceptual quality of the cover. The proposed method learns the possible steganalytic features such as edges, corners, textures, blobs etc., through a convolution based deep feature extractor network and produces an edge map locating probable stego-secret rich regions of a suspected stego-image. Sterilization by pixel modification is performed by constructing a hexagonal skeleton of the given image using the edge map. The use of deep features and the hexagonal neighbourhood reduces the degradation of visual quality caused by spatial modifications of image pixels. The efficacy of the method is assessed on an experimental image set of size 45,135 containing 5,015 images from USC-SIPI and BossBase 1.01 dataset and their secret-embedded versions generated by eight benchmark steganography schemes. The proposed method is observed to produce optimal outcomes in terms of sterilization percentage, sterilization time and visual quality among the relevant state-of-the-art works.
Semi-supervised Contrastive Regression for Estimation of Eye Gaze

With the escalated demand of human-machine interfaces for intelligent systems, development of gaze controlled system have become a necessity. Gaze, being the non-intrusive form of human interaction, is one of the best suited approach. Appearance based deep learning models are the most widely used for gaze estimation. But the performance of these models is entirely influenced by the size of labeled gaze dataset and in effect affects generalization in performance. This paper aims to develop a semi-supervised contrastive learning framework for estimation of gaze direction. With a small labeled gaze dataset, the framework is able to find a generalized solution even for unseen face images. In this paper, we have proposed a new contrastive loss paradigm that maximizes the similarity agreement between similar images and at the same time reduces the redundancy in embedding representations. Our contrastive regression framework shows good performance in comparison to several state of the art contrastive learning techniques used for gaze estimation.