Reviewerly/exHarmony huggingface dataset:
A Decision Modeling Study with Fuzzy Cognitive Mapping Method on Customers’ Brand Preferences Criteria in Telecommunications Industry

Brand is one of the most important parts of businesses and many elements are brought together to create a brand. In order for customers to prefer brands, businesses need to consider both customer general and specific expectations, focus on the right points and offer the most appropriate solution to customers because customers consider many factors when making their brand choices. The purpose of this study is to determine which areas the companies operating in the telecommunications sector should focus on according to customer expectations and criteria for brand preference by using the Fuzzy Cognitive Mapping (FCM) method. Customer expectations and criteria are obtained as a result of literature research. After the data are prepared, the most effective criteria are determined with the FCM method. In this study, it is determined that which areas the companies operating in the telecommunications sector should focus on according to customer expectations and criteria. FCM is very useful to decide in complex situations like this. The greatest limitation of this study is determining the customer criteria for brand preference. This study shows that FCM can be very useful to determine the most crucial customer expectations for telecommunications sector. In addition, FCM can be used in other sectors where customer expectations are very important and decisive, to prioritize customer brand preference criteria and to guide businesses.
Blind Image Super-Resolution with Degradation-Aware Adaptation

Most existing super-resolution (SR) methods are designed to restore high resolution (HR) images from certain low resolution (LR) images with a simple degradation, e.g. bicubic downsampling. Their generalization capability to real-world degradation is limited because it often couples several degradation factors such as noise and blur. To solve this problem, existing blind SR methods rely on either explicit degradation estimation or translation to bicubicly downsampled LR images, where inaccurate estimation or translation would severely deteriorate the SR performance. In this paper, we propose a plug-and-play module, which could be applied to any existing image super-resolution model for feature-level adaptation to improve the generalization ability to real-world degraded images. Specifically, a degradation encoder is proposed to compute an implicit degradation representation with a ranking loss based on the degradation level as supervision. The degradation representation then works as a kind of condition and is applied to the existing image super-resolution model pretrained on bicubicly downsampled LR images through the proposed region-aware modulation. With the proposed method, the base super-resolution model could be fine-tuned to adapt to the condition of degradation representation for further improvement. Experimental results on both synthetic and real-world datasets show that the proposed image SR method with compact model size performs favorably against state-of-the-art methods. Our source code is publicly available at https://github.com/wangyue7777/blindsr_daa .
The duration threshold of video content observation: An experimental investigation of visual perception efficiency

Visual perception principle of watching video is crucial in ensuring video works accurately and effectively grasped by audience. This article proposes an investigation into the efficiency of human visual perception on video clips considering exposure duration. The study focused on the correlation between the video shot duration and the subject?s perception of visual content. The subjects? performances were captured as perceptual scores on the testing videos by watching time-regulated clips and taking questionnaire. The statistical results show that three-second duration for each video shot is necessary for audience to grasp the main visual information. The data also indicate gender differences in perceptual procedure and attention focus. The findings can help for manipulating clip length in video editing, both via AI tools and manually, maintaining perception efficiency as possible in limited duration. This method is significant for its structured experiment involving subjects? quantified performances, which is different from AI methods of unaccountable.
Causal Property Based Anti-conflict Modeling with Hybrid Data Augmentation for Unbiased Scene Graph Generation

Scene Graph Generation (SGG) aims to detect visual triplets of pairwise objects based on object detection. There are three key factors being explored to determine a scene graph: visual information, local and global context, and prior knowledge. However, conventional methods balancing losses among these factors lead to conflict, causing ambiguity, inaccuracy, and inconsistency. In this work, to apply evidence theory to scene graph generation, a novel plug-and-play Causal Property based Anti-conflict Modeling (CPAM) module is proposed, which models key factors by Dempster-Shafer evidence theory, and integrates quantitative information effectively. Compared with the existing methods, the proposed CPAM makes the training process interpretable, and also manages to cover more fine-grained relationships after inconsistencies reduction. Furthermore, we propose a Hybrid Data Augmentation (HDA) method, which facilitates data transfer as well as conventional debiasing methods to enhance the dataset. By combining CPAM with HDA, significant improvement has been achieved over the previous state-of-the-art methods. And extensive ablation studies have also been conducted to demonstrate the effectiveness of our method.
RepF-Net: Distortion-Aware Re-projection Fusion Network for Object Detection in Panorama Image

Panorama image has a large 360 $$^{\circ }$$ field of view, providing rich contextual information for object detection, widely used in virtual reality, augmented reality, scene understanding, etc. However, existing methods for object detection on panorama image still have some problems. When 360 $$^{\circ }$$ content is converted to the projection plane, the geometric distortion brought by the projection model makes the neural network can not extract features efficiently, the objects at the boundary of the projection image are also incomplete. To solve these problems, in this paper, we propose a novel two-stage detection network, RepF-Net, comprehensively utilizing multiple distortion-aware convolution modules to deal with geometric distortion while performing effective features extraction, and using the non-maximum fusion algorithm to fuse the content of the detected object in the post-processing stage. Our proposed unified distortion-aware convolution modules can be used to deal with distortions from geometric transforms and projection models, and be used to solve the geometric distortion caused by equirectangular projection and stereographic projection in our network. Our proposed non-maximum fusion algorithm fuses the content of detected objects to deal with incomplete object content separated by the projection boundary. Experimental results show that our RepF-Net outperforms previous state-of-the-art methods by 6 $$\%$$ on mAP. Based on RepF-Net, we present an implementation of 3D object detection and scene layout reconstruction application.
HaViT: Hybrid-Attention Based Vision Transformer for Video Classification

Video transformers have become a promising tool for video classification due to its great success in modeling long-range interactions through the self-attention operation. However, existing transformer models only exploit the patch dependencies within a video when doing self-attention, while ignoring the patch dependencies across the different videos. This paper argues that external patch prior information is beneficial to the performance of video transformer models for video classification. Motivated by this assumption, this paper proposes a novel Hybrid-attention based Vision Transformer (HaViT) model for video classification, which explicitly exploits both internal patch dependencies within a video and external patch dependencies across videos. Different from existing self-attention, the hybrid-attention is computed based on internal patch tokens and an external patch token dictionary which encodes external patch prior information across the different videos. Experiments on Kinetics-400, Kinetics-600, and Something-Something v2 show that our HaViT model achieves state-of-the-art performance in the video classification task against existing methods. Moreover, experiments show that our proposed hybrid-attention scheme can be integrated into existing video transformer models to improve the performance.
Heterogeneous Interactive Learning Network for Unsupervised Cross-Modal Retrieval

Cross-modal hashing has received a lot of attention because of its unique characteristic of low storage cost and high retrieval efficiency. However, these existing cross-modal retrieval approaches often fail to align effectively semantic information due to information asymmetry between image and text modality. To address this issue, we propose Heterogeneous Interactive Learning Network (HILN) for unsupervised cross-modal retrieval to alleviate the problem of the heterogeneous semantic gap. Specifically, we introduce a multi-head self-attention mechanism to capture the global dependencies of semantic features within the modality. Moreover, since the semantic relations among object entities from different modalities exist consistency, we perform heterogeneous feature fusion through the heterogeneous feature interaction module, especially through the cross attention in it to learn the interaction between different modal features. Finally, to further maintain semantic consistency, we introduce adversarial loss into network learning to generate more robust hash codes. Extensive experiments demonstrate that the proposed HILN improves the accuracy of $$T\rightarrow I$$ and $$I \rightarrow T$$ cross-modal retrieval tasks by 7.6 $$\%$$ and 5.5 $$\%$$ over the best competitor DGCPN on the NUS-WIDE dataset, respectively. Code is available at https://github.com/Z000204/HILN .
IMPRINT CYTOLOGY: A COST-EFFECTIVE ALTERNATIVE TEST FOR FROZEN SECTION IN INTRAOPERATIVE CONSULTATIONS FOR CANCER SURGERY

Introduction: Frozen Section (FS) helps in the typing of cancer – benign or malignant, tumor subtyping, assessment of margin status and lymph node involvement by the tumor preoperatively. Imprint smear (IS) study in place of Frozen section for intraoperative consultations can be conducted in those Institutes where facility of cryostat is unavailable. This study aims to determine the reliability of Imprint smear study done in intraoperative specimens as an alternative to frozen section by correlating with the Histopathological diagnosis. This is a Materials And Methods: retrospective study conducted at the Government Royapettah Hospital, Chennai over a one-year period from January 2020 till December 2020. The results of Imprint smear study in intraoperative specimens were compared with the histopathological results. We studied 96 per operative Results: specimens that included 48 lymph nodes, 23 ovary, 17 breast, 4 soft tissue, 2 thyroid, 1 bone and 1 cervix specimens. The Imprint study results, when compared with Histopathological diagnosis, showed an overall correlation of results for 83 specimens with a sensitivity of 98.60%, specicity of 52.0%, positive predictive value of 67.26%, negative predictive value of 97.38%, accuracy of 75.30% and p-value of 0.00 suggesting statistically signicant association. Based on the above results, wit Conclusion: h higher value of sensitivity and negative predictive value, the cases reported as negative for malignancy in imprint smear can be nalized as negative thereby preventing radical resection for clinically doubtful malignant cases diagnosed as benign lesions. Imprint smear also helps in providing a timely per operative diagnosis of malignancy guiding the operating surgeons for completion of radical surgery and thereby reducing a re-exploration surgery after diagnosing malignancy later in Histological sections. We conclude that an attempt to do imprint smear study should be done at every institution where cryostat facility is not available
A Study on Metric-Based and Initialization-Based Methods for Few-Shot Image Classification

Few-shot learning (FSL), or learning to generalize using few training data samples, is a particularly challenging problem in machine learning. This paper discusses various state-of-the-art distance metric-based and initialization-based FSL methods. It also gives a background on the meta-learning framework employed by many of the discussed models to generalize to novel classification tasks after training on multiple training tasks. We also discuss other techniques, such as whole-class classification, that have produced better results than meta-learning for metric-based methods.
Histopathological Nuclei Segmentation Using Spatial Kernelized Fuzzy Clustering Approach

Image segmentation is a crucial image processing step in many applications related to biomedical image analysis. One such key sector is high-resolution histopathological image segmentation for nuclei detection that aids in high-quality feature extraction for meticulous analysis in the domain of digital pathology for disease diagnosis. Manual nuclei detection and segmentation require domain expertise and are rigorous and time-consuming. The existing automated analytical tools are capable of nuclei segmentation, but there is a granulated segmentation technique selection and configuration management requirement for each analysis due to a wide variation in nuclei structures, along with overlapping and highly correlated image regions. In this paper, an unsupervised spatial kernelized fuzzy segmentation algorithm is presented for automated nuclei segmentation of light microscopy images of stained nuclei. The algorithm has stable performance across a wide gamut of image types without the need for experiment dependent adjustment of segmentation parameters. For performance analysis and rigorous use-case testing, a highly standardized dataset is obtained from the Data Science Bowl. The proposed algorithm manages to achieve segmentation accuracies in the range of 95–96% across varied image types which defends the robustness of the technique along with visual indications obtained from qualitative results.
Medical Prescription Label Reading Using Computer Vision and Deep Learning

One of the most crucial skills in a person’s daily life is handwriting. When it comes to making scripts for various professions, doctors, on the other hand, have been familiar with their low-quality handwriting for decades. To solve this, we demonstrated a deep learning-based method for detecting drug names from doctor’s prescriptions, which will benefit the public. The drug is first cropped from the image with reduced dimensions and then fed into two alternative architectures, CRNN alone and EAST + CRNN architecture. The cursive handwritten image is then converted to conventional text using these models. After obtaining the texts, the text is calculated using the CTC loss and the outcome is predicted.
Improved CNN Model for Breast Cancer Classification

In this paper, we propose an improved convolutional neural network for the automatic classification of breast cancer pathological images, with the goal of achieving more accurate results. It is also proposed to use two different convolutional structures to improve the accuracy of pathological image recognition by the network, which is covered in greater detail in the paper. After constructing the foundation network from a deep residual network, octave convolution is used to replace the traditional convolutional layer during the feature extraction stage. This reduces the number of redundant features in the feature map and improves the effect of the detailed feature extraction, both of which are beneficial. As a result of the introduction of heterogeneous convolution into the network, a portion of the traditional convolutional layers can be replaced, resulting in a reduction in the number of parameters necessary for model training. It is possible to overcome the problem of overfitting caused by a small number of data samples available by employing an effective data enhancement method based on the concept of the image block. In the experiments, the accuracy of the network at the image level on the four-classification tasks of the network is shown to be 91.25%. This means that the network model that was proposed has a better recognition rate and is faster in real time.
Introduction

UAVs are one of the greatest innovations made by human beings in almost all sectors, like e-commerce, defense, agriculture, surveillance, traffic management system, communication in remote areas, human organ transportation, chemical or drug delivery, entertainment sector, wildlife monitoring, etc. In addition, UAVs support disaster management during earthquakes, massive floods, forest fires, and Nuclear/chemical accidents. Figure 1.1 shows different UAV activities for disaster management. UAVs can be used in all three stages of disaster management; for example, in the pre-disaster stage, UAVs are used for surveillance, prevention, and early detection. During a disaster, they might be used for food packet/medicine delivery, monitoring the situation to support critical decision-making and prevent or reduce losses. Finally, after the disaster, UAVs can be used to monitor the situation, assess quickly, and estimate losses.
Adversarial Attacks Against Visually Aware Fashion Outfit Recommender Systems

Pre-trained CNN models are frequently employed for a variety of machine learning tasks, including visual recognition and recommendation. We are interested in examining the application of attacks generated by adversarial machine learning techniques to the vertical domain of fashion and retail products. Specifically, the present work focuses on the robustness of cutting-edge CNN models against state-of-the-art adversarial machine learning attacks that have shown promising performance in general visual classification tasks. In order to achieve this objective, we conducted adversarial experiments on two prominent fashion-related tasks: visual clothing classification and outfit recommendation. Large-scale experimental validation of the fashion category classification task on a real dataset of PolyVore consisting of various outfits reveals that ResNet50 is one of the most resilient networks for the fashion categorization task, whereas DenseNet169 and MobileNetV2 are the most vulnerable. Performance-wise however, DenseNet169 is the most time-consuming network to attack. However, the results of the outfit recommendation task were somewhat unexpected. In both of the push or nuke attack scenarios and altogether, it was demonstrated that adversarial attacks were unable to degrade the quality of outfit recommenders. The only exception was the more complicated adversarial attack of DeepFool, which could only weaken the quality of visual recommenders at large attack budget ( $$\epsilon $$ ) values. Numerous explanations could be provided for this phenomenon, which can be attributed to the fact that a collection of adversarially perturbed images can nonetheless appear pleasing to the human eye. This may possibly be a result of the greater image sizes in the selected dataset. Overall, the results of this study are intriguing and encourage more studies in the field of adversarial attacks and fashion recommendation system security.
Interference Fringe-Based Metrology for Industrial Applications

Optical 3D scanners have become widely used metrological tool for rapidly capturing dense 3D models of objects. The contact-based measurement techniques offer high accuracy but are hindered by the operational speed and ease of operation. The optic-based metrology, a non-contact approach, is an alternative which would offer reliable accuracy with high data acquisition rate. Interference property of light is one of the tools extensively used in optic-based metrology to derive measurements. In interference-based optical metrology, the resultant fringes which are a band of light intensity are analyzed by an optical transfer function which yields the 2D and 3D data. An extension of fringe frequency principle is digital fringe projection technique where fringes are digitally generated and illuminated on the component and the deformed fringes are analyzed by transfer functions to draw the 3D measurements. This article would brief sinusoidal fringe-based measurement techniques.
Is Social Media a Villain to Cyberbullying Victims?: Examining a Predictive Model of Cyberbullying Victimization Among U.S. Adults in the Context of Social Media

The purpose of this study is to provide an integrative model of cyberbullying victimization among U.S. adults in the context of social media. Based on previous research on cyberbullying victimization, key predictors such as individuals' traits (i.e., sex, age, race, income, employment status, and education) and psycho-social variable (i.e., self-esteem, social support and coping strategies) that may be linked with the relationships between social media usage and cyberbullying victimization were used in the model. The findings demonstrated that social media usage and social media users' sex, age, race, and education level have significantly influence on cyberbullying victimization. In addition, the model confirmed the mediating effects of self-esteem and social support among social media usage, individuals' traits and cyberbullying victimization.
Industry 4.0—Its Advancement and Effects on Security of Whistle-Blowers on Dark Web

The darknet has gotten perhaps the most examined points in network safety circles. As Internet is the source of communication in cyber physical space and dark web is a untraceable hidden layer in the virtual world. This paper investigates a new paradigm in the dark web space. It gives an insight and an oveíview of daík web and vaíious bíowseís which aíe used to access daík web foí unethical píactices. A darknet gatherings uncover an alternate part where one can relate and remain constantly vigilant to safeguard against cyber threats. The focus is to propose strategy and safeguard ourselves in the world where criminals are operating in the dark web which is untraceable. As witnessed the recent loophole created by whistle-blowers in dark web on Facebook, it was a threat to the organization as not only they lost their money but their existence as well. The aim is to analyze the situation and find recent techniques and trends to protect against such unknown threats. The dark web has become a source for attacks by criminals and makes the survival difficult. Using dark web for unlawful practices has become a regular practice for cyber criminals. Accumulated the interest of authorities and policymakers, people can get to the dark web by utilizing unique programming and accessing unethical practices.
Neural Network Experiment on PYNQ

This chapter presents modifiable frameworks for fast and easy neural network prototyping on the Xilinx PYNQ platform. With a Python-based programming interface, the framework combines the convenience of high-level abstraction with the speed of optimized FPGA implementation. We will introduce the theory of PYNQ acceleration through the following two experiments.