Reviewerly/exHarmony huggingface dataset:
Fast Optimal Transport for Latent Domain Adaptation

In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the target data is unknown. We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score.

In this paper, we present a new process for creating video summaries in an unsupervised manner. Our approach involves training a transformer encoder model to reconstruct missing frames in a video in a self-supervised way using the partially masked video as input. We then introduce an algorithm that utilizes the above-trained encoder to generate an importance score for each frame. Such frame importance scores are used to create the summary of the video. We show that the reconstruction loss of the model for a video with masked frames correlates with the representativeness of the remaining frames in the video. We validate the effectiveness of our approach on two benchmark datasets of TVSum and SumMe. We demonstrate that it outperforms state-of-the-art (SOTA) methods. Additionally, our approach is more stable during the training process compared to SOTA techniques based on generative adversarial learning. Our source code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Leveraging Efficient Training and Feature Fusion in Transformers for Multimodal Classification

People navigate a world that involves many different modalities and make decision on what they observe. Many of the classification problems that we face in the modern digital world are also multimodal in nature, where textual information on the web rarely occurs alone, and is often accompanied by images, sounds, or videos. The use of transformers in deep learning tasks has proven to be highly effective. However, the relationship between different modalities remains unclear. This paper investigates ways to simultaneously utilize self-attention over both text and vision modalities. We propose a novel architecture that combines the strengths of both modalities. We show that combining a text model with a fixed image model leads to the best classification performance. Additionally, we incorporate a late fusion technique to enhance the architecture’s ability to capture multiple modalities. Our experiments demonstrate that our proposed method outperforms state-of-the-art baselines on Food101, MM-IMDB, and FashionGen datasets.
Semantic and Instance-Aware Pixel-Adaptive Convolution for Panoptic Segmentation

Although the weight-sharing property of convolution is one of the major reasons for the success of convolution neural networks, the content-agnostic operation is insufficient for several tasks requiring content-adaptive processing, including panoptic segmentation. Inspired by several recent works on content-adaptive convolutions, we introduce the GuidedPAKA, the first content-adaptive convolution method specialized for panoptic segmentation. Specifically, GuidedPAKA learns the pixel-adaptive kernel attention consisting of the channel and spatial kernel attentions. Instead of commonly used self-attention operation, we guide the channel and spatial kernel attentions using their respective supervision signals, i.e., semantic segmentation maps and local instance affinities. Consequently, these kernel attentions extract features helpful for panoptic segmentation. Experimental results show that the proposed GuidedPAKA improves the performance of panoptic segmentation when integrated into the baseline model.
CLOT: Contrastive Learning-Driven and Optimal Transport-Based Training for Simultaneous Clustering

Clustering via representation learning is one of the most promising approaches for self-supervised learning of deep neural networks. It aims at obtaining artificial supervisory signals from unlabeled data. In this paper, we propose an online clustering method called CLOT (Contrastive Learning-Driven and Optimal Transport-Based Clustering) that is based on robust and multiple losses training settings. More specifically, CLOT learns representations by contrasting both the features at the latent space and the cluster assignments. In the first stage, CLOT performs the instance- and cluster-level contrastive learning which is respectively conducted by maximizing the similarities of the projections of positive pairs (views of the same image) while minimizing those of negative ones (views of the rest of images). In the second stage, it extends standard cross-entropy minimization to an optimal transport problem and solves it using a fast variant of the Sinkhorn-Knopp algorithm to produce the cluster assignments. Further, it enforces consistency between the produced assignments obtained from views of the same image. Compared to the state of the arts, the proposed CLOT outperforms eight competitive clustering methods on three challenging benchmarks, namely, CIFAR-100, STL-10, and ImageNet-10 for ResNet-34.
Dual Temporal Transformers for Fine-Grained Dangerous Action Recognition

Recognizing dangerous actions is a critical task in computer vision, especially for surveillance applications. While existing deep learning methods have been successful in confined environments, they struggle with the anomalous and salient variations of human postures in dangerous actions. Additionally, finer-grained dangerous actions require more discriminative cues, adding to the complexity of the task. To address these challenges, we propose a novel solution that models the intrinsic and invariant properties of dangerous actions at multiple temporal semantic levels. Concretely, we propose a Dual Temporal Transformers (DTT) to capture temporal interactions between distinct key points in the human body aggregation from shallow to deep layers, increasing the perception field from local to global, simultaneously. By doing so, our method avoids overfitting to unrelated or minor clues in videos and achieves a generalized representation of abnormal actions. We evaluate our approach on indoor and outdoor environments and found that DTT outperforms existing methods in terms of efficiency and accuracy. Our code and dataset are pubic available on https://github.com/AveryJohnsonJJ/DTT.git.
Segmentation of the Left Ventricle by SDD double threshold selection and CHT

Automatic and robust segmentation of the left ventricle (LV) in magnetic resonance images (MRI) has remained challenging for many decades. With the great success of deep learning in object detection and classification, the research focus of LV segmentation has changed to convolutional neural network (CNN) in recent years. However, LV segmentation is a pixel-level classification problem and its categories are intractable compared to object detection and classification. In this paper, we proposed a robust LV segmentation method based on slope difference distribution (SDD) double threshold selection and circular Hough transform (CHT). The proposed method achieved 96.51% DICE score on the test set of automated cardiac diagnosis challenge (ACDC) which is higher than the best accuracy reported in recently published literatures.
NEV-NCD: Negative Learning, Entropy, and Variance Regularization Based Novel Action Categories Discovery

Novel Categories Discovery (NCD) facilitates learning from a partially annotated label space and enables deep learning (DL) models to operate in an open-world setting by identifying and differentiating instances of novel classes based on the labeled data notions. One of the primary assumptions of NCD is that the novel label space is perfectly disjoint and can be equipartitioned, but it is rarely realized by most NCD approaches in practice. To better align with this assumption, we propose a novel single-stage joint optimization-based NCD method, Negative learning, Entropy, and Variance regularization NCD (NEV-NCD). We demonstrate the efficacy of NEV-NCD in previously unexplored NCD applications of video action recognition (VAR) with the public UCF101 dataset and a curated in-house partial action-space annotated multi-view video dataset. Further, we perform a thorough ablation study by varying the composition of final joint loss and associated hyper-parameters. During our experiments with UCF101 and multi-view action dataset, NEV-NCD achieves ≈ 83% classification accuracy in test instances of labeled data. NEV-NCD achieves ≈ 70% clustering accuracy over unlabeled data outperforming both naive baselines and state-of-the-art pseudo-labeling-based approaches by ≈ 40% and ≈ 3.5% over both datasets.
Geometric Prior-Assisted Feature Presentation Enhancement for Object Detection in Aerial Images

Detecting objects in aerial images is an active yet challenging task due to the arbitrary orientation of aerial targets and the lack of details for small objects. Observing that geometric priors, e.g., some kinds of points and lines with specific geometrical properties, are helpful to determine the oriented bounding box, we proposed a Point-Line-Region (PLR) supervision module embedded in the Feature Pyramid Networks (FPN) to learn the robust geometrical features, which are conducive to evaluate and locate the orientation, vertexes, and boundary of the bounding box. Our method improves feature representations of aerial targets by utilizing the supervision of across-category geometrical semantics rather than their category semantics in the previous works. Extensive comparison experiments on the DOTA dataset prove that remarkable accuracy gains of mAP, about 2.1%, are achieved by integrating the PLR module in different architectures of networks.
Analyzing Disagreements in Argumentation Annotation of Scientific Texts in Russian Language

This paper presents the analysis of inter-annotator disagreements in modeling argumentation in scientific papers. The aim of the study is to specify annotation guidelines for the typical disagreement cases. The analysis focuses on inter-annotator disagreements at three annotation levels: theses identification, links construction between theses, specification of reasoning models for these links. The dataset contains 20 argumentation annotations for 10 scientific papers from two thematic areas, where two experts have independently annotated each text. These 20 annotations include 917 theses and 773 arguments. The annotation of each text has consisted in modelling its argumentation structure in accordance with Argument Interchange Format. The use of this model results in construction of an oriented graph with two node types (information nodes for statements, scheme nodes for links between them and reasoning models in these links) for an annotated text. Identification of reasoning models follows Walton’s classification. To identify disagreements between annotators, we perform an automatic comparison of graphs that represent an argumentation structure of the same text. This comparison includes three stages: 1) identification of theses that are present in one graph and absent in another; 2) detection of links that connect the corresponding theses between graphs in a different manner; 3) identification of different reasoning models specified for the same links. Next, an expert analysis of the automatically identified discrepancies enables specification of the typical disagreement cases based on the structural properties of argumentation graphs (positioning of theses, configuration of links across statements at different distances in the text, the ratio between the overall frequency of a reasoning model in annotations and the frequency of disagreements over its identification). The study shows that the correspondence values between argumentation graphs reach on average 78 % for theses, 55 % for links, 60 % for reasoning models. Typical disagreement cases include 1) detection of theses expressed in a text without explicit justification; 2) construction of links between theses in the same paragraph or at a distance of four and more paragraphs; 3) identification of two specific reasoning models (connected respectively to the 40 % and 33 % of disagreements); 4) confusion over functionally different schemes due to the perception of links by annotators in different aspects. The study results in formulating annotation guidelines for minimizing typical disagreement cases at each level of argumentation structures.
Genuine multipartite entanglement measures based on multi-party teleportation capability

Abstract Quantifying entanglement is vital to understand entanglement as a resource in quantum information processing, and many entanglement measures have been suggested for this purpose. When mathematically defining an entanglement measure, we should consider the distinguishability between entangled and separable states, the invariance under local transformation, the monotonicity under local operations and classical communication, and the convexity. These are reasonable requirements but may be insufficient, in particular when taking into account the usefulness of quantum states in multi-party quantum information processing. Therefore, if we want to investigate multipartite entanglement as a resource, then it can be necessary to consider the usefulness of quantum states in multi-party quantum information processing when we define a multipartite entanglement measure. In this paper, we define new multipartite entanglement measures for three-qubit systems based on the three-party teleportation capability, and show that these entanglement measures satisfy the requirements for being genuine multipartite entanglement measures. We also generalize our entanglement measures for N -qubit systems, where $$N \ge 4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> , and discuss that these quantities may be good candidates to measure genuine multipartite entanglement.
Demystifying Cyber Crimes

With the National Cybercrime Reporting portal witnessing an increase of 15.3% increase in the cyber cases in second quarter of 2022, the post pandemic world has seen a tremendous rise in the number of cybercrime cases. The cases have increased many folds, 125% from 2021, and still continues beyond. The targets of crime are not limited to any age group and innocent children have not been spared. The exposure to online classes, conferences, meetings, etc. has opened the door to criminal activities in a humongous way. Thus, there comes the need for each and every one of us to be well aware of the recent practices that these criminals use and not fall into the clutches of these nefarious cyber criminals.
Attacks on Web Applications

Online applications hold sensitive data that is valuable, and hackers strive to identify weaknesses and exploit them in order to steal data, pose as users, or disrupt the application. Web applications are increasingly exposed to dangers and attack vectors that are more advanced. Additionally, theft of private information—such as user credentials or billing information for credit cards—occurs frequently. Attackers initially concentrated on obtaining personal information that was accidentally exposed through poorly built or poorly protected web apps. Insecure design, security misconfiguration, vulnerable and outdated components, identification and authentication failures, etc. are some of the most prominent web application vulnerabilities that will be covered in this chapter. The authors are using countermeasures including fuzz testing, source code review, and encoding approaches to get around these vulnerabilities. As a result, this chapter offers information on the various attacks that website visitors who use web applications encounter.
Penetration Testing Building Blocks

Penetration testing is an art, and the path of its mastery starts from having a good grasp on fundamental knowledge. Throughout this chapter, readers will be made familiar with the building blocks in an easy-to-interpret manner. During the course of this chapter, readers will learn about topics such as penetration testing types, phases, dos &amp; don'ts, and types of OSINT (open source intelligence) methods including image OSINT, email OSINT, Google dorking and social media intelligence (SOCMINT). Please note that this chapter will not be very comprehensive. Readers are recommended to do their own research on such topics in order to gain more in-depth knowledge. Links to additional reading materials will be added in reference section at the end of the chapter. Readers are advised to go through them at least once to learn more comprehensive information about some chapter topics.
Automatic Video Object Detection Using Particle Swarm Optimisation in Fog Computing

In this chapter, the authors focus on optimising video surveillance by considering five factors while processing the surveillance. The five factors are bandwidth availability, connection availability, storage availability, CPU availability, and memory availability. Since the research work mainly focused on fog computing environment, two major factors are bandwidth and connection availability while sending a video content to the authorized person. The size of video content will be large and slow while transferring the data from one location to another location through wireless network. This problem will be optimized using particle swarm optimization algorithm (PSO). This proposed PSO algorithm will optimize the video content and make it effective and efficient to transfer from one location to another location, and then the object detection (OD) is conducted using suitable hardware arrangements involving a microcontroller unit and GSM communication module for altering the user with objects in forest area.
Software Defect Prediction Using Machine Learning Techniques

Software defect prediction gives development teams observable results while influencing business outcomes and development flaws. Developers can uncover flaws and plan test activities by anticipating problematic code sections. Early identification depends on the percentage of classifications that make the right prediction. Additionally, software-defective data sets are supported and partially acknowledged because of their vast size. The confusion, precision, recall, identification accuracy, etc., are assessed and compared with the existing schemes in a systematic research analysis. Previous research has employed the weak simulation tool for software analysis, but this study proposes building three machine learning models using linear regression, KNN classifier, and random forest (RF). According to the analytical investigation, the suggested approach will offer more beneficial options for predicting device failures. Moreover, software-defected data sets are supported and at least partially recognized due to their enormous dimension.
Developing a cloud evidence method for dynamic early warning of tunnel construction safety risk in undersea environment

Traditional methods have limitations in achieving precise predictions of risk occurrence at an exact future time and have difficulties transforming between qualitative and quantitative indicators and handling multi-source heterogeneous risk data. This study quantifies and analyzes the multi-source construction safety risks classified into the categories of man, machine, material, method and environment (4M1E), and presents a cloud evidence method that integrates wavelet de-noising algorithm, cloud model, and Dempster-Shafer (D-S) evidence theory. A real-time risk prediction and warning is provided using this method after the fusion of multi-source uncertain information and the transformation between qualitative and quantitative indicators, enabling the timely detection of potential risks for project managers. This method analyzing “uncertainty” with “certainty” is verified by an undersea tunnel construction project. The result shows that this method is effective in early warning risks two days before their actual occurrence, providing reference significance for risk early warning of the tunnel construction project.
An Enhanced Approach for Multi-Modal Sentimental Analysis in Natural Language Processing

Sentiment analysis is a common technique in artificial intelligence and natural language processing. The automated assessment of consumer comments on services or products is becoming more prevalent. Multi-modal sentiment analysis has evolved as a fascinating study topic that evaluates and interprets sentiment expressions using several modalities, such as text, pictures, audio, and video. This technique enables a more thorough knowledge of human emotions by capturing the interaction of many modalities. The capacity to assess sentiment across various modalities has several advantages over unimodal techniques. Multi-modal sentiment analysis improves performance by combining text, images, audio, and video information. This allows it to catch complimentary and contextually relevant cues. Improving the performance of multi-modal sentiment analysis covers several processes, including sentiment polarity identification, multi-modal feature extraction, and fusion.
MKCL: Medical Knowledge with Contrastive Learning model for radiology report generation

Automatic radiology report generation has the potential to alert inexperienced radiologists to misdiagnoses or missed diagnoses and improve healthcare delivery efficiency by reducing the documentation workload of radiologists. Motivated by the continuous development of automatic image captioning, more and more deep learning methods have been proposed for automatic radiology report generation. However, the visual and textual data bias problem still face many challenges in the medical domain. Additionally, do not integrate medical knowledge, ignoring the mutual influences between medical findings, and abundant unlabeled medical images influence the accuracy of generating report. In this paper, we propose a Medical Knowledge with Contrastive Learning model (MKCL) to enhance radiology report generation. The proposed model MKCL uses IU Medical Knowledge Graph (IU-MKG) to mine the relationship among medical findings and improve the accuracy of identifying positive diseases findings from radiologic medical images. In particular, we design Knowledge Enhanced Attention (KEA), which integrates the IU-MKG and the extracted chest radiological visual features to alleviate textual data bias. Meanwhile, this paper leverages supervised contrastive learning to relieve radiographic medical images which have not been labeled, and identify abnormalities from images. Experimental results on the public dataset IU X-ray show that our proposed model MKCL outperforms other state-of-the-art report generation methods. Ablation studies also demonstrate that IU medical knowledge graph module and supervised contrastive learning module enhance the ability of the model to detect the abnormal parts and accurately describe the abnormal findings. The source code is available at: https://github.com/Eleanorhxd/MKCL.