Reviewerly/exHarmony huggingface dataset:
Road Accident Data Annotation and Reporting (RADAR) - A Comprehensive Approach to Automate Accident Images Analysis Using Deep Learning Algorithms

Road accidents are unpredictable events that take lives of millions of people across the globe. Not all accidents are human error, they can be due to defects in the road infrastructures also. In order to analyze these defects and mark accident hotspots requires precise data. This paper proposes a novel system called RADAR (Road Accident Data Annotation and Reporting System) for extracting crucial data from images of accident scenes captured by a mobile camera, Unmanned Aerial Vehicle (UAV) and surveillance cameras to analyze the causes and hotspots of accidents. The system processes the captured image to extract important information such as the type of collision, weather conditions, the exact location of the accident (which is reverse-geocoded), and the classes and number of vehicles involved in the accident. This data can be used to provide accurate and timely information to emergency services and other stakeholders, and to improve road safety by identifying accident hotspots and implementing targeted interventions. The proposed system is based on deep learning techniques and utilizes state-of-the-art object detection algorithms to accurately identify and classify vehicles and other objects in the accident scene. Experimental results demonstrate the effectiveness and efficiency of the proposed system in accurately extracting important information from accident scenes.
Research on Target Detection Network Based on improved Swin-DETR

The Swin-DETR network model based on the improved DETR model is proposed to solve the problem that the resolution ratio of small targets is low in complex environment and it is not easy to be detected. Swin-T architecture is introduced into the backbone network of DETR model to improve the model’s deep extraction ability of parts of image features. Meanwhile, CBAM module is integrated into the backbone network. Through the fusion attention mechanism in CBAM module, the identification accuracy of small targets in the backbone network is further improved. Therefore, compared with the original DETR network, the identification accuracy and model accuracy of the small target in the target detection task are improved effectively. For the target detection task of multi-small targets in complex environment, through the comparison experiment with the current mainstream target detection model, it is proved that the algorithm can effectively improve the accuracy and recognition accuracy of the model for small targets.
Generated AI Face Detection using Xception Model

Generative Adversarial Networks (GANs) have emerged as a valuable deep learning technique, capable of generating lifelike artificial faces through learned data. However, the proliferation of GAN-generated content raises concerns about detecting artificially created faces. Therefore, it is important to detect artificially generated faces visually to prevent fraud, ensure security and privacy, maintain information credibility, and support law enforcement and public safety. To address this challenge, we present a system designed specifically to detect fake face images generated by AI. Our research contributes to the ongoing efforts in combating the challenges posed by GAN-generated fake content, particularly in the domain of face images. By developing robust detection systems, we can enhance our ability to identify and mitigate the potentially harmful consequences of deceptive or maliciously generated visual content. Our research leverages the Xception model, utilizing depthwise separable convolution as the underlying method for classification. We used a collection of 20.000 images comprising both generated faces from the StyleGAN dataset and real faces from CelebA-HQ. Our system’s performance was evaluated using a testing set and has resulted in an impressive accuracy of 98.77%. The generated class has 98% in precision and f1-score, and 97% in recall. For the real class, it has 97% in precision, 98% in recall and f1-score. This indicates the system is effective in accurately distinguishing between generated fake faces and authentic ones.
Folded Handwritten Digit Recognition Based on WGAN-GP Model

The study of overlapped handwritten digit recognition algorithms is critical for improving automated recognition accuracy, improving document processing, and automating recognition systems. The majority of current research in this field is focused on detecting two overlapped handwritten numbers. However, when one handwritten digit is folded onto another, recognition becomes more difficult, and there is currently no well-established recognition algorithm for this circumstance. To solve the issue of folded digit recognition, a method is provided that reconstructs the folded handwritten digit images using Generative Adversarial Networks (GANs) and transformation optimization algorithm, followed by recognition using a recognition network. The MNIST dataset is used to validate the suggested approach. The experimental findings demonstrate that the recognition accuracy reaches 97.22%, proving the suggested approach's considerable promise in solving the recognition of folded handwritten digit images.
Neural network retrieval of optical thickness of horizontally inhomogeneous cloudiness from nadir multispectral radiance data

The currently accepted algorithm for retrieving the optical parameters of clouds from satellite-based radiance measurement data implies the application of independent pixel approximation and a model of uniform cloudiness. This single-pixel approach cannot capture the 3D effects manifest themselves across multiple satellite pixels. At the same time, three-dimensional radiative transfer effects are the major source of retrieval errors in the passive remote sensing of clouds. The paper describes an alternative approach that extends the capabilities of traditional methods to obtain the estimates of cloud parameters. It is based on the building artificial neural network trained with the simulation data of solar radiative transfer in the cloudy atmosphere. The possibility of retrieving the optical thickness of broken horizontally inhomogeneous warm clouds is demonstrated. High values of the correlation coefficient between the benchmark and retrieved values of the optical depth were achieved at a fixed effective radius of cloud drops. The dependence of the correlation coefficient on the hyperparameters of the neural network, the volume and structure of the training dataset is studied.
Why adiabatic quantum annealing is unlikely to yield speed-up

Abstract We study quantum annealing for combinatorial optimization with Hamiltonian <?CDATA $H = H_0 + z H_f$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>H</mml:mi> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>+</mml:mo> <mml:mi>z</mml:mi> <mml:msub> <mml:mi>H</mml:mi> <mml:mi>f</mml:mi> </mml:msub> </mml:math> where H f is diagonal, <?CDATA $H_0 = -\vert \phi\rangle\langle \phi\vert$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mo>−</mml:mo> <mml:mo fence="false" stretchy="false">|</mml:mo> <mml:mi>ϕ</mml:mi> <mml:mo fence="false" stretchy="false">⟩</mml:mo> <mml:mo fence="false" stretchy="false">⟨</mml:mo> <mml:mi>ϕ</mml:mi> <mml:mo fence="false" stretchy="false">|</mml:mo> </mml:math> is the equal superposition state projector and z the annealing parameter. We analytically compute the minimal spectral gap, which is <?CDATA ${\cal{O}}\left({1/\sqrt{N}}\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mrow> <mml:mi class="MJX-tex-calligraphic">O</mml:mi> </mml:mrow> </mml:mrow> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mn>1</mml:mn> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:msqrt> <mml:mi>N</mml:mi> </mml:msqrt> </mml:mrow> </mml:mfenced> </mml:math> with N the total number of states, and its location <?CDATA $z_*$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>z</mml:mi> <mml:mo>∗</mml:mo> </mml:msub> </mml:math> . We show that quantum speed-up requires an annealing schedule which demands a precise knowledge of <?CDATA $z_*$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>z</mml:mi> <mml:mo>∗</mml:mo> </mml:msub> </mml:math> , which can be computed only if the density of states of the optimization problem is known. However, in general the density of states is intractable to compute, making quadratic speed-up unfeasible for any practical combinatorial optimization problems. We conjecture that it is likely that this negative result also applies for any other instance independent transverse Hamiltonians such as <?CDATA $H_0 = -\sum_{i = 1}^n \sigma_i^x$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>H</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mo>−</mml:mo> <mml:mrow> <mml:munderover> <mml:mo>∑</mml:mo> <mml:mrow> <mml:mi>i</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:mi>n</mml:mi> </mml:munderover> </mml:mrow> <mml:msubsup> <mml:mi>σ</mml:mi> <mml:mi>i</mml:mi> <mml:mi>x</mml:mi> </mml:msubsup> </mml:math> .
Management of Information Processes of the Economy in Conditions of Digitalization

The scientific and applied principles of digitization are analyzed in the article. The advantages and restraining factors of its development in the national economy conditions are highlighted. The legal support of digitalization is analyzed. Forms and methods of digitization are singled out. The article analyses the advantages of blockchain technology use. The global blockchain financing is analyzed and the prospects of its use are verified. The advantages and features of digital transformation of the financial sector are shown. The emphasis is placed on the need of the road maps use and technological incentives for the active use of digitization tools at the level of institutional sectors of the national a economy. The article proves that the creation of a single digital space of the European Union is an important political initiative aimed at increasing the competitiveness of the digital economy of Ukraine and the EU and promoting the creation of an integrated, inclusive European society. The creation of a digital space should be based on the Strategy for the Development of a Single Digital Space, which will combine the initiatives of EU member states, business representatives, and civil society at the supranational level. In order to integrate Ukraine into the Single Digital Space of the EU, it is necessary to reform the regulatory framework for the use of digital technologies by implementing mechanisms for the protection of intellectual property rights and improving the level of cyber security, introducing modern electronic services.
Multi-View Predicate Recognition for Solving Semantic Ambiguity Problem in Scene Graph Generation

Recent works on Scene Graph Generation (SGG) have been concentrating on solving the problem of long-tailed distribution. While these methods are making significant improvements on the tail predicate categories, they sacrifice the performance of the head ones severely. The major issue lies in the semantic ambiguity problem, which is the contradiction between the commonly used criterion and the nature of relationships in the SGG datasets. The models are evaluated with graph constraint, which allows merely one relationship between a pair of objects. However, the relationships are much more complex and can always be described from different views. For example, when a man is in front of a computer, we can also say he is watching it. Both options are plausible, describing the different aspects of the relationship. Which of them is determined to be the ground-truth is highly subjective. In this paper, we claim that the relationships should be considered from multiple views to avoid the semantic ambiguity. In other words, the model should provide all the possibilities, rather than being biased to any one of the options. To this end, we propose the Multi-View Predicate Recognition (MVPR), which separates the label set into multiple views and enables the model to represent and predict in a "multi-view'' style. Specifically, MVPR can be divided into three parts: Adaptive Bounding Box for Predicate is proposed to help the model attend to the crucial areas for the predicate categories in different views; Multi-View Predicate Feature Learning is designed to separate the feature space of different views of predicate categories; Multi-View Predicate Prediction and Multi-View Graph Constraint are used to allow the model to provide multi-view predictions to accurately estimate ambiguous relationships. Experimental results on the Visual Genome dataset show that our MVPR can significantly improve the model performance on the SGG task, and achieves a new state-of-the-art.
MNVLD: Violent Language Detection Model with Optimized Loss Function

Violent language on the Internet has become a serious problem on social platforms, posing a threat to users’ sense of security and a conducive communication environment. Most existing methods focus on creating violent language dictionaries and classifying texts using support vector machines (SVM) and naive Bayesian techniques. However, these methods face challenges when confronted with tens of thousands of text comments or commenters using obscure words. Additionally, the current network violence language detection technology performs poorly in multi-domain settings. This paper aims to enhance the MFND model in the context of rumor detection. We propose the MNVLD model, which incorporates an appropriate loss function based on the characteristics of the network violence language dataset, to effectively detect violent language across multiple domains.
Multimedia Cognition and Evaluation in Open Environments

Within the past decade, a plethora of emerging multimedia applications and services has catalyzed the production of an enormous quantity of multimedia data. This data-driven epoch has significantly propelled the trajectory of advanced research in various facets of multimedia, including image/video content analysis, multimedia search and recommendation systems, multimedia streaming, and multimedia content delivery among others. In parallel to this, the discipline of cognition, has embarked on a renewed trajectory of progression, largely attributing its remarkable success to the revolutionizing advent of machine learning methodologies. This concurrent evolution of the two domains invariably presents an intriguing question: What happens when multimedia meets cognition? To decipher this complex interplay, we delve into the concept of Multimedia Cognition, which encapsulates the mutual influence between multimedia and cognition. This exploration is primarily directed toward three crucial aspects. Firstly, the way multimedia and cognition influence each other, prompting theoretical developments towards multiple intelligence and cross-media intelligence. More important, cognition reciprocates this interaction by infusing novel perspectives and methodologies into multimedia research, which can promote the interpretability, generalization ability, and logical thinking of intelligent systems in open environments. Last but not least, these two aspects form a loop in which multimedia and cognition interactively enhance each other, bringing a new research problem, so that the proper evaluation for multimedia cognition in open environments is important. In this paper, we discuss what and how efforts have been done in the literature and share our insights on research directions that deserve further study to produce potentially profound impacts on multimedia cognition and evaluation in open environments.
First-order Multi-label Learning with Cross-modal Interactions for Multimodal Emotion Recognition

Multimodal emotion recognition (MER) is essential for the machine to fully understand human intentions. Various deep neural network based models are proposed but it is still challenging to better model and fuse multimodal features. In addition, recent studies have focused on the classification task of predicting discrete labels, while lacking consideration of the dimension value. In this paper, we propose a multimodal fusion model based on Transformer architecture and cross-modal interactions, and adopt a multi-label learning algorithm of first-order strategy to predict discrete labels and dimension values respectively. We also propose a semi-supervised learning method of moment injection with unlabeled data to enhance the robustness of the model. Finally, we use ensemble learning to further improve the performance of the model. We evaluate the proposed method on the MER-MULTI sub-challenge of Multimodal Emotion Recognition Challenge (MER 2023). Experimental results demonstrate the promising performance of our proposed method, which can achieve the evaluation metric of 0.6765 on the test set.
The Trend and Path of Data Governance in University Library Informatization Construction: Taking the library of Wuhan university of Technology as an example

With the rapid development of information construction, the application system of in colleges and university departments has accumulated a large amount of data. How to effectively use the existing data resources to make it to provide support for information construction, mining the data between the intrinsic value, will effectively help promote informatization construction. As an important document resource center and service guarantee department in colleges and universities, the library should follow the pace of information construction and fully promote the process of information construction. This paper analyzes the main problems in the process of university data governance, and taking the library of Wuhan University of Technology as an example, analyzes the structured, semi-structured and unstructured data accumulated in the daily management and service of university libraries and the service and application of relevant data resources, and studies and explores the trend and path of university library information construction data governance.
Research on the classification of winding machine faults based on the ETL model structure

In the detection and maintenance of winder, a large number of text maintenance records containing rich fault characteristics will be saved, but at present, the fault diagnosis of textile winder still mainly relies on manual investigation, failing to realize intelligent diagnosis, and the text maintenance records can not be fully utilized. Therefore, aiming at the problem of textile winder fault text classification, a fault classification method for textile winder named ERNIE-TEXTCNN-LightGBM (ETL) is proposed based on short text and sparse feature data. Through mask language model training of ERNIE model, The contextual semantic data and entity knowledge data are obtained respectively, and the semantic characteristics of the fault article are fully grasped. Then TEXTCNN model is used to extract local features from convolution layer and pooling layer, and learn the semantic association between adjacent words to overcome the loss of local features. Finally, the LightGBM model is integrated to reduce the parameters of the model, to realize the fault classification of the maintenance text, to assist the fault maintenance of the winder, and to improve the production efficiency of the factory. In order to verify the effectiveness of the algorithm, ETL is compared with other common text classification models, and the experiments show that the proposed method is effective and superior in realizing the maintenance text fault diagnosis of winder.
Phishing URL recognition based on ON-LSTM attention mechanism and XGBoost model

Aiming at the problems of losing sensitive words and low detection performance in phishing URL recognition, a phishing URL recognition method based on ON-LSTM attention mechanism and XGBoost model is proposed. Firstly, the URL is divided according to the word level of sensitive words, and the hierarchical features of URL are fully extracted through the unsupervised self-learning feature of the URL by the ON-LSTM mode. Then, the attention mechanism is used to assign weights to the feature vectors after feature extraction, so as to further improve the depth of URL feature extraction. Finally, the XGBoost classification model is constructed, the XGBoost model is trained by feature vectors to realize URL classification and recognition, which effectively improves the performance and efficiency of model classification. The experimental results show that compared with the comparison model, this method has better ability and performance to identify phishing URLs.
Taming Vector-Wise Quantization for Wide-Range Image Blending with Smooth Transition

Wide-range image blending is a novel image processing technique that merges two different images into a panorama with a transition region. Conventional image inpainting and outpainting methods have been used to complete this task, but always create significant distorted and blurry structures. The State-Of-The-Art (SOTA) method uses a U-Net-like model with a feature prediction module for content inference. However, it fails to generate panoramas with smooth transitions and visual realness, particularly when the input images have distinct scenery features. It indicates that the predicted features may deviate from the natural latent distribution of authentic images. In this paper, we propose an effective deep-learning model that integrates vector-wise quantization for feature prediction. This approach searches for the most-like latent features from a discrete codebook, resulting in high-quality wide-range image blending. In addition, we propose to use the global-local discriminator for adversarial training to improve the predicted content quality and smooth the transition. Our experiments demonstrate that our method generates visually appealing panoramic images and outperforms baseline approaches on the Scenery6000 dataset.
Nonword-to-Image Generation Considering Perceptual Association of Phonetically Similar Words

Text-to-Image (T2I) generation has long been a popular field of multimedia processing. Recent advances in large-scale vision and language pretraining have brought a number of models capable of very high-quality T2I generation. However, they are reported to generate unexpected images when users input words that have no definition within a language (nonwords), including coined words and pseudo-words. To make the behavior of T2I generation models against nonwords more intuitive, we propose a method that considers phonetic information of text inputs. The phonetic similarity is adopted so that the generated images from a nonword contain the concept of its phonetically similar words. This is based on the psycholinguistic finding that humans would also associate nonwords with their phonetically similar words when they perceive the sound. Our evaluations confirm a better agreement of the generated images of the proposed method with both phonetic relationships and human expectations than a conventional T2I generation model. The cross-lingual comparison of generated images for a nonword highlights the differences in language-specific nonword-imagery correspondences. These results provide insight into the usefulness of the proposed method in brand naming and language learning.