Reviewerly/exHarmony huggingface dataset:
FPFlow: Detect and Prevent Browser Fingerprinting with Dynamic Taint Analysis

Abstract Browser fingerprinting is a practical user tracking technology widely adopted by many real-world websites to potentially track users’ browsing behaviors. By collecting information such as screen resolution, user agent, and WebGL rendered data, the tracker can generate a unique identifier for users without their knowledge, leading to a severe violation of user privacy. Therefore, an effective detection and defense technology for browser fingerprinting is needed to protect user privacy. In this paper, we proposed FPFlow, a dynamic JavaScript taint analysis framework to detect and prevent browser fingerprinting. FPFlow monitors the whole process of browser fingerprinting, including collecting information, generating fingerprinting, and sending it to the remote server. We evaluated FPFlow on TRANCO top 10,000 websites. Our experiments showed that our framework could effectively detect browser fingerprints. We found 66.6% of the websites performing fingerprinting and revealed how browser fingerprinting is applied in real-world websites. We also showed that FPFlow could prevent browser fingerprinting with an acceptable overhead.
Implementation of Blockchain Enabled Healthcare System using Hyperledger Fabric

The healthcare industry is one of the top priorities for every country, as the government wants to improve the process and provide better services to the public in the health sector. In COVID19, there is a need to immediate response and secured way of storing data due to overwhelming patients admitting to the hospitals. Many stakeholders are involved in the health sector, such as patients, doctors, pharmaceutical companies, laboratories, and hospitals which makes the complete system complex. Security and privacy are the major concern of comprehensive patient-related information accessed by the involved participants. Blockchain technology is capable of ensuring data integrity and privacy with immutable features. The blockchain technology can be implemented in public and private means, but the medical data is sensitive information which cannot be shared to the public. In this paper, the permissioned blockchain is implemented for healthcare sector using Hyperledger Fabric. The Fabric is an open-source platform through which a Smart Contract is developed on the blockchain network. With the help of a smart contract, the secure transaction is performed on the ledger to store the confidential data related to a patient. The data stored in the form of an immutable block ensures the security of health records stored on distributed ledger through a smart contract. With this implementation, the efficient healthcare process is simplified and the transactions with multiple end-users are done securely, leading to transparency and traceability.
Multi-atlas Segmentation of the Aorta from 4D Flow MRI: Comparison of Several Fusion Strategies

4D flow Magnetic Resonance Imaging (4D flow MRI) provides information that improves the estimation of hemodynamic characteristics of the aorta and allows further flow analysis. However, reliable segmentation of the aorta, required as a preliminary step, is still an open problem due to the low image quality. Thus, an automatic segmentation tool could encourage the use of 4D flow MRI in clinical practice for diagnostic and prognostic decision-making. In this paper, we propose a fully automatic multi-atlas-based method to segment the aorta using the systolic phase of 4D flow MRI. The Dice similarity coefficient and Hausdorff distance were used to quantify the performance. In addition, a statistical significance test between the maximum diameters obtained with the manual and automatic segmentations was conducted to determine the reliability of the automatic segmentations. The results show that our method could be a first reliable step towards automatic segmentation of the aorta in all phases of 4D flow MRI.
Visualization of the Results of the Analysis of Programming Languages for Their Superficial Comparison

The article is devoted to the choice of a clear and concise form for presenting the results of analise and comparing programming languages and systems, convenient for assessing the expressive power of languages and the complexity of implementing systems. The formalization is adapted to the paradigm analysis of the definitions of programming languages and the selection of practical criteria for decomposition of programs. The semantic decomposition of the definitions of languages and systems as part of the analysis of programming paradigms was chosen as the main ap-proach. Such a choice makes it possible to single out autonomously developed typical program components that can be adapted to the design of various information systems. Many works on methods for developing software systems depend on the practicality of approaches to decomposition of programs debugged using programming systems. The solution to this problem is useful when studying programming methods, studying the history of programming languages, for comparing programming paradigms, the potential of used circuits and models, assessing the novelty level of created programming languages, and also when choosing criteria for program decomposition. In addition, their existence allows us to form a teaching methodology for developing the components of information systems. Along the way, the distance in conceptual complexity between programming and programming system development is shown.
Small Sample Electromagnetic Signal Recognition based on Time Series Data Augmentation

With the rapid development of science, technology and industry, the electromagnetic environment becomes more and more complex. The acquisition of information first needs to identify the signal. However, the number of electromagnetic transmitting equipment is large, and the signal is unstable, which easily leads to a small number of available signal samples. As a result, over fitting is easy to occur in the training process, which reduces the accuracy of recognition. In this paper, the complex network model is used for the electromagnetic signal recognition. Aiming at the problem of less dataset, the time series data augmentation method is used for the processing of this dataset, so as to enhance the accuracy of electromagnetic signal recognition. According to the characteristics of time series, four methods are used to augment the data, including time domain flipping, amplitude inversion, amplitude scaling and noise disturbance. The experimental results display that the four augmentation methods can enhance the recognition accuracy, and the mixed form of different augmentation methods can better enhance the effect.
Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space

In this paper, we present refining graph representation for cross-domain recommendation (CDR) based on edge pruning considering feature distribution in a latent space. Conventional graph-based CDR methods have utilized all ratings and purchase histories of user’s products. However, some items purchased by users are not related to the domain for recommendation, and this information becomes noise when making CDR. So, the proposed method introduces edge pruning into the latest graph-based CDR method to refine graph representation. To compare the item embedding features calculated in different domains, we construct a latent space and perform edge pruning through their correlations. Additionally, we introduce a state-of-the-art graph neural network into the graph construction of the proposed method that considers the interactions between users and items thereby obtaining effective embedding features in a domain. This makes it possible to consider domain-specific user preferences and estimate embedding features with high-expressive power. Furthermore, to compare the embedding features of items in the two domains, we construct their latent spaces and project them. Edge pruning is performed using the correlation of items between the two domains on the latent space. We obtain cross domain specific graph representation through edge pruning, which improves the performance by considering the relationship between both items across domains. To the best of our knowledge, no study in the CDR field focuses on eliminating unnecessary node information. We have demonstrated the effectiveness of the proposed method by comparing several graph-based state-of-the-art methods.
Research and Application of Agriculture Knowledge Graph

With the evolution of knowledge graphs, more and more domain knowledge graphs appear in people's field of vision. Knowledge graphs can meet the needs of rapid, accurate and intelligent information query. This article first explains the basic concepts, methods and techniques of knowledge graphs and domain knowledge graphs. Secondly, it introduces the status quo of research and application of knowledge graph in the field of agriculture at home and abroad. Then, for the construction and application of the domestic agricultural domain knowledge graph, a systematic solution and approach are proposed. Finally, it summarizes the main problems and challenges facing the construction of knowledge graphs in the agricultural field in China. The development prospects of agricultural knowledge graphs are broad. In the next ten years or even longer, intelligent applications based on knowledge graphs are important for smart agriculture. Direction of development.
Analysis of CNL-UNet for Efficient Biomedical Image Segmentation

Biomedical image segmentation has a lot of significance in disease detection and diagnosis. Many researchers proposed many deep learning architectures such as U-Net, SegNet, DualChexNet, DualANet for biomedical image segmentation. Recently, a novel lightweight architecture namely CNL-UNet is proposed for multimodal biomedical image segmentation. The CNL-UNet used the CNL module, res-path, and transfer learning for effective biomedical image segmentation. In this paper, we made an in-depth analysis of the CNL-UNet to prove its robustness on multimodal biomedical image segmentation. We experimented in detail with the various components of the CNL-UNet. We made eight combinations of the CNL-UNet by removing or adding these components and experimented with these eight combinations on the ultrasound and MRI datasets. The results show that the CNL module helps the model to reduce the false outputs and gain high precision, recall, and F1 score. The res-path has a remarkable contribution to provide precise segmentation and increasing the performance of the model. And also, transfer learning plays an important role in faster convergence and also in increasing the performance of the network. In essence, this experiment increases the confidence of the CNL-UNet regarding its remarkable performance on multimodal biomedical image segmentation.
Continuous Authentication Based on Keystroke and Mouse Dynamics in Video Private Network

As a typical internal network, the video private network carries various video image resources and involves a large amount of citizen privacy. To prevent potential internal security threats, it is necessary to carry out continuous identity authentication for operators in the video private network. In this paper, a continuous authentication system based on keystroke and mouse dynamics is proposed. The keystroke and mouse operations of users are collected to extract their operating behavior characteristics. An artificial neural network model for each user is constructed for identity authentication. To achieve continuous identity authentication, a novel trust model is present to evaluate users’ identities dynamically. The experimental results show that the proposed method can identify impostors after an average of 115 actions and classify genuine users as impostors only after an average of more than 1,000 actions.
Revisiting Dead Leaves Model: Training With Synthetic Data

Deep neural networks targeting stereo disparity estimation have recently surpassed the performance of hand-crafted traditional models. However, training these networks require large labeled databases for obtaining accurate disparity estimates. In this letter, we address the large data requirement by generating synthetic data using natural image statistics. Images generated using dead leaves model have been shown to share many statistical characteristics commonly seen in natural images. In this work, we created a synthetic dataset using the 3D dead leaves model consisting of occluding spheres, and projected them onto parallel camera planes to obtain stereo image pairs along with ground-truth disparity map. This generated data was subsequently used to train a deep neural network in a supervised manner to estimate disparity. Through experiments we show that this trained model achieves competitive performance across real-world and synthetic stereo datasets, even without any additional fine-tuning. The proposed method for dataset generation is simplistic in nature, computationally inexpensive and can be easily scaled for large scale data generation.
Research on Malicious URL Identification and Analysis for Network Security

With the rapid development of the Internet, the emergence of various malicious URLs seriously endangers the national network information security and user information security. Therefore, it is of great theoretical significance and practical value for network security to accurately identify and deal with malicious URLs. This paper proposes a research method of character level feature extraction and recognition of malicious URLs based on CNN + BiLSTM + CNN model. Based on the massive URL data sets, the parameter distribution characteristics of malicious URLs are analyzed, and the skip gram model is introduced to unsupervised train the preprocessed data sets, so as to embed the characters of URLs. Then the CNN + BiLSTM + CNN model is introduced to extract and optimize the local and temporal features of malicious URLs. The experimental results show that on the same data set, the malicious URL recognition method based on CNN + BiLSTM + CNN model has better recognition effect and higher accuracy than the traditional BiLSTM based algorithm and CNN based algorithm. The F1 value is increased to 98.14%, and the average iteration time is greatly reduced. It shows that the research method proposed in this paper has good applicability in the field of malicious URL identification for network security.
Interpolation based Single-path Sub-pixel Convolution for Super-Resolution Multi-Scale Networks

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR).Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model.Algorithms for single model multiscale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor.Recent SR methods employ multi-scale and multi-path learning as a solution.However, this causes unshared parameters and unbalanced parameter distribution across various scale factors.We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms.The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size.Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module.Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.
Parallelized Context Modeling for Faster Image Coding

Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate the symbol probabilities in that space with a context model. During inference, the learned context model provides symbol probabilities, which are used by the entropy encoder to obtain the bitstream. Currently, the most effective context models use autoregression, but autoregression results in a very high decoding complexity due to the serialized data processing. In this work, we propose a method to parallelize the autoregressive process used for image compression. In our experiments, we achieve a decoding speed that is over 8 times faster than the standard autoregressive context model almost without compression performance reduction.
Gender face Recognition Using Advanced Convolutional Neural Network Model

Gender face recognition has several useful applications in human Android reactions, in which the overall user experience can improved. The convolutional neural network model has made excellent achievements in this field. This paper proposed an advanced convolutional neural network model named "A-CNN" to recognize gender. The advantage of the model is training big data without technical challenges and achieved excellent overall performance. The model train used 5000 images, which consisted of gender face male and female. The results have shown that the proposed model gives excellent achievement during the training stage with an accuracy of 97% and loss validation 0.1. The result has demonstrated that the proposed model can facilitate the automatic classification of human gender.
The Role of Artificial Intelligence in Entrepreneurship

The study discusses the role of entrepreneurship and artificial intelligence (AI) in creating new products and their contribution to reshaping the future of economic growth, and identify also the administrative aspect to support and spread the culture of entrepreneurship, which contributed to the launch of a number of innovative projects, which led to the emergence of a number of innovations and the emergence of other projects that serve the orientation of countries to achieve economic development, and the results showed that both artificial intelligence and entrepreneurship have become a pillar of change that works to make a change. An ambition in the traditional and progressive business scene.
Hybrid Recurrent Deep Learning Model for DeepFake Video Detection

Nowadays deepfake videos are concern with social ethics, privacy and security. Deepfake videos are synthetically generated videos that are generated by modifying the facial features and audio features to impose one person’s facial data and audio to other videos. These videos can be used for defaming and fraud. So, counter these types of manipulations and threats, detection of deepfake video is needed. This paper proposes multilayer hybrid recurrent deep learning models for deepfake video detection. Proposed models exploit the noise-based temporal facial convolutional features and temporal learning of hybrid recurrent deep learning models. Experiment results of these models demonstrate its performance over stacked recurrent deep learning models.
How to Justify Free Speech

This chapter is concerned with why and how free speech should be justified. It critically analyzes four major justifications for free speech and argues for a conception of free speech that combines two justifications- equal autonomy and democratic participation- on a principled ground as a strong foundation for free speech. First, I claim that the discovery of truth justification for free speech relies on uncertain empirical claims that assume free speech leads to truth and social progress and is far from providing a strong defense of speech as intrinsically valuable. Second, I contend, like the discovery of truth argument, the personal development argument relies on uncertain empirical validation and falls short of demonstrating that free speech is an intrinsic value but rather assigns significance to free speech as a means for the well-being of persons through personal fulfillment. Third, I point out that the equal autonomy argument is ambiguous on the status of different categories of speech since it applies to a whole universe of speech and the democratic participation argument is vague on the status of undemocratic speech in terms of whether undemocratic speech should be counted as political speech. I contend that the proposed framework of this book, namely the double-grounded principled approach, provides a stronger justification for free speech when compared with the single grounded justifications, by combining and integrating the relevant aspects of the equal autonomy and democratic participation arguments in its scope. Unlike the equal autonomy argument alone, it endows political speech the highest protection and moreover, it is clearer on the status of undemocratic speech by providing a space for it within the realm of free speech as long as it contributes to public deliberation and does not constitute hate speech.
Structured Verification of Machine Learning Models in Industrial Settings

The use of machine learning (ML) allows us to automate and scale the decision-making processes. The key to this automation is the development of ML models that generalize training data toward unseen data. Such models can become extremely versatile and powerful, which makes democratization of artificial intelligence (AI) possible, that is, providing ML to non-ML experts such as software engineers or domain experts. Typically, automated ML (AutoML) is being referred to as a key step toward it. However, from our perspective, we believe that democratization of the verification process of ML systems is a larger and even more crucial challenge to achieve the democratization of AI. Currently, the process of ensuring that an ML model works as intended is unstructured. It is largely based on experience and domain knowledge that cannot be automated. The current approaches such as cross-validation or explainable AI are not enough to overcome the real challenges and are discussed extensively in this article. Arguing toward structured verification approaches, we discuss a set of guidelines to verify models, code, and data in each step of the ML lifecycle. These guidelines can help to reliably measure and select an optimal solution, besides minimizing the risk of bugs and undesired behavior in edge-cases.
Adaptive Cross-stitch Graph Convolutional Networks

Graph convolutional networks (GCN) have been widely used in processing graphs and networks data. However, some recent research experiments show that the existing graph convolutional networks have isseus when integrating node features and topology structure. In order to remedy the weakness, we propose a new GCN architecture. Firstly, the proposed architecture introduces the cross-stitch networks into GCN with improved cross-stitch units. Cross-stitch networks spread information/knowledge between node features and topology structure, and obtains consistent learned representation by integrating information of node features and topology structure at the same time. Therefore, the proposed model can capture various channel information in all images through multiple channels. Secondly, an attention mechanism is to further extract the most relevant information between channel embeddings. Experiments on six benchmark datasets shows that our method outperforms all comparison methods on different evaluation indicators.