Reviewerly/exHarmony huggingface dataset:
Blockchain and its Applications NFT’s: Use of Blockchain Security in the NFT Marketplace

In the current time’s, Blockchain or most commonly known as blockchain technology which is a distributed software network and a secure network that allows the secure transfer of assets without any intermediate and has made a huge hype in the digital market or in the internet market place and is taking over like a breeze with its secure and easier transactions, mostly transactions done with the cryptocurrencies like bitcoin ,etherum, etc , with the help of these methods the transactions can be kept out of the books or the papers , where NFT(Non fungible token) is a unique token with its unique characteristics, it is a unique digital art with which is traded on the blockchain network and etherum network including the etherum environment as the front end with the help of this front end system they can trade within the network using etherum coins on platform such as open sea where digital assests are listed , auctioned and traded for the secure transaction of these NFT it uses blockchain network to do the transactions more efficiently. Throughout this research all the required information about the Blockchain and its applications, NFT’s: Use of blockchain Security in the NFT marketplace is listed out.
Twitter is the least of Musk's hurdles in India

Headline INDIA: Twitter is the least of Musk's hurdles
Channel and spatial attention mechanism for fashion image captioning

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span lang="EN-US"&gt;Image captioning aims to automatically generate one or more description sentences for a given input image. Most of the existing captioning methods use encoder-decoder model which mainly focus on recognizing and capturing the relationship between objects appearing in the input image. However, when generating captions for fashion images, it is important to not only describe the items and their relationships, but also mention attribute features of clothes (shape, texture, style, fabric, and more). In this study, one novel model is proposed for fashion image captioning task which can capture not only the items and their relationship, but also their attribute features. Two different attention mechanisms (spatial-attention and channel-wise attention) is incorporated to the traditional encoder-decoder model, which dynamically interprets the caption sentence in multi-layer feature map in addition to the depth dimension of the feature map. We evaluate our proposed architecture on Fashion-Gen using three different metrics (CIDEr, ROUGE-L, and BLEU-1), and achieve the scores of 89.7, 50.6 and 45.6, respectively. Based on experiments, our proposed method shows significant performance improvement for the task of fashion-image captioning, and outperforms other state-of-the-art image captioning methods.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Unsupervised Time Series Anomaly Detection Based on Adversarial Interpolation and Pseudo-anomaly Calibration

Existing work on unsupervised time series anomaly detection relies on clean training datasets (free of anomalies), which is often violated in practice. In this work, we propose an unsupervised time series anomaly detection method based on adversarial interpolation and pseudo-anomaly calibration for training an anomaly detector under data con-tamination. Specifically, the normality description learned from the model is improved by using the following two proposed calibration methods: (1) by enforcing adversarial interpolation to learn an effective normality description from the typical samples rather than the edge or abnormal samples, and (2) by discriminating the original samples from the simulated pseudo-anomaly samples to learn the exact normality description boundary. These two calibration ways result in the normality description that prevents data contamination. Experimental results compared with baselines on public time series datasets demonstrate the effectiveness of our proposed method.
Design of College Practice Information Management Platform based on Constraint Clustering Algorithm

The college practice information management platform studied in this paper is a complete WEB application mode. The system adopts a B/S architecture as a whole, and is compatible with current mainstream browsers. Users do not need to install additional software, but only need to open the browser and input the website configured in advance in the background to easily use this system. In the actual development of the system, the front-end is mainly developed using components such as the Angular framework, Material UI, Tabulator, and Marvin JS. The back-end is developed using Python language, and the back-end server uses Postgres database. In terms of system deployment, a hybrid cloud architecture is adopted, with public clouds deployed within the research and development company to provide public security services such as user permission control and user password verification; The private cloud is deployed inside the user's enterprise to store all local data generated by the user in the system, ensuring the security of the user's data.
Quantum Computers and Algorithms: A Threat to Classical Cryptographic Systems

Contemporary cryptographic algorithms are resistant to the strongest threats to cybersecurity and high-profile cyber-attacks. In recent times, information security scientists and researchers had developed various cryptographic schemes that defeated attacks using the most sophisticated (in terms of processor speed) classical computer. However, this resistance will soon erode with the arrival of quantum computers. In this paper, we profiled quantum computers and quantum algorithms based on their widely believed threat against currently secure cryptographic primitives. We found that Grover’s and Shor’s quantum-based algorithms actually pose a threat to the continued security of symmetric cryptosystems (e.g. 128-bit AES) and asymmetric (public key) cryptosystems (e.g. RSA, Elgamal, elliptic curve Diffie Hellman (ECDH), etc.) respectively.We discovered that the source of the algorithms’ cryptanalytic power against the current systems, stems from the fact that they (Grover and Shor) both equipped their respective algorithms with a quantum circuit component that can execute the oracle in parallel by applying a single circuit to all possible states of an n-qubit input. With this exponential level of processing characteristic of quantum computers and quantum-based algorithms, it is easy for the current cryptosystems to be broken since the algorithms can existentially solve the underlying mathematical problems such as integer factorization, discrete logarithm problem and elliptic curve problem, which formed the basis of the security of the affected cryptosystems. Based on this realization and as part of our readiness for a post quantum era, we explored other mathematical structures (lattices, hashes, codes, isogenies, high entropy-based symmetric key resistance, and multivariate quadratic problems) whose hardness could surpass the cryptanalytic nightmare posed by quantum computers and quantum-based algorithms. Our contribution is that, based on the findings of this research work, we can confidently assert that all hope is not lost for organizations heavily relying on protocols and applications like HTTPS, TLS, PGP, Bitcoin, etc., which derived their security from the endangered cryptosystems.
JPEG steganalysis based on steganographic feature enhancement and graph attention learning

The purpose of image steganalysis is to determine whether the carrier image contains hidden information or not. Since JPEG is the most commonly used image format over social networks, steganalysis in JPEG images is also the most urgently needed to be explored. However, in order to detect whether secret information is hidden within JPEG images, the majority of existing algorithms are designed in conjunction with the popular computer vision related networks without considering the key characteristics of image steganalysis. It is crucial that the steganographic signal, as an extremely weak signal, can be enhanced during its representation learning process. Motivated by this insight, we introduce a representation learning algorithm for JPEG steganalysis that is mainly consisting of a graph attention learning (GAL) module and a feature enhancement module. The GAL module is designed to avoid global feature loss caused by the local feature learning of convolutional neural network and reliance on depth stacking to extend the perceptual domain. The feature enhancement module is applied to prevent the stacking of convolutional layers from weakening the steganographic information. In addition, pretraining as a way to initialize the network weights with a large-scale dataset is utilized to enhance the ability of the network to extract discriminative features. We advocate pretraining with ALASKA2 for the model trained with BOSSBase + BOWS2. The experimental results indicate that the proposed algorithm outperforms previous arts in terms of detection accuracy, which has verified the superiority and applicability of the proposed work.
Detecting Fake-Normal Pornographic and Gambling Websites through one Multi-Attention HGNN

The rapid development of pornographic and gambling websites, fueled by the widespread abuse of information technology, has become a growing concern. They pose a serious threat to the physical and mental health of children and can also endanger personal property. Therefore, it is necessary to detect them. However, pornographic and gambling websites become more and more tricky, which shows fake-normal to evade censorship and challenges traditional content-based detection methods. Therefore, it is essential to rely on information about relationships between websites.We propose HMAN, one Multi-Attention Heterogeneous Graph Neural Network (HGNN) model to detect pornographic and gambling websites by integrating content features and structural information, even if they present fake-normal. By one multi-attention mechanism consisting of explicit weight, self-attention and attention mechanism, content features can be selectively utilized with the assistance of structural information. The experimental results show that our method achieves the best 95.1% Macro-Avg-F1 and outperforms all baselines. We also illustrate that all extracted metapaths do contribute to the detection, where the hyperlink, title/meta terms and IP address are relatively important.
Graph Convolutional Network with Long Time Memory for Skeleton-based Action Recognition

Skeleton-based action recognition task has been widely studied in recent years. Currently, the most popular researches use graph convolutional network (GCN) to solve this task by modeling human joints data as spatio-temporal graph. However, a large number of long-term temporal motion relationships cannot be effectively captured by GCN. Thus, recurrent neural network (RNN) is introduced to solve this defect. In this work, we propose a model namely graph convolutional network with long time memory (GCN-LTM). Specifically, there are two task streams in our proposed model: GCN stream and RNN stream, respectively. The GCN stream aims to capture the spatial motion relationships as well as the RNN stream focuses on extracting the long-term temporal patterns. In addition, we introduce the contrastive learning strategy to better facilitate feature learning between these two streams. The multiple ablation experiments have verified the feasibility of our proposed model. Numerous experiments show that the proposed model is superior to the current state-of-the-art method under two large-scale datasets including NTU-RGBD and NTU-RGBD-120.
A Computational Framework for Effective Representation and Extraction of Knowledge Graph for Power Plant Maintenance and Overhaul

The maintenance and overhaul of power plant equipment largely depends on valuable knowledge accumulated during previous projects. This knowledge is often implicit and difficult to capture. This paper aims to address this challenge by proposing a deep learning based framework for the automatic extraction of knowledge from power plant maintenance reports in the form of knowledge graph (KG). Unlike other work, this framework can support effective text classification and Named Entity Recognition (NER) tasks for fire power plants reports. Specifically, we develop a TextCNN-GRU method with multi-attention to classify power plant maintenance text effectively, and use the BERT-BiLSTM-CRF model for NER tasks. To evaluate the proposed framework, we conduct experiments on a dataset collected from a realworld power plant, and the results obtained show outstanding performance. This work hence opens up opportunity for supporting intelligent maintenance of lire power plants using deep learning based methods and KG.
Knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression

The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.
Black-box Word-level Textual Adversarial Attack Based On Discrete Harris Hawks Optimization

Neural network-based applications are prone to being fooled by adversarial examples due to the natural vulnerability of deep neural networks (DNNs). Textual adversarial attacks are particularly challenging due to the discreteness between texts. The adversarial examples crafted by word-level textual attacks which are typically treated as optimization problems in black-box scenarios perform better in human evaluation. Existing approaches have struggled to balance the success rate with the time consuming, mainly because the chosen optimization algorithm is not efficient enough. In this paper, we propose a method to generate textual adversarial examples called Discrete Harris Hawk Optimization (DHHO). We set up three operations for handling discrete data, which are applied to each stage of the Harris Hawk Optimization (HHO) to enable it to solve optimization problems in discrete space. By attacking BiLSTM and BERT on two benchmark data sets, we conduct extensive experiments to evaluate our attack method with a success rate of up to 98% and a reduction of time is at least 50%. Moreover, the experimental results also show that our adversarial examples can ensure high quality and transferability.
Matrix of Fused Features-based Approach for Tor Application Classification

Encrypted traffic identification is critical for detecting network attacks and improving the quality of service, making it an essential aspect of cyberspace security. However, existing methods for classifying Tor applications are limited by their reliance on single packet size or time-related features of traffic flows, which limits the potential for improving classification accuracy. To address this challenge, we propose an end-to-end classification framework called the Matrix of Fused Features Network (MFFN). This framework fuses direction, packet size, and time-related features of the raw flows to generate matrices that represent Tor applications. We then use the residual net-work, a popular deep learning model, to automatically extract potential features from these matrices and classify them into Tor applications. We evaluate our approach on the public dataset ISCXTor2016, and experimental results demonstrate that MFFN achieves 97% accuracy for Tor application classification, outperforming existing methods.
A Collaborative Learning Method based on Cross-domain Representation Correlation Congruence Distillation

In unsupervised domain adaptation (UDA), learning of domain invariant representation is easily dominated by labeled source domain data, while the data distribution characteristic of target domain is often ignored. Although there were some studies reported on extraction of the inter-class relation of samples and reduce the classifier mistakes in the target domain, however, they still failed to solve the problem on shared knowledge distillation when domains were cross-transferring. In this paper, we propose a two-stage maximizing cross-domain invariant features adaptation method, designing a collaborative learning model based on divergence congruence among source and target domain representation correlations. In the first stage, we use generative adversarial network to extract the common features from unpaired images, and the positive migration of classifiers is created between two domain spaces. Secondly, in the mixing domain label space, and correlation efficiency is obtained, upon which uncertainty weight mechanism is used to strengthen the features to stand out from cross-domain prediction and suppress the negative migration in the meantime. With cross-domain knowledge, few-shot learning in the target domain can further promote the training of adaptive information, enhancing a collaborative effect across two domains. In the UDA scenario, two baseline datasets Image CLEF-DA and Office-home are subjected to domain adaptation experiments. Compared with the state-of-the-art algorithms, the average recognition accuracy reaches up to nearly 2% increase.
Phishsifter: An Enhanced Phishing Pages Detection Method Based on the Relevance of Content and Domain

A Phishing website is used to steal users’ private information. The accelerated development of phishing kits has made it convenient to create such websites, which has become a persistent security threat. In this article, we propose a novel method to detect phishing webpages based on the relevance of the webpage content and domain. For phishing webpages whose domain is relevant to the content, we use the target identification method to identify the target brand. We use two components, the website logo and domain, to identify phishing sites, which increases the accuracy of identification. For irrelevant websites, we use a feature-based approach to distinguish phishing webpages. The experiment shows that the accuracy of target identification is 97.21%, while the false positive rate is 1.47%. The accuracy of the feature-based method is 98.32%. The proposed scheme can meet the needs of practical applications and provide an interpretation of the classification results.
BERT-based Question Answering using Knowledge Graph Embeddings in Nuclear Power Domain

In order to improve the resource utilization rate of existing nuclear power data and promote workers to efficiently obtain the operation information of nuclear power units and assist them in fault diagnosis and maintenance decision-making, this paper constructs a knowledge graph question answering (KGQA) dataset in the field of nuclear power. The BEm-KGQA model based on the pre-trained language model and knowledge graph embedding method was proposed. Our model learns the embedded representation of the knowledge graph through BERT and fine-tunes the BERT model. In the question embedding stage, it learns the embedded representation of the question based on the fine-tuned BERT model. Through experiments, we demonstrate the effectiveness of the method over other models. In addition, this paper implements a nuclear power question answering system. Based on the question answering system, employees can learn about unit information and efficiently obtain information on unusual operating events of nuclear power.
An Intelligent Human-Agent Interaction Support System in Medicine

Conversational robots have been widely used in the medical field to monitor, diagnose, and manage patient diseases. The construction of question and answer systems relies on domain knowledge, and according to research, there are currently no conversational bots available to answer postoperative thyroid eye disease questions. To alleviate healthcare resources, we have developed a multi-agent dialogue system for answering post-operative thyroid eye disease questions. The system consists of three agents: an intent understanding agent, a knowledge processing agent, and an intelligent interaction support agent, each responsible for intent recognition, knowledge base building, and conversation generation. The system is scalable and reusable with a division of labour between the agents working together to complete the dialogue tasks. In the experimental part, we used the dialogue system to simulate a post-operative thyroid eye surgery dialogue scenario, and the system was able to accurately recognise intent and answer the user’s questions well.
Multi-granularity Text Representation and Transformer-Based Fusion Method for Visual Question Answering

The Visual Questions and Answers (VQA) task is to obtain the answer to a question by combining the image information with the image related textual question information. On the one hand, the representation of problem features in traditional models is mostly the extraction of coarse-grained information, resulting in the inability to effectively utilize the deep semantics of text information. On the other hand, the traditional fusion of text and image features mostly adopts a series fusion method to simply splice the feature vectors of different modalities. This simple fusion method cannot deal with the problem of information redundancy and conflict between different modal features, which ultimately affects the accuracy of Visual Question Answering. To solve the above problems, this paper proposes a text feature representation module (MGT) for multi-granularity information. It mainly uses the method of hierarchical expansion convolution to preserve text information from multiple levels and multiple angles to improve the utilization of text features. This paper also proposes a Transformer-based multimodal fusion module (BTMF). It mainly uses the internal correlation between the picture and text modal features to dynamically adjust the weight of each modal feature. Simultaneously learn different modal information while keeping the contextual information unchanged. This paper verifies the effectiveness of the method on the VQA2.0 dataset.