Reviewerly/exHarmony huggingface dataset:
Real time detection of Phishing attacks in edge devices using LSTM networks

Phishing is the problematic utilization of electronic trades to boggle and exploit clients. Phishing assaults attempt to get interesting, limited data, for example, usernames, passwords, Visa data, network attestations, and that is only a brief look at something bigger. By acting like a real individual or establishment through telephone or email, progressed aggressors utilize social intending to push misfortunes toward performing unequivocal activities—like tapping on a harmful affiliation or affiliation—or tirelessly uncovering advantaged data. Phishing is a smart and from this time forward threatening to pragmatic action, which has an enormous social and monetary assessments. Phones are notable with developers since they’re planned for quick responses reliant upon irrelevant important information. The standard target of this work is to cultivate a model that can recognize and thwart possible phishing attacks persistently. A brilliant phishing attack area instrument, when executed to handheld devices assembles the bandwidth of confirmation in the overall modernized town. Thwarting phishing itself assist with achieving sensible development. Different computations like Decision trees and SVM are used to distinguish phishing attacks. This kind of estimation needs enormous computational capacity to run and the customer data is transported off the specialist for the ID of phishing attacks. The standard deterrent is that our scrutinizing data is delivered off one more laborer for assessment. In the current situation a pariah moves toward our scrutinizing data which will incite an insurance issue and disclosure can be dependent upon various components like association information transmission etc. to beat the security issue we can use progressing gauge on the edges. For perceiving phishing attacks in low computational devices we can use a quantized model. This work centers around distinguishing phishing assaults progressively with the assistance of LSTM networks.
RoSA:A Mechatronically Synthesized Dataset for Rotodynamic System Anomaly Detection

The time-series datasets commonly applied for anomaly detection research showcase specific suboptimal properties. This work novelly conceptualizes condition state synthesis to improve the data-synthetic pipeline of an anomalous-event dataset. We demonstrate two technical contributions in this study. First, we propose a methodology to formulate, accelerate and enrich the condition state synthetic process. The proposed method includes three critical phases: analysis of a rotodynamic plant, systematic design of its condition state space, and development of a Markovian model for controlled state transitions. Second, a Rotodynamic System with Synthetic Anomaly dataset is constructed. It is a large-scale time-series dataset featuring controlled, abundant and diverse anomalous condition states, and per-time-step condition state labels. A comprehensive learning-based case study is conducted to illustrate that these unique features tangibly benefit anomaly detection research. Potential usages of the proposed dataset as an anomaly detection study benchmark are discussed.
A Novel Deterministic Threshold Proxy Re-Encryption Scheme From Lattices

Aiming at the problem that it is difficult to flexibly realize, the sharing and efficient search of encrypted data in large data-bases, this paper proposes a deterministic threshold proxy re-encryption scheme under the auxiliary input model. This scheme uses Shamir's secret sharing technology to achieve threshold control, uses homomorphic signature technology to verify the legitimacy of ciphertext, and applies deterministic algorithms to solve the search problem in large databases, while ensuring the user's control over their own data, and proves its security can reach indistinguishable semantic security (PRIV1-INDr) under the standard model. Compared with other schemes, this scheme not only shortens the length of the ciphertext and improves the decryption efficiency, but it also has anti-auxiliary input, robustness, and multi-hop characteristics and can better meet actual needs.
Shapley Additive Explanations for Text Classification and Sentiment Analysis of Internet Movie Database

The application of Artificial Intelligence (AI) is increasing in areas like sentiment analysis and natural language processing (NLP). Automatic sentiment analysis provides a guide to capture the user emotions and classify the reviews into positive or negative. One of the challenges of using general lexicon analysis is its insensitivity to all domains. There arises a need for the interpretability of the output predicted from the AI sentiment analysis models. This paper developed a Shapley Additive Explanations for Text Classification (SHAP) based model to classify the user opinion texts into negative or positive labels. Our sentiment analysis model is evaluated on the Internet Movie Database (IMDB) datasets which have rich vocabulary and coherence of the textual data. Results showed that the model predicted 89% of the user reviews correctly. This model is very flexible for extending it to the unlabeled data.
Deep Region Adaptive Denoising for Texture Enhancement

Image denoising is a highly challenging problem yet important task in image processing. Recently, many CNN-based denoising methods have made great performances but they commonly denoise blindly texture and non-texture regions together. This frequently leads to excessive texture smoothing and detail loss. To address this issue, we propose a novel region adaptive denoising network that adjusts the denoising strength according to region textureness. The proposed network conducts denoising tasks for texture and non-texture areas independently to improve the visual quality of the resulting image. To this end, we first generate a texture map that separates the image into texture and non-texture region. Because the difference between texture and non-texture is more evident in the frequency domain than in the spatial domain, the classification is performed through discrete cosine transform (DCT). Second, guided by the texture map, denoising is performed independently in two subnets, corresponding to the texture and non-texture regions. This allows the texture subnet to avoid excessive smoothing of high frequency details, and the non-texture subnet to maximize noise reduction in flat regions. Finally, a cross fusion that takes into intra- and inter-relationship between two resulting features is proposed. The cross fusion highlights the discriminant features from two subnets without degradation when combining the output of two subnets, and thus helps enhancing the performance of regions adaptive denoising. The superiority of the proposed method is validated on both synthetic and real-world images. We demonstrate that our method outperforms the existing methods in both objective scores and subjective image quality, in particular showing outstanding results in the restoration of visually sensitive textures. Furthermore, ablation study shows that our network can adaptively control the noise removal strength by manually manipulating the texture map and that the details of the texture region can be further improved. This also can simplify the cumbersome noise tuning process when deploying deep neural networks (DNN) architectures into products.
Truncated representation graph with adaptive weighted and manifold regularization for agglomerative clustering

Agglomerative clustering has emerged as a promising technique with its ability to generate the informative cluster hierarchies. The graph learning of data is very common in many agglomerative clustering algorithms. In this problem, the construction of the affinity graph with high quality is a vital element to improve the performance of clustering. To this end, we propose a new method named truncated representation graph with adaptive weighted and manifold regularization. On the one hand, an adaptive weighting matrix is embedded to the reconstruction error term to reinforce the effect of important features during graph learning. It encourages our method to obtain a robust representation graph of the data. On the other hand, the manifold regularization is introduced to capture the geometric structure of the data so that the representation coefficients of data are smooth with respect to the intrinsic data manifold. Moreover, we truncate the trivial representation coefficients to achieve a block-diagonality and more effective graph for agglomerative clustering. The experimental results on various benchmark datasets verify the superiority of our proposed method with respect to the state-of-the-art approaches.
Survey of Grammar-Based Data Structure Compression

A data string can be represented with the help of context-free grammar such that the string is the unique string belonging to the language of the grammar. One can then losslessly compress the string indirectly by encoding the grammar into a unique binary codeword. This approach to data compression, called grammar-based data compression, can also be employed to losslessly compress graphical data structures, which are graphs in which every vertex carries a data label. Under mild restrictions, grammar-based data compression schemes are universal compressors, meaning that they perform at least as well as any finite-state compression scheme. Some of the theory of universal grammar-based compressors is surveyed. Applications of grammar-based compressors to various areas, such as bioinformatics and data networks, are discussed. Future directions for grammar-based compression research are outlined, including compression issues arising in highly repetitive databases and issues concerning the compression of sparse graphical data.
Automatically Labeling Aging Scenarios with a Machine Learning Approach

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. Labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods.
TwinLSTM: Two-channel LSTM Network for Online Action Detection

Online Action Detection (OAD) has attracted more and more attention in recent years. A network for OAD generally consists of three parts: a frame-level feature extractor, a temporal modeling module, and an action classifier. Most recent OAD networks use a single-channel Recurrent Neural Network (RNN) to capture long-term history information, with spatial and temporal features concatenated as network input. In OAD, spatial features describe object appearance and scene configuration within each frame while temporal features capture motion cues over time. It is crucial to effectively fuse both spatial and temporal features. In this paper, we propose a new framework named TwinLSTM based on two-channel Long Short-Term Memory (LSTM) network for OAD, in which each channel is used to extract and handle either spatial features or temporal features. To more effectively fuse both spatial and temporal features, we design a prediction fusion module (PFM) to utilize hidden states of both channels to obtain more action content, including information interaction and future context prediction. We evaluate TwinLSTM on two challenging datasets: THUMOS14 and HDD. Experiments show that TwinLSTM outperforms existing single-channel models by a significant margin. We also show the effectiveness of PFM through comprehensive ablation studies.
Towards interpreting vulnerability of object detection models via adversarial distillation

Recent works have shown that deep learning models are highly vulnerable to adversarial examples, limiting the application of deep learning in security-critical systems. This paper aims to interpret the vulnerability of deep learning models to adversarial examples. We propose adversarial distillation to illustrate that adversarial examples are generalizable data features. Deep learning models are vulnerable to adversarial examples because models do not learn this data distribution. More specifically, we obtain adversarial features by introducing a generation and extraction mechanism. The generation mechanism generates adversarial examples, which mislead the source model trained on the original clean samples. The extraction term removes the original features and selects valid and generalizable adversarial features. Valuable adversarial features guide the model to learn the data distribution of adversarial examples and realize the model’s generalization on the adversarial dataset. Extensive experimental evaluations have proved the excellent generalization performance of the adversarial distillation model. Compared with the normally trained model, the mAP has increased by 2.17% on their respective test sets, while the mAP on the opponent’s test set is very low. The experimental results further prove that adversarial examples are also generalizable data features, which obey a different data distribution from the clean data.
A Detection method of electromagnetic analysis attacks based on change in amplitude of noise around integrated circuits

Electromagnetic analysis (EMA) attacks are a type of side-channel attacks that can be performed without opening or modifying a cryptographic module. In this study, we propose a detection method for the measurement of side-channel information, which is performed at the preliminary stage of the analysis of the target device as a countermeasure against EMA attacks performed near integrated circuits (ICs) with cryptographic modules. The presence of an electromagnetic (EM) field probe is detected by analyzing the sampling background noise around the IC using an analog-to-digital converter (ADC) inside the IC and by observing any changes in the EM environment. The proposed method can effectively detect a probe placed 5 cm from the IC package.
Financial Innovation and Financial Risks

This study examines research on the role of financial innovation in banking and its implications for a bank's financial soundness. A literature review and mathematical simulation was carried out in Excel, to examine and identify the relationships between financial performance and efficiency and stability. The simulation is divided into 4 parts: 1) the bank is not innovating, 2) the bank is innovating to reduce costs, 3) the bank is innovating to stimulate demand, 4) the bank is innovating to reduce costs and stimulate demand. The use of simulation allows you to obtain comparable data for any period. Analysis and simulation results show that innovation leads to different consequences. Cost cutting innovations help the bank to increase its equity capital faster and reduce risks. On the other hand, innovations that stimulate demand lead to an increase in income and a decrease in the share of equity capital. The study allows researchers to better assess the impact of modern innovations in the banking sector. As far as the authors know, this research format is one of the most promising areas of research in economic activity in general, and banking in particular.
Digital Humanism and a Quantitative Analysis of Users Behavior in Virtual Exhibitions during the Pandemic Context

The paper highlights the importance of promoting and valorizing cultural heritage collections from cultural institutions, such as art galleries, libraries, archives, and museums, using the most recent I&CTs (Information and Communication Technologies), as well as identifying behavior patterns of visitors inside virtual exhibitions in order to provide them personalized content. The paper's objective is to highlight the influence of modern I&CTs in the cultural field, and the advantages provided by new tools, such as virtual exhibitions, in accessing, promoting, and valorizing cultural collections, especially in pandemic situations. Digital transformation is happening not only in cultural institutions but also in human beings. The concept of Digital Humanism is very popular and developed together with the COVID-19 pandemic evolution.
SGX-Stream: A Secure Stream Analytics Framework In SGX-enabled Edge Cloud

This paper introduces SGX-Stream, a secure and efficient data analytics framework for data streams using Intel SGX. SGX-Stream employs sketch algorithms in a cloud–edge architecture. To ensure performance and security, SGX-Stream preprocesses the data at the edge of the network to generate sketches and send them to the cloud for further processing inside the SGX enclave. To prioritise urgent tasks, SGX-Stream develops a hybrid task-aware scheduler tailored for SGX to manage task execution securely and practically. SGX-Stream is implemented as a full-fledged framework within the enclave with a small TCB size of 6 kLoC. With an extensible interface, SGX-Stream facilitates the development of various applications, such as adversarial attack detection over data stream. Under different workloads, SGX-Stream can bring 14× speedup on urgent tasks with less than 800 KB of scheduling memory consumption. We also demonstrate SGX-Stream’s practicality with three real-world applications.
BigBraveBN: algorithm of structural learning for bayesian networks with a large number of nodes

Learning a Bayesian network is an NP-hard problem and with an increase in the number of nodes, classical algorithms for learning the structure of Bayesian networks become inefficient. In recent years, some methods and algorithms for learning Bayesian networks with a high number of nodes (more than 50) were developed. But these solutions have their disadvantages, for instance, they only operate one type of data (discrete or continuous) or their algorithm has been created to meet a specific nature of data (medical, social, etc.). The article presents a BigBraveBN algorithm for learning large Bayesian Networks with a high number of nodes (over 100). The algorithm utilizes the Brave coefficient that measures the mutual occurrence of instances in several groups. To form these groups, we use the method of nearest neighbours based on the Mutual information (MI) measure. In the experimental part of the article, we compare the performance of BigBraveBN to other existing solutions on multiple data sets both discrete and continuous. The experimental part also represents tests on real data. The aforementioned experimental results demonstrate the efficiency of the BigBraveBN algorithm in structure learning of Bayesian Networks.
Optimal Transport Guided Node Classification in Cross Networks

Inspired by the huge powerful representation ability of graph neural networks (GNNs), GNNs begin to solve node classification in cross networks. However, current GNNs are designed for single graph, and overlook the distribution shift in cross network which degrades the performance on test nodes. In this paper, we firstly analyze deep cross-network embeddings based on GNNs and illustrate that in cross-network node classification training and test embedding spaces have different distributions. According to domain adaptation and our analysis, we propose a novel framework and loss function, which introduces optimal transport to GNNs and generates transferable embeddings. Extensive experiments in citation networks verify the effectiveness of our method OT-DCNE.
Multi-classification assessment of personal credit risk based on stacking integration

In the multi-category assessment of personal credit risk, the prediction accuracy of a single classifier is often low, and each category cannot be well distinguished. In this paper, the stacking ensemble algorithm is used to integrate the base classifiers to build a multi-class evaluation model. Through the empirical analysis of the five-class data of an anonymous commercial bank in China, six types of base classifiers are selected to train the data, and the evaluation results of each single classifier is obtained. The four classifiers with the best classification effect are used as the first-layer base classifier in the stacking ensemble, and Random Forest is selected as the second-layer meta-learner. Compared with the evaluation results of the single-classifier model, the stacking ensemble model has improved in the four indicators of accuracy, precision, recall and F1-score, which verifies the efficiency and effectiveness of the model proposed in this paper.
Using GNN to detect financial fraud based on the related party transactions network

Financial fraud cases will greatly destroy the trust of market participants and affect the stability of the financial system. Existing research shows that the existence of related party transactions will increase the risk of financial fraud. Therefore, related party transactions may contain information that can help identify fraud. Existing studies have introduced simple statistics of related party transactions into fraud detection. However, with the increasing complexity of related party transaction networks, it is difficult for traditional methods to describe the whole picture of transactions, so the performance of fraud detection is limited. Therefore, this paper innovatively introduces a heterogeneous graph to connect listed companies and their related parties to form a complete picture of transactions, and then utilizes the graph neural network to make predictions. Experiments conducted on the data of Chinese listed companies in 2020 show that the introduction of related party transactions can improve the recognition effect. What's more, the related party transactions network can further improve the performance of fraud detection.
Context-driven pyramid registration network for estimating large topology-preserved deformation

Deep learning-based deformable image registration methods have become attractive alternatives to traditional methods because of their great performance and fast run time. However, it is still challenging for these methods to estimate large topology-preserved deformation, and the contextual information that is important for large deformation is also under-mined. To address these issues, we propose a novel unsupervised context-driven pyramid registration network for estimating large topology-preserved deformation named CPRNet. Specifically, based on the multi-resolution feature pyramids, we first design multi-receptive-field guidance modules, aiming at exploiting the multi-scale spatial correlation between features of two pyramids. Then we devise multi-view context fusion modules to dynamically fuse deep contextual information containing high-level semantic information from different views of feature maps. Further, we develop a residual estimation strategy to estimate the deformation in a coarse-to-fine manner. Moreover, a deformation field regularization module is proposed to address the challenge of balancing the registration performance and topology preservation. The experiments both on liver computed tomography (CT) images and brain magnetic resonance (MR) images demonstrate that our proposed method provides effective and accurate registration on various datasets with a fast run time. Compared with existing learning-based registration methods, our proposed method exceeds the performance in most trials while maintaining desirable topology preservation capability and can potentially fit various image registration tasks.