Reviewerly/exHarmony huggingface dataset:
Blockchain-Enabled Privacy-Preserving Access Control for Data Publishing and Sharing in the Internet of Medical Things

Recently, the rapid developments in the Internet of Medical Things (IoMT) enable smart devices to generate and transmit massive personal electronic medical records (EMRs). However, there are many sensitive attributes in an EMR, which could be accessed by external or internal unauthorized users for malicious purposes. In this article, we present a triple subject purpose-based access control (TS-PBAC) model, which is compatible with a blockchain-enabled reliable transaction network, and design an individual-centric security and privacy-preserving mechanism for access control with different purposes and roles in IoMT scenarios. Specifically, we design hierarchical purpose tree (HPT) and related policies to guarantee the legality of an external user with different purposes. To improve the privacy for sensitive attributes against an internal attacker, we design a local differential privacy (LDP)-based policy and role-based access control scheme in an edge computing paradigm to grant fine-granularity rights for authorized users. In addition, we introduce mutual evaluation metrics to evaluate data quality from a patient-and-medical-service level in an open anonymous network, only using logs kept in the blockchain. We test our approach by real-world EMRs with 100000 patients. The experimental results show that the proposed privacy-preserving scheme can better protect patient’s privacy than traditional access control policies in IoMT environments, and can make reliable and stable access control decisions between data publishers and data requesters with different purposes.
Unsupervised Anomaly Detection for Surface Defects With Dual-Siamese Network

Unsupervised anomaly detection in real industrial scenarios is challenging since the small amount of defect-free images contain limited discriminative information, and anomaly defects are unpredictable. Although nowadays image reconstruction-based methods are widely being used in various anomaly detection applications, they cannot effectively learn semantic representation, which leads to imperfect reconstruction. In this article, anomaly detection is formulated as a joint problem of feature reconstruction and inpainting in the dual-siamese framework. The proposed approach forces the network to model the feature distribution from the normal area and capture the semantic context for discriminating normal and abnormal areas. It first uses a Siamese architecture to capture discriminative features of defect-free samples and its corresponding defective samples generated by the defect random generation module. A dense feature fusion module is then employed to obtain the dense feature representation of dual input. The second Siamese network is proposed to reconstruct and inpaint the dual-dense features of the previous stage. Compared to the existing methods that mostly employ single image reconstruction, it is beneficial to simultaneously reconstruct and inpaint the information of dense discriminative features. The experimental results on the MVTec AD datasets and some major real industrial datasets demonstrate that our method achieves state-of-the-art inspection accuracy.
Generative adversarial learning for optimizing ontology alignment

Abstract The edge computing in knowledge‐defined network (KDN) is a kind of distributed computing architecture, and the format of edge resources stored in different edge computing nodes are different, which yields the data heterogeneity problem and hampers the interaction between edge nodes. Ontology is considered as the solution of data heterogeneity on Semantic Web, and matching ontologies is a high‐efficiency method of addressing the data heterogeneity problem. Ontology meta‐matching investigates how to determine the optimal weights to aggregate multiple similarity measures to achieve high‐quality ontology alignment, which is a challenge about nonlinear mathematical problem in ontology matching domain. To face this challenge, unsupervised learning method such as generative adversarial network (GAN) becomes an effective methodology. GAN consists of two models of different targets that are opposed to each other in training to produce the final best result. To improve the GAN's efficiency, this work further proposes a GAN with simulated annealing algorithm (SA‐GAN), where the stagnation counter is introduced to accelerate GAN's the convergence speed. The experiment uses the famous benchmark in the ontology domain, and the comparisons with the advanced ontology matching systems shows that SA‐GAN is able to find high‐quality alignments to help build bridges between edge nodes on edge computing.
The Secret Life of Software Vulnerabilities: A Large-Scale Empirical Study

Software vulnerabilities are weaknesses in source code that can be potentially exploited to cause loss or harm. While researchers have been devising a number of methods to deal with vulnerabilities, there is still a noticeable lack of knowledge on their software engineering life cycle, for example how vulnerabilities are introduced and removed by developers. This information can be exploited to design more effective methods for vulnerability prevention and detection, as well as to understand the granularity at which these methods should aim. To investigate the life cycle of known software vulnerabilities, we focus on how, when, and under which circumstances the contributions to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">introduction</i> of vulnerabilities in software projects are made, as well as how long, and how they are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">removed</i> . We consider 3,663 vulnerabilities with public patches from the National Vulnerability Database—pertaining to 1,096 open-source software projects on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GitHub</small> —and define an eight-step process involving both automated parts (e.g., using a procedure based on the SZZ algorithm to find the vulnerability-contributing commits) and manual analyses (e.g., how vulnerabilities were fixed). The investigated vulnerabilities can be classified in 144 categories, take on average at least 4 contributing commits before being introduced, and half of them remain unfixed for at least more than one year. Most of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contributions</i> are done by developers with high workload, often when doing maintenance activities, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">removed</i> mostly with the addition of new source code aiming at implementing further checks on inputs. We conclude by distilling practical implications on how vulnerability detectors should work to assist developers in timely identifying these issues.
Certificateless Aggregated Signcryption Scheme (CLASS) for Cloud-Fog Centric Industry 4.0

Over recent years, the Industrial Internet of Things and connectivity of the various sensors on the industrial and automaton front have played a crucial role in the manufacturing process. Production ventures are predominantly represented by Industry 4.0 so produce colossal information. Data outsourcing is one of the ways to manage the overhead of the massive data generated from the various resource-constrained devices utilized in the industrial environment. Therefore, the crowdsourced data from many organizations are outsourced to the cloud system. However, privacy and security challenges such as illegal admittance, data leakage are raised by the outsourced storage. Data authentication is an optimistic approach to establishing the integrity, confidentiality, and authenticity of the data. The certificateless signcryption scheme is most appropriate for lightweight devices established in the industrial ecosystem. In this article, we propose a privacy-conserving, lightweight data aggregation scheme to attain security in an industrial network. In the proposed model, the data owner collects the industrial data from various resource-constrained devices and sends this data to the data aggregator and proficiently data obtained by the industrial data user securely. Particularly, in this article, we propose a proficient certificateless aggregated signcryption scheme, which provides a data aggregation element in comparison to existing schemes. Our proposed scheme includes mutual authentication, public viability, integrity and confidentiality of data, volatile to key escrow, and privacy-preserving aspects for the industrial data. Performance evaluation and result analysis demonstrate that the proposed protocol performs better than other schemes significantly.
Cubature Kalman Filter Under Minimum Error Entropy With Fiducial Points for INS/GPS Integration

Traditional cubature Kalman filter (CKF) is a preferable tool for the inertial navigation system (INS)/global positioning system (GPS) integration under Gaussian noises. The CKF, however, may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances. To address this issue, a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points (MEEF-CKF) is proposed. The MEEF-CKF behaves a strong robustness against complex non-Gaussian noises by operating several major steps, i.e., regression model construction, robust state estimation and free parameters optimization. More concretely, a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step. The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points (MEEF) under the framework of the regression model. In the MEEF-CKF, a novel optimization approach is provided for the purpose of determining free parameters adaptively. In addition, the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic. The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex non-Gaussian noises.
A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are a class of Recurrent Neural Networks (RNN) suitable for sequential data processing.Bidirectional LSTM (BLSTM) enables a better understanding of context by learning the future time steps in a bidirectional manner.Moreover, GRU deploys reset and update gates in the hidden layer, which is computationally more efficient than a conventional LSTM.This paper proposes an efficient network model based on deep BLSTM-GRU for ciphertext classification aiming to mark the category to which the ciphertext belongs.The proposed model performance was evaluated using well-known evaluation metrics on two publicly available datasets encrypted with various classical cipher methods and performance was compared against onedimensional convolutional neural network (1D-CNN) and various other deep learning-based approaches.The experimental results showed that the BLSTM-GRU cell unit network model achieved a high classification accuracy of up to 95.8%.To the best of our knowledge, this is the first time an RNN-based model has been applied for the ciphertext classification.
Evaluating hybrid quantum-classical deep learning for cybersecurity botnet DGA detection

Hybrid quantum machine learning (QML) algorithms have potentials for current quantum computing technologies since only part of the model is computed by a quantum device. This paper proposes a novel hybrid quantum-classical deep learning model for cybersecurity application: domain generation algorithms (DGA)-based botnet detection. We analyzed our hybrid model’s performance compared with the classical model counterpart to investigate the quantum circuit’s effectivity as a layer in a deep learning model. We used four features of the Botnet DGA dataset: MinREBotnets, CharLength, TreeNewFeature, and nGramReputation_Alexa. The hybrid model’s quantum circuit is a combination of Pennylane’s embedding (Angle Embedding and IQP Embedding) and layers circuit (Basic Entangler Layers, Random Layers, and Strongly Entangling Layers). Also, we implemented noise models for assessing the applicability of the model for current Noisy Intermediate Scale Quantum (NISQ) technology. We found that in some cases, the hybrid model reached high performance (maximum accuracy up to 94,7% using n=100; 93,9% using n=1,000). We discovered that the combination of Angle Embedding and Strongly Entangled delivers high accuracy, superior to the classical deep learning model in n=100 experiments). However, the overall performance is still inferior for the rest cases compared to the classical deep learning model counterpart.
DSU-Net: Distraction-Sensitive U-Net for 3D lung tumor segmentation

Automatic segmentation of lung tumors is a crucial and challenging problem. Many existing methods suffer from ambiguity of tissue regions and tumor regions, which occur with similar appearance. To address this problem, we propose a new cascaded two-stage U-net model, Distraction-Sensitive U-Net (DSU-Net), to explicitly take the ambiguous region information (referred as distraction region) into account. Stage-I generates a global segmentation for the whole input CT volume and predicts latent distraction regions, which contain both false negative areas and false positive areas, against the segmentation ground truth. Stage-II embeds the distraction region information into local segmentation for volume patches to further discriminate the tumor regions. To this end, a Distraction Attention Module (DAM) is proposed and applied in each level of U-Net in Stage-II, to improve the discrimination of features. We evaluate our network on a lung cancer dataset from Gross Target Volume segmentation of MICCAI2019 challenge. Experimental results show that the proposed DSU-Net outperforms existing U-like networks.
Low-degree term first in ResNet, its variants and the whole neural network family

To explain the working mechanism of ResNet and its variants, this paper proposes a novel argument of shallow subnetwork first (SSF), essentially low-degree term first (LDTF), which also applies to the whole neural network family. A neural network with shortcut connections behaves as an ensemble of a number of subnetworks of differing depths. Among the subnetworks, the shallow subnetworks are trained firstly, having great effects on the performance of the neural network. The shallow subnetworks roughly correspond to low-degree polynomials, while the deep subnetworks are opposite. Based on Taylor expansion, SSF is consistent with LDTF. ResNet is in line with Taylor expansion: shallow subnetworks are trained firstly to keep low-degree terms, avoiding overfitting; deep subnetworks try to maintain high-degree terms, ensuring high description capacity. Experiments on ResNets and DenseNets show that shallow subnetworks are trained firstly and play important roles in the training of the networks. The experiments also reveal the reason why DenseNets outperform ResNets: The subnetworks playing vital roles in the training of the former are shallower than those in the training of the latter. Furthermore, LDTF can also be used to explain the working mechanism of other ResNet variants (SE-ResNets and SK-ResNets), and the common phenomena occurring in many neural networks.
Dynamic knowledge graph reasoning based on deep reinforcement learning

Knowledge graph reasoning is a task of reasoning new knowledge or conclusions based on existing knowledge. Recently, reinforcement learning has become a new technical tool for knowledge graph reasoning. However, most previous work focuses on the short fixed-step multi-hop reasoning or the single-step reasoning. In this paper, a dynamic knowledge graph reasoning framework is proposed based on deep reinforcement learning, which learns to navigate the graph to find the promising target answer conditioned on the input query. A novel reward function is constructed based on the proposed dynamic reasoning hypothesis, and the dynamic reward is proposed to obtain the dynamic reasoning model. The judgment condition is established to achieve dynamic reasoning, and an embedding model is chosen as the pre-trained model for initialization. The experimental analysis on several typical datasets shows that our model is indeed effective, and can improve over existing path-based knowledge graph reasoning models, and can be further applied to longer reasoning paths with good results.
A unified framework for multi-modal federated learning

Federated Learning (FL) is a machine learning setting that separates data and protects user privacy. Clients learn global models together without data interaction. However, due to the lack of high-quality labeled data collected from the real world, most of the existing FL methods still rely on single-modal data. In this paper, we consider a new problem of multimodal federated learning. Although multimodal data always benefits from the complementarity of different modalities, it is difficult to solve the multimodal FL problem with traditional FL methods due to the modality discrepancy. Therefore, we propose a unified framework to solve it. In our framework, we use the co-attention mechanism to fuse the complementary information of different modalities. Our enhanced FL algorithm can learn useful global features of different modalities to jointly train common models for all clients. In addition, we use a personalization method based on Model-Agnostic Meta-Learning(MAML) to adapt the final model for each client. Extensive experimental results on multimodal activity recognition tasks demonstrate the effectiveness of the proposed method.
Development of an AR-Based AI Education App for Non-Majors

The purpose of this study is to develop an AR-based app to teach non-engineering majors AI and analyze the educational effect.AIEduAR has developed a visual solution that displays the methods of teaching non-engineering majors how the machine learning on their own.Eighty-eight undergraduates who had not received any AI education were given lessons on AI learning principles and required to solve 10 problems using AIEduAR.Through AIEduAR, the students inputted data into augmented reality, set data attributes and learning rates, and entered new data to check out the accuracy of trained AI.Analysis of the technology acceptance model and Instructional Materials Motivation Survey questionnaire concluded that they are highly likely to accept and learn AR technology in AI education.Moreover, AIEduAR turned out to be effective in boosting their confidence in learning and instantaneous feedback.This study has successfully incorporated AR into AI education and developed learning steps required for AI education using AR.
40-user fully connected entanglement-based quantum key distribution network without trusted node

Abstract Quantum key distribution (QKD) would play an important role in future information technologies due to its theoretically proven security based on the laws of quantum mechanics. How to realize QKDs among multiple users in an effective and simple way is crucial for its real applications in communication networks. In this work, we propose and demonstrate a fully connected QKD network without trusted node for a large number of users. Using flexible wavelength division multiplexing/demultiplexing and space division multiplexing, entanglement resources generated by a broadband energy-time entangled quantum light source are distributed to 40 users. Any two users share a part of entanglement resources, by which QKD is established between them. As a result, it realizes a fully connected network with 40 users and 780 QKD links. The performance of this network architecture is also discussed theoretically, showing its potential on developing quantum communication networks with large user numbers owing to its simplicity, scalability, and high efficiency.
LogoDet-3K: A Large-scale Image Dataset for Logo Detection

Logo detection has been gaining considerable attention because of its wide range of applications in the multimedia field, such as copyright infringement detection, brand visibility monitoring, and product brand management on social media. In this article, we introduce LogoDet-3K, the largest logo detection dataset with full annotation, which has 3,000 logo categories, about 200,000 manually annotated logo objects, and 158,652 images. LogoDet-3K creates a more challenging benchmark for logo detection, for its higher comprehensive coverage and wider variety in both logo categories and annotated objects compared with existing datasets. We describe the collection and annotation process of our dataset and analyze its scale and diversity in comparison to other datasets for logo detection. We further propose a strong baseline method Logo-Yolo, which incorporates Focal loss and CIoU loss into the basic YOLOv3 framework for large-scale logo detection. It obtains about 4% improvement on the average performance compared with YOLOv3, and greater improvements compared with reported several deep detection models on LogoDet-3K. We perform extensive evaluation on three other existing datasets to further verify on both logo detection and retrieval tasks, and we demonstrate better generalization ability of LogoDet-3K on logo detection and retrieval tasks. The LogoDet-3K dataset is used to promote large-scale logo-related research. The code and LogoDet-3K can be found at https://github.com/Wangjing1551/LogoDet-3K-Dataset.
A knowledge infused context driven dialogue agent for disease diagnosis using hierarchical reinforcement learning

Disease diagnosis is an essential and critical step in any disease treatment process. Automatic disease diagnosis has gained immense popularity in recent years, owing to its effiacy, easy accessability and reliablity. The major challenges for the diagnosis agent are inevitably large action space (symptoms) and varieties of diseases, which demand either rich domain knowledge or an intelligent learning framework. We propose a novel knowledge-infused context-driven (KI-CD) hierarchical reinforcement learning (HRL) based diagnosis dialogue system, which leverages a bayesian learning-inspired symptom investigation module called potential candidate module (PCM) for aiding context-aware, knowledge grounded symptom investigation. The PCM module serves as a context and knowledge guiding companion for lower-level policies, leveraging current context and disease-symptom knowledge to identify candidate diseases and potential symptoms, and reinforcing the agent for conducting an intelligent and context guided symptom investigation with the information enriched state and an additional critic known as learner critic. The knowledge-guided symptom investigation extracts an adequate set of symptoms for disease identification, whereas the context-aware symptom investigation aspect substantially improves topic (symptom) transition and enhances user experiences. Furthermore, we also propose and incorporate a hierarchical disease classifier (HDC) with the model for alleviating symptom state sparsity issues, which has led to a significant improvement in disease classification accuracy. The proposed framework outperforms the current state-of-the-art method on the multiple benchmarked datasets and, in all evaluation metrics other than dialogue length (diagnosis success rate, average match rate, symptom identification rate, and disease classification accuracy by 7.1 %, 0.23 %, 19.67 % and 8.04 %, respectively), which firmly establishes the effiacy of the proposed bayesian learning-inspired context-driven symptom investigation and disease diagnosis methodology 1.