Reviewerly/exHarmony huggingface dataset:
The impact of training sessions on physical security awareness: Measuring employees’ knowledge, attitude and self-reported behaviour

Today, the need to deal with workplace security threats has become an important matter for many organisations. While companies have invested in technological and organization measures, the human processes behind these techniques are often ignored. However, employees' awareness of security procedures and policy of the organisation may strengthen or weaken the implemented security measures. This article assessed in an empirical way employees' security awareness and has a twofold aim. First, a study was conducted to examine the associations between three dimensions of employees' security awareness. By the use of a survey (n = 1,443; mean age = 42.5; 60.4% female, 30.4% male), the relationship between employees' knowledge about security procedures and policy, the attitudes towards security and the self-reported security behaviour is measured. Second, a case study was carried out to examine the impact of training sessions on employee's level of security awareness. By organizing an awareness training, its effect on employees' knowledge, attitude and behaviour was measured (n = 74; mean age = 51.7; 70.3% female, 29.7% male). While the first study found a significant relationship between employees' knowledge and attitude and their self-reported behaviour, the second study showed that the training session had a positive effect on employees' level of security awareness. Based on the findings from both studies, recommendations for practice and future research are presented.
Multitask Identity-Aware Image Steganography via Minimax Optimization

High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receiving end. To address this issue, we propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images. The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time. Thus, we introduce a simple content loss to preserve the identity information, and design a minimax optimization to deal with the contradictory aspects. We demonstrate that the robustness results can be transferred across different cover datasets. In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method, providing a multitask framework. The experiments under the multitask scenario show the effectiveness of our framework compared with other visual information hiding methods and state-of-the-art high-capacity image steganography methods. The code is available at https://github.com/jiabaocui/MIAIS.
Feature Extraction of Ancient Chinese Characters Based on Deep Convolution Neural Network and Big Data Analysis

In recent years, deep learning has made good progress and has been applied to face recognition, video monitoring, image processing, and other fields. In this big data background, deep convolution neural network has also received more and more attention. In order to extract the ancient Chinese characters effectively, the paper will discuss the structure model, pool process, and network training of deep convolution neural network and compare the algorithm with the traditional machine learning algorithm. The results show that the accuracy and recall rate of the Chinese characters in the plaque of Ming Dynasty can reach the peak, 81.38% and 81.31%, respectively. When the number of training samples increases to 50, the recognition rate of MFA is 99.72%, which is much higher than other algorithms. This shows that the algorithm based on deep convolution neural network and big data analysis has excellent performance and can effectively identify the Chinese characters under different dynasties, different sample sizes, and different interference factors, which can provide a powerful reference for the extraction of ancient Chinese characters.
Performance Characterization of Post-Quantum Digital Certificates

Public Key Infrastructure (PKI) generates and distributes digital certificates to provide the root of trust for securing digital networking systems. To continue securing digital networking in the quantum era, PKI should transition to use quantum-resistant cryptographic algorithms. The cryptography community is developing quantum-resistant primitives/algorithms, studying, and analyzing them for cryptanalysis and improvements. National Institute of Standards and Technology (NIST) selected finalist algorithms for the post-quantum digital signature cipher standardization, which are Dilithium, Falcon, and Rainbow. We study and analyze the feasibility and the processing performance of these algorithms in memory/size and time/speed when used for PKI, including the key generation from the PKI end entities (e.g., a HTTPS/TLS server), the signing, and the certificate generation by the certificate authority within the PKI. The transition to post-quantum from the classical ciphers incur changes in the parameters in the PKI, for example, Rainbow I significantly increases the certificate size by 163 times when compared with RSA 3072. Nevertheless, we learn that the current X.509 supports the NIST post-quantum digital signature ciphers and that the ciphers can be modularly adapted for PKI. According to our empirical implementations-based study, the post-quantum ciphers can increase the certificate verification time cost compared to the current classical cipher and therefore the verification overheads require careful considerations when using the post-quantum-cipher-based certificates.
User-Definable Dynamic Hand Gesture Recognition Based on Doppler Radar and Few-Shot Learning

In recent years, the radar-based dynamic hand gesture recognition (DHGR) system has been widely used in the non-contact interaction with smart electronic devices because of its advantages of safety, privacy security and robustness to different illumination environments. In order to achieve high-precision gesture recognition, Machine Learning algorithms, especially the deep learning algorithms are generally chosen by researchers. However, most deep learning models are trained based on large gesture dataset and only accept certain predefined specified gestures as input, i.e., user-defined gestures are not allowed. In this work, a neural network model trained with meta-learning method is proposed to deal with the few-shot classification task and realize the user-definable DHGR. Instead of learning a mapping between the unknown input gesture and the fixed predefined classes, the proposed model is trained to learn to compare the input gesture with arbitrary gestures entered by users, and then decide which class of gesture is the most probable one. Without retraining, the model can allow the user to enter the self-defined gestures for one or a few times and then recognize these gestures next time. To the best of our knowledge, this is the first work to apply the meta few-shot learning to the DHGR problem in the true sense. In this model, an embedding module is used in the network to extract the features of the input micro-Doppler spectrograms, and a comparison module is used to execute the feature-level comparison. Finally, a weighting module is proposed to weigh the comparison results and make the prediction. The evaluation result shows that with only one support sample for each class (i.e., for the one-shot tasks), the proposed model achieves an accuracy of 91% for 5 gesture classes and 90% for 10 gesture classes. When using 3 support samples (i.e., for the 3-shot tasks), the accuracies for 5 and 10 gestures are further improved to 96% and 92%, respectively.
Blockchain-Based Privacy Enforcement in the IoT Domain

The Internet of Things (IoT) pervades our lives every day and has given end users the opportunity of accessing personalized and advanced services based on the analysis of the sensed data. However, IoT services are also characterized by new challenges related to security and privacy because end users often share sensitive data with different consumers without precise knowledge of how they will be managed and used. To cope with these issues, we propose a blockchain-based privacy enforcement framework where users can define how their data can be used and check if their will is respected without relying on a centralized manager. The preliminary tests we performed, simulating different scenarios, show the feasibility of our approach.
Image classification in frequency domain with 2SReLU: A second harmonics superposition activation function

Deep Convolutional Neural Networks are able to identify complex patterns and perform tasks with super-human capabilities. However, besides the exceptional results, they are not completely understood and it is still impractical to hand-engineer similar solutions. In this work, an image classification Convolutional Neural Network and its building blocks are described from a frequency domain perspective. Some network layers have established counterparts in the frequency domain like the convolutional and pooling layers. We propose the 2SReLU layer, a novel non-linear activation function that preserves high frequency components in deep networks. A convolution-free network is presented, and it is demonstrated that in the frequency domain it is possible to achieve competitive results without using the computationally costly convolution operation. A source code implementation in PyTorch is provided at: https://gitlab.com/thomio/2srelu.
Task-Specific Loss for Robust Instance Segmentation With Noisy Class Labels

Deep learning methods have achieved significant progress in the presence of correctly annotated datasets in instance segmentation. However, object classes in large-scale datasets are sometimes ambiguous, which easily causes confusion. Besides, limited experience and knowledge of annotators can lead to mislabeled object semantic classes. To solve this issue, a novel method is proposed in this paper, which considers different roles of noisy class labels in different sub-tasks. Our method is based on two basic observations: firstly, the foreground-background annotation of a sample is correct even though its class label is noisy. Secondly, symmetric loss benefits the model robustness to noisy labels but harms the learning of hard samples, while cross entropy loss is the opposite. Based on the two basic observations, in the foreground-background sub-task, cross entropy loss is used to fully exploit correct gradient guidance. In the foreground-instance sub-task, symmetric loss is used to prevent incorrect gradient guidance provided by noisy class labels. Furthermore, we apply contrastive self-supervised loss to update features of all foreground, to compensate for insufficient guidance provided by partially correct labels especially in the highly noisy setting. Extensive experiments conducted with three popular datasets (i.e., Pascal VOC, Cityscapes and COCO) have demonstrated the effectiveness of our method in a wide range of noisy class label scenarios.
Enhancing the Robustness of Visual Object Tracking via Style Transfer

The performance and accuracy of computer vision systems are affected by noise in different forms. Although numerous solutions and algorithms have been presented for dealing with every type of noise, a comprehensive technique that can cover all the diverse noises and mitigate their damaging effects on the performance and precision of various systems is still missing. In this paper, we have focused on the stability and robustness of one computer vision branch (i.e., visual object tracking). We have demonstrated that, without imposing a heavy computational load on a model or changing its algorithms, the drop in the performance and accuracy of a system when it is exposed to an unseen noise-laden test dataset can be prevented by simply applying the style transfer technique on the train dataset and training the model with a combination of these and the original untrained data. To verify our proposed approach, it is applied on a generic object tracker by using regression networks. This method’s validity is confirmed by testing it on an exclusive benchmark comprising 50 image sequences, with each sequence containing 15 types of noise at five different intensity levels. The OPE curves obtained show a 40% increase in the robustness of the proposed object tracker against noise, compared to the other trackers considered.
The Cyber-MAR Project: First Results and Perspectives on the Use of Hybrid Cyber Ranges for Port Cyber Risk Assessment

With over 80% of goods transportation in volume carried by sea, ports are key infrastructures within the logistics value chain. To address the challenges of the globalized and competitive economy, ports are digitizing at a fast pace, evolving into smart ports. Consequently, the cyber-resilience of ports is essential to prevent possible disruptions to the economic supply chain. Over the last few years, there has been a significant increase in the number of disclosed cyber-attacks on ports. In this paper, we present the capabilities of a high-end hybrid cyber range for port cyber risks awareness and training. By describing a specific port use-case and the first results achieved, we draw perspectives for the use of cyber ranges for the training of port actors in cyber crisis management.
Weakly Supervised Few-Shot Segmentation via Meta-Learning

Semantic segmentation is a classic computer vision task with multiple applications, which includes medical and remote sensing image analysis. Despite recent advances with deep-based approaches, labeling samples (pixels) for training models is laborious and, in some cases, unfeasible. In this paper, we present two novel meta-learning methods, named WeaSeL and ProtoSeg, for the few-shot semantic segmentation task with sparse annotations. We conducted an extensive evaluation of the proposed methods in different applications (12 datasets) in medical imaging and agricultural remote sensing, which are very distinct fields of knowledge and usually subject to data scarcity. The results demonstrated the potential of our method, achieving suitable results for segmenting both coffee/orange crops and anatomical parts of the human body in comparison with full dense annotation.
Super Spreader Identification Using Geometric-Min Filter

Super spreader identification has a lot of applications in network management and security monitoring. It is a more difficult problem than heavy hitter identification because flow spread is harder to measure than flow size due to the requirement of duplicate removal. The prior work either incurs heavy memory overhead or requires heavy computations. This paper designs a new super-spreader monitor capable of identifying all flows whose spreads are greater than a user-specified threshold with a probability that can be arbitrarily set. It introduces a generalized geometric hash function, a generalized geometric counter, and a novel geometric-min filter that blocks out the vast majority of small/medium flows from being tracked, allowing us to focus on a small number of flows in which super spreaders are identified. We provide an analytical way of properly setting the system threshold to meet probabilistically guaranteed identification of super spreaders, and implement it on both hardware (FPGA) and software platforms. We perform extensive experiments based on real Internet traffic traces from CAIDA. The results show that with proper parameter settings, the new monitor can identify more than 99% super spreaders with a low memory requirement, better than the prior art.
SACBP: Belief Space Planning for Continuous-Time Dynamical Systems via Stochastic Sequential Action Control

We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends Sequential Action Control [] to stochastic belief dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.
An Asynchronous Quasi‐Cloud/Edge/Client Collaborative Federated Learning Mechanism for Fault Diagnosis

Although the federated learning method has the ability to balance data and protect data privacy by means of model aggregation, while the existing methods are difficult to achieve the effectiveness of centralized learning under data sharing. The existing federated structure only has a certain degree of confidentiality for data privacy, that is to say, each client can reconstruct a part of the information of other clients based on the model parameters shared between the server and the clients under certain conditions. In order to make the federated learning mechanism more confidential, we breaks the existing mechanism that the parameters between the federated model and the client model are completely shared, and establishes a new asynchronous quasi-cloud/edge/client collaborative federated learning mechanism. We construct a hierarchical multi-level confidential communication network, where the network parameters are shared in a way of quasi-cloud/edge/client coordination without data communication. The cloud and the edges respectively use the sequential Kalman filter algorithm to perform an asynchronous fusion of the network parameters uploaded in their respective fusion centers for the next round of updates; The effectiveness of the proposed algorithm is verified on the data of a type of rotating machinery
Hierarchical polynomial-based fuzzy neural networks driven with the aid of hybrid network architecture and ranking-based neuron selection strategies

In this study, we propose hierarchical polynomial-based fuzzy neural networks (HPFNN). The aim of this study is to develop the design methodologies of hierarchical model to improve the prediction accuracy of the model without sacrificing computational efficiency through combining hybrid network architecture composed of different neurons and ranking-based neuron selection strategies. The essential bullets of the proposed model can be enumerated as follows: (a) A hybrid network architecture is designed by combining the traits of fuzzy rule-based neurons with random vector functional link (FRN-RVFL) and polynomial neurons. FRN-RVFL is employed to erect the first layer of network. The entire network topology and the neurons of the remaining layers are constructed with polynomial neural network. (b) Two kinds of ranking-based neuron selection (RNS) strategies such as Linear-RNS (LRNS) and Exponential-RNS (EPNS) are presented. Compared with traditional neuron selection strategy, RNS can enrich the diversity of candidate neurons while maintaining the approximation ability of neurons, which provides opportunities for selecting neurons with predictive potential. (c) Regularization-based least square approach is applied to alleviate the possible overfitting in coefficient estimation as well as enhance generalization abilities of the model. The performance of HPFNN is verified by using a series of synthetic data and machine learning datasets. Based on the experimental results, HPFNN exhibits sound prediction accuracy and reasonable computational cost in contrast with the same type of hierarchical models. Furthermore, HPFNN achieves better generalization performance on at least 12 of 20 datasets when compared with the performance of state-of-the-art models.
Evaluation of a multi-sensor Leap Motion setup for biomechanical motion capture of the hand

The Leap Motion controller (LMC) offers a low-cost means of markerless hand tracking, however, its utility is limited by a small field of view and reliance on appropriate sensor positioning. A recent update from Leap Motion has enabled the use of a multiple LMC device on a single computer, allowing the tracking of hands from multiple orientations, potentially overcoming the aforementioned limitations. This study describes a method of implementing a multi-LMC setup and evaluates its effect on the validity and reliability of the derived kinematics. This study implemented a Kabsch algorithm and Kalman filter to re-orientate and fuse the trajectories captured by three LMC at different orientations. Reliability was assessed by comparing between-day differences in maximum joint angles (ΔMJA) and a calculated coefficient of multiple correlations (CMC). Validity was assessed by comparing the LMC to the gold standard, a Vicon markered motion capture (MMC) system, and calculating the ΔMJA and applying the linear fit method. The proposed method was evaluated by comparing the reliability and validity of the single-LMC setups to the multi-LMC setup. A multi-LMC setup proved successful in improving the reliability and validity of kinematic data, most notably where reliability and validity were poor and variation was high between the single-LMC setups. Findings suggest that through implementing the proposed method, limitations associated with single-LMC setups, notably its reliance on optimal sensor positioning, can be overcome.
Multi-Scale Graph Convolutional Network With Spectral Graph Wavelet Frame

Graph neural networks have achieved impressive progress in solving large and complex graph-structured problems. However, existing methods cannot sufficiently explore the advantages of multi-scale information by enlarging the receptive fields through stacking deep layers or multiplying exponentiated graph Laplacian. They are restricted by the discrete scales rigidly dependent on node-hops or suffer from the over-smoothing issue incurred by long-range low-pass filtering. In this paper, we propose a multi-scale graph convolutional network based on the spectral graph wavelet frame to improve multi-scale representation learning. The proposed network flexibly leverages multi-scale neighboring information with continuous scaling to enhance the discrimination ability of learned multi-scale representations and offer stable feature extraction ensured by frame bounds in theory. The multi-scale spectral convolutional layer is constructed with a low-pass filter and a sequence of dilated band-pass filters to achieve well-established localization in both vertex and frequency domains. The graph convolutional filters can be realized based on spectral graph wavelet kernel functions analytically formulated using parameterized band-limited filters or adaptively learned from training data. Furthermore, fast approximations of exact spectral filtering are developed for memory efficient operations on sparse tensors with guaranteed prediction accuracy and numerical stability. The proposed network is shown to achieve state-of-the-art performance in extensive experiments on citation networks, bioinformatics graphs, and social networks.