Reviewerly/exHarmony huggingface dataset:
Cybersecurity Training in Norwegian Critical Infrastructure Companies

Human preparedness is a critical aspect of critical infrastructure (CI) cybersecurity. Many efforts, including educational curricula and training programs, have been taken at both national and company level to ensure human preparedness in CI incident response. These efforts are usually based on corporate requirements or external guidelines and policies. However, the best practices recommended for these efforts in the literature differ significantly from the measures implemented in CI companies. For this reason, we compared state of practice in cybersecurity awareness and training in selected CI companies with the recommendations in literature, aiming to identify the areas that CI companies need to increase efforts for further security implementations. Specifically, we conducted interviews (n=7) and sent out questionnaires to cybersecurity personnel (n=11) in different CI sectors of Norway. The collected data were analyzed to establish the commonalities, differences, and areas of concern among the interviewees, with respect to certain critical attributes. All Norwegian companies involved in the study offered some type of awareness or training activities to their employees, but these activities varied greatly in the level of maturity. Besides, we noted several limitations in methods and contents. According to many participants, the team skills, communication skills, and managerial skills were often inadequately developed. Additional limitations in delivery methods were noticed, too. Finally, we suggested the solutions from the best practices in the literature, and pointed out the areas where the literature has not provided effective measures.
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction.Anecdotally, they have been observed to fail to maintain character identity throughout discourse; and more specifically, may take on the role of their interlocutor.In this work we formalize and quantify this deficiency, and show experimentally through human evaluations that this is indeed a problem.In contrast, we show that discriminative models trained specifically to recognize who is speaking can perform well; and further, these can be used as automated metrics.Finally, we evaluate a wide variety of mitigation methods, including changes to model architecture, training protocol, and decoding strategy.Our best models reduce mistaken identity issues by nearly 65% according to human annotators, while simultaneously improving engagingness.Despite these results, we find that maintaining character identity still remains a challenging problem.
Generative Cross-Domain Data Augmentation for Aspect and Opinion Co-Extraction

As a fundamental task in opinion mining, aspect and opinion co-extraction aims to identify the aspect terms and opinion terms in reviews.However, due to the lack of fine-grained annotated resources, it is hard to train a robust model for many domains.To alleviate this issue, unsupervised domain adaptation is proposed to transfer knowledge from a labeled source domain to an unlabeled target domain.In this paper, we propose a new Generative Cross-Domain Data Augmentation framework for unsupervised domain adaptation.The proposed framework is aimed to generate targetdomain data with fine-grained annotation by exploiting the labeled data in the source domain.Specifically, we remove the domain-specific segments in a source-domain labeled sentence, and then use this as input to a pre-trained sequence-to-sequence model BART to simultaneously generate a target-domain sentence and predict the corresponding label for each word.Experimental results on three datasets demonstrate that our approach is more effective than previous domain adaptation methods.
Device Identification in Blockchain-Based Internet of Things

In recent years, blockchain technology has received tremendous attention. Blockchain users are known by a changeable public key (PK) that introduces a level of anonymity; however, studies have shown that anonymized transactions can be linked to deanonymize the users. Most of the existing studies on user deanonymization focus on monetary applications; however, the blockchain has received extensive attention in nonmonetary applications such as the Internet of Things (IoT). In this article, we study the impact of deanonymization on the IoT-based blockchain. We populate a blockchain with data of smart home devices and then apply machine learning algorithms in an attempt to classify the transactions to a particular device that, in turn, risks the privacy of the users. Two types of attack models are defined: 1) informed attacks: where attackers know the type of devices installed in a smart home and 2) blind attacks: where attackers do not have this information. We show that machine learning algorithms can successful classify the transactions with 90% accuracy. To enhance the anonymity of the users, we introduce multiple obfuscation methods which include combining multiple packets into a transaction, merging ledgers of multiple devices, and delaying transactions. The implementation results show that these obfuscation methods significantly reduce the attack success rates to 20%–30% and, thus, enhance the user privacy.
A Blockchain-Based Decentralized Marketplace for Trustworthy Trade in Developing Countries

The possibilities that Distributed Ledger Technologies (DLTs) offer for cooperation, development, and achievement of the Sustainable Development Goals (SDGs) are remarkable. This is because DLTs enable several key features, such as sharing complete information about every data transaction in the distributed system that participants belong to, the immutability of the recorded data transactions, or consensus among what data can be regarded as true, of great usefulness for the implementation of the SDGs. As far as developing countries are concerned, this information could be useful in trading locally produced goods, as it could enhance the reputation and profitability of Small-Scale Producers (SSPs). Unfortunately, it is rare to find a digitalized marketplace that has been specifically implemented for this application domain. This paper puts forward a blockchain-based marketplace that makes use of Smart Contracts and offers information about how the sold goods were produced and can be traced to their very origin. Besides, cloud computing has been conceived to be used in this development from the beginning to reduce the computational resources required by end user operations. An implementation with cloud computing facilities, software components running on the Ethereum blockchain and a web front end have been tested with satisfactory performance results.
Highly private blockchain-based management system for digital COVID-19 certificates

As a result of the declaration of the COVID-19 pandemic, several proposals of blockchain-based solutions for digital COVID-19 certificates have been presented. Considering that health data have high privacy requirements, a health data management system must fulfil several strict privacy and security requirements. On the one hand, confidentiality of the medical data must be assured, being the data owner (the patient) the actor that maintain control over the privacy of their certificates. On the other hand, the entities involved in the generation and validation of certificates must be supervised by a regulatory authority. This set of requirements are generally not achieved together in previous proposals. Moreover, it is required that a digital COVID-19 certificate management protocol provides an easy verification process and also strongly avoid the risk of forgery. In this paper we present the design and implementation of a protocol to manage digital COVID-19 certificates where individual users decide how to share their private data in a hierarchical system. In order to achieve this, we put together two different technologies: the use of a proxy re-encryption (PRE) service in conjunction with a blockchain-based protocol. Additionally, our protocol introduces an authority to control and regulate the centers that can generate digital COVID-19 certificates and offers two kinds of validation of certificates for registered and non-registered verification entities. Therefore, the paper achieves all the requirements, that is, data sovereignty, high privacy, forgery avoidance, regulation of entities, security and easy verification.
MS-IAF: Multi-Scale Information Augmentation Framework for Aircraft Detection

Aircrafts have been an important object of study in the field of multi-scale image object detection due to their important strategic role. However, the multi-scale detection of aircrafts and their key parts from remote sensing images can be a challenge, as images often present complex backgrounds and obscured conditions. Most of today’s multi-scale datasets consist of independent objects and lack mixed annotations of aircrafts and their key parts. In this paper, we contribute a multi-scale aircraft dataset (AP-DATA) consisting of 7000 aircraft images that were taken in complex environments and obscured conditions. Our dataset includes mixed annotations of aircrafts and their key parts. We also present a multi-scale information augmentation framework (MS-IAF) to recognize multi-scale aircrafts and their key parts accurately. First, we propose a new deep convolutional module ResNeSt-D as the backbone, which stacks scattered attention in a multi-path manner and makes the receptive field more suitable for the object. Then, based on the combination of Faster R-CNN with ResNeSt-D, we propose a multi-scale feature fusion module called BFPCAR. BFPCAR overcomes the attention imbalance problem of the non-adjacent layers of the FPN module by reducing the loss of information between different layers and including more semantic features during information fusion. Based on AP-DATA, a dataset with three types of features, the average precision (AP) of MS-IAF reached 0.884, i.e., 2.67% higher than that of the original Faster R-CNN. The APs of these two modules were improved by 2.32% and 1.39%, respectively. The robustness of our proposed model was validated using the open sourced RSOD remote sensing image dataset, and the best accuracy was achieved.
An anomalous sound detection methodology for predictive maintenance

In the last decade, Anomalous Sound Detection (ASD) is becoming an increasingly challenging task for a plethora of applications due to the widespread diffusion of Deep Neural Networks. Nevertheless, the arise of recent cyber–physical attacks (i.e. Triton or Stuxnet), that deceive monitoring platforms, pose novel and challenging issues. For this reason, advanced predictive maintenance techniques are starting to exploit sounds generated by particular industrial equipment, whose analysis can unveil symptom of possible failures. For this kind of context, it is very easy to collect data related to normal and abnormal behavior of a given machinery, thus several kinds of deep neural architectures can be effectively trained to predict eventual downtime situations. In this paper, we propose a novel deep learning-based methodology for anomalous sound detection task, having flexibility, modularity and efficiency characteristics. The proposed methodology analyzes audio clips based on the mel-spectogram and ID equipment information, while a one-hot encoding method extracts features that are, successively, used to train an ID Conditioned Network. In particular, the main novelty of the proposed methodology concerns the conditioning of an autoencoder by jointly analyzing the relationships between mel-spectogram and the related machine identifier through an encoder–decoder architecture for computing an anomaly score related to the input sequence. Several experiments have been made for investigating the efficiency and effectiveness of the proposed methodology on multiple instances of different industrial machines (pumps, valves, slide rails and fans), achieving low inference time and memory requirements w.r.t. the other approaches in the literature.
YOLOv5-MGC: GUI Element Identification for Mobile Applications Based on Improved YOLOv5

The identification of interface elements is the first step in mobile application automated testing and the key to smooth testing. However, existing object detection algorithms have a low accuracy rate, and some tiny elements are missed in the recognition of graphical user interface (GUI) elements. To address this limitation, this paper proposes the YOLOv5-MGC algorithm, a robot vision-based interface element recognition algorithm for mobile applications. The algorithm improves the network by using K-means++ algorithm for target anchor box generation, applying the attention mechanism, adding a microscale detection layer, and introducing the Ghost bottleneck module. The proposed approach enhances the recognition accuracy of the elements through the target anchor box and attention mechanism. Moreover, it enhances the network’s ability to detect tiny elements, which improves the shortcomings of the current target detection algorithm and is conducive to further promoting mobile application robot testing and enhancing robot testing automation. Experimental results show that the YOLOv5-MGC algorithm is superior to the YOLOv5 for object detection in the recognition of GUI elements, with the mean average precision (mAP_0.5) reaching 89.8% and the recognition precision reaching 80.8%.
Developing Security Assurance Metrics to Support Quantitative Security Assurance Evaluation

Security assurance (SA) is a technique that helps organizations to appraise the trust and confidence that a system can be operated correctly and securely. To foster effective SA, there must be systematic techniques to reflect the fact that the system meets its security requirements and, at the same time, is resilient against security vulnerabilities and failures. Quantitative SA evaluation applies computational and mathematical techniques for deriving a set of SA metrics to express the assurance level that a system reaches. Such metrics are intended to quantify the strength and weaknesses of the system that can be used to support improved decision making and strategic planning initiatives. Utilizing metrics to capture and evaluate a system’s security posture has gained attention in recent years. However, scarce work has described how to combine SA evaluation while taking into account both SA metrics modeling and analysis. This paper aims to develop a novel approach for the modeling, calculation, and analysis of SA metrics that could ultimately enhance quantitative SA evaluation.
Deep Object Co-Segmentation and Co-Saliency Detection via High-Order Spatial-Semantic Network Modulation

Object co-segmentation (CSG) is to segment the common objects of the same category in multiple relevant images while the co-saliency detection (CSD) aims to discover the salient and common foreground objects in a group of images. To process both tasks simultaneously, this paper presents an adaptive spatially and high-order semantically modulated deep network framework. A backbone network is first adopted to extract multi-resolution image features. With the multi-resolution features of the relevant images as input, we design an adaptive spatial modulator to learn a spatial representation that can highlight the co-object regions for each image. The adaptive spatial modulator fully captures the rich correlations of all image feature descriptors via unsupervised clustering and a graph aggregation strategy. The learned representation can well localize the common foreground object while effectively suppressing the background signals. For the high-order semantic modulator, we model it as a supervised image classification task. We propose a hierarchical high-order pooling module to learn the rich semantic features for classification use. The outputs of the two modulators manipulate the multi-resolution features by a shift-and-scale operation so that the features focus on segmenting common object regions. The proposed model is trained end-to-end without any intricate post-processing. Extensive experiments on three CSG benchmark datasets (MSRC, i-Coseg, and PASCAL-VOC) and three CSD datasets (Cosal2015, CoCA, and CoSOD3k) demonstrate the superior accuracy of the proposed method compared to state-of-the-art methods on both tasks.
Combining Keyframes and Image Classification for Violent Behavior Recognition

Surveillance cameras are increasingly prevalent in public places, and security services urgently need to monitor violence in real time. However, the current violent-behavior-recognition models focus on spatiotemporal feature extraction, which has high hardware resource requirements and can be affected by numerous interference factors, such as background information and camera movement. Our experiments have found that violent and non-violent video frames can be classified by deep-learning models. Therefore, this paper proposes a keyframe-based violent-behavior-recognition scheme. Our scheme considers video frames as independent events and judges violent events based on whether the number of keyframes exceeds a given threshold, which reduces hardware requirements. Moreover, to overcome interference factors, we propose a new training method in which the background-removed and original image pair facilitates feature extraction of deep-learning models and does not add any complexity to the networks. Comprehensive experiments demonstrate that our scheme achieves state-of-the-art performance for the RLVS, Violent Flow, and Hockey Fights datasets, outperforming existing methods.
Evolutionary diversity optimisation for the traveling thief problem

There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem. This can provide the practitioners with invaluable information about the solution space and robustness against imperfect modelling and minor problems' changes. It also enables the decision-makers to involve their interests and choose between various solutions. In this study, we investigate for the first time a prominent multi-component optimisation problem, namely the Traveling Thief Problem (TTP), in the context of evolutionary diversity optimisation. We introduce a bi-level evolutionary algorithm to maximise the structural diversity of the set of solutions. Moreover, we examine the inter-dependency among the components of the problem in terms of structural diversity and empirically determine the best method to obtain diversity. We also conduct a comprehensive experimental investigation to examine the introduced algorithm and compare the results to another recently introduced framework based on the use of Quality Diversity (QD). Our experimental results show a significant improvement of the QD approach in terms of structural diversity for most TTP benchmark instances.
Exploring the feature space of TSP instances using quality diversity

Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by a $(\mu+1)$-EA for TSP instance generation.
Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm

Scene text detection is an important and challenging task in computer vision. For detecting arbitrarily-shaped texts, most existing methods require heavy data labeling efforts to produce polygon-level text region labels for supervised training. In order to reduce the cost in data labeling, we study mixed-supervised arbitrarily-shaped text detection by combining various weak supervision forms (e.g., image-level tags, coarse, loose and tight bounding boxes), which are far easier to annotate. Whereas the existing weakly-supervised learning methods (such as multiple instance learning) do not promote full object coverage, to approximate the performance of fully-supervised detection, we propose an Expectation-Maximization (EM) based mixed-supervised learning framework to train scene text detector using only a small amount of polygon-level annotated data combined with a large amount of weakly annotated data. The polygon-level labels are treated as latent variables and recovered from the weak labels by the EM algorithm. A new contour-based scene text detector is also proposed to facilitate the use of weak labels in our mixed-supervised learning framework. Extensive experiments on six scene text benchmarks show that (1) using only 10% strongly annotated data and 90% weakly annotated data, our method yields comparable performance to that of fully supervised methods, (2) with 100% strongly annotated data, our method achieves state-of-the-art performance on five scene text benchmarks (CTW1500, Total-Text, ICDAR-ArT, MSRA-TD500, and C-SVT), and competitive results on the ICDAR2015 Dataset. We will make our weakly annotated datasets publicly available.
Clustered federated learning with weighted model aggregation for imbalanced data

As a promising edge learning framework in future 6G networks, federated learning (FL) faces a number of technical challenges due to the heterogeneous network environment and diversified user behaviors. Data imbalance is one of these challenges that can significantly degrade the learning efficiency. To deal with data imbalance issue, this work proposes a new learning framework, called clustered federated learning with weighted model aggregation (weighted CFL). Compared with traditional FL, our weighted CFL adaptively clusters the participating edge devices based on the cosine similarity of their local gradients at each training iteration, and then performs weighted per-cluster model aggregation. Therein, the similarity threshold for clustering is adaptive over iterations in response to the time-varying divergence of local gradients. Moreover, the weights for per-cluster model aggregation are adjusted according to the data balance feature so as to speed up the convergence rate. Experimental results show that the proposed weighted CFL achieves a faster model convergence rate and greater learning accuracy than benchmark methods under the imbalanced data scenario.