Reviewerly/exHarmony huggingface dataset:
Managing cyber risk, a science in the making

Not a day goes by without news about a cyber attack. Fear spreads out and lots of wrong ideas circulate. This survey aims at showing how all these uncertainties about cyber can be transformed into manageable risk. After reviewing the main characteristics of cyber risk, we consider the three layers of cyber space: hardware, software and psycho-cognitive layer. We ask ourselves how is this risk different from others, how modelling has been tackled and needs to evolve, and what are the multi-facetted aspects of cyber risk management. This wide exploration pictures a science in the making and points out the questions to be solved for building a resilient society.
Readability and Clarity of Individual Pension Product Contracts

Abstract The study analyses the readability and clarity of economic specialised texts on supplementary individual pensions. The research includes agreements on individual pension products available on the Polish market at the beginning of 2017. The level of comprehensibility of language has been assessed using the “Jasnopis” application, based, among others, on the FOG index, while the structure of the content of the agreements has been assessed by an expert linguist (the expert linguist is one of the authors of this and a number of other publications on clear and understandable texts). In addition, one of the texts was subject to a survey, the so-called cloze text, which provided more accurate information about readability for a specific reader group. The results obtained allow the conclusion that the language of agreements on individual pension products is very difficult and their understanding requires higher education or expertise in the field of finance.
Libraries on metaverse, do they exist?

Purpose This paper aims to explore the existence of Metaverse or 3D virtual libraries and how they use Metaverse to expand library services. Design/methodology/approach The authors use literature to search projects and research papers that discuss the metaverse library. They also did observations on metaverse libraries. Findings There are some virtual realities or metaverse platforms that a library or virtual community can use to build their 3D virtual world. Some libraries already create a virtual world library or metaverse library. However, those initiatives are still in the prototype stage and are used for exhibitions and projects. Originality/value This paper overviews and builds a simple understanding of the metaverse library. The value of the paper is giving motivation when building the metaverse library. This initiative shows that big hi-tech companies do not dominate Metaverse.
Local Pixel Attack Based on Sensitive Pixel Location for Remote Sensing Images

As deep neural networks (DNNs) are widely used in the field of remote sensing image recognition, there is a model security issue that cannot be ignored. DNNs have been shown to be vulnerable to small perturbations in a large number of studies in the past, and this security risk naturally exists in remote sensing object detection models based on DNNs. The complexity of remote sensing object detection models makes it difficult to implement adversarial attacks on them, resulting in the current lack of systematic research on adversarial examples in the field of remote sensing image recognition. In order to better deal with the adversarial threats that remote sensing image recognition models may confront and to provide an effective means for evaluating the robustness of the models, this paper takes the adversarial examples for remote sensing image recognition as the research goal and systematically studies vanishing attacks against a remote sensing image object detection model. To solve the problem of difficult attack implementation on remote sensing image object detection, adversarial attack adaptation methods based on interpolation scaling and patch perturbation stacking are proposed in this paper, which realizes the adaptation of classical attack algorithms. We propose a hot restart perturbation update strategy and the joint attack of the first and second stages of the two-stage remote sensing object detection model is realized through the design of the attack loss function. For the problem of the modification cost of global pixel attack being too large, a local pixel attack algorithm based on sensitive pixel location is proposed in this paper. By searching the location of the sensitive pixels and constructing the mask of attack area, good local pixel attack effect is achieved. Experimental results show that the average pixel modification rate of the proposed attack method decreases to less than 4% and the vanishing rate can still be maintained above 80%, which effectively achieves the balance between attack effect and attack cost.
DroNER: Dataset for drone named entity recognition

The dataset is constructed from the drone flight log messages extracted from publicly available drone image datasets provided by VTO Labs under the Drone Forensic Program. The entire process of building this dataset includes extraction, decryption, parsing, cleansing, unique filtering, annotation, splitting, and analysis. The resulting dataset is in CoNLL format, annotated using the IOB2 scheme with six entity types. The total number of log messages acquired from 12 DJI drone models is 1850. The data are split based on the drone models, resulting in 1412 messages for training and 438 messages for testing. The average length of log messages is 6.5 globally, 6.6 and 8.8 for the train and the test sets, respectively.
Transfer adversarial attacks across industrial intelligent systems

As indispensable parts of industrial production control, data-driven industrial intelligent systems (IIS) achieve efficient executions of significant tasks such as fault classification (FC), fault detection (FD), and soft sensing (SS). Recently, machine learning models have been proven vulnerable to adversarial attacks, where the transfer-based attacks provide highly feasible attacks on systems in real-world black-box scenarios. In this paper, to study the practical security risks of IIS, we investigate transferable adversarial attacks from: (1) showing the existence of transferable adversarial examples across different industrial tasks; (2) exploring factors (e.g., data feature, model structure, and attack method) affecting transferability under multi-scenarios; (3) proposing a new method to enhance the transferability; (4) providing guidelines on practical system deployments to defend against transferable adversarial threats. The attacks demonstrate generality on two types of datasets, Tennessee Eastman industrial process (TEP) and WM-811K wafer map dataset, and the experiment results show that: (1) transfer is asymmetric and complex models are relatively stable with low sample transferability; (2) iterative and single-step methods have opposite performance characteristics under the intra- and cross-task transfer; (3) overfitting of optimization methods leads to weak transferability; (4) smoothing gradients and widening intermediate layer perturbations are effective directions for improving transferability.
Building Change Maturity in Organisations

‘Building change maturity in organisations' explores what change maturity looks like in practice and how increased maturity brings a myriad of benefits to the organisation investing in it. Building maturity of any kind is often seen as a luxury, and with the growing amount of change organisations contend with, this is no longer the case. It's becoming increasingly clear that if an organisation does not develop the capability to change effectively, it will metaphorically miss the boat. This tipping point is very real, making the subject of building change maturity a poignant one. The impact of change maturity is often seen via change management on the project or program levels, and ROI measurements don't often go beyond this focus. Building the capability at the organisational level might appear like a theoretical exercise for those managing change, but with the right mindset, change capability can already be built into the transformation projects already taking place.
The Stories We Tell About Data: Surveying Data-Driven Storytelling Using Visualization

The emerging practice of data-driven storytelling is framing data using familiar narrative mechanisms, such as slideshows, videos, and comics, to make even highly complex phenomena understandable. In this survey, we propose a taxonomy focused specifically on media types for the purpose of widening the purview of data-driven storytelling by putting more tools into the hands of designers. The classification shows that current data-driven storytelling practice does not yet leverage the full repertoire of media that can be used for storytelling, such as the spoken word, e-learning, and video games. Using our taxonomy as a generative tool, we also explore three novel storytelling mechanisms, including for live streaming, gesture-driven oral presentations, and data-driven comics.
Dynamic Perfusion Representation and Aggregation Network for Nodule Segmentation Using Contrast-Enhanced US

Dynamic contrast-enhanced ultrasound (CEUS) imaging has been widely applied in lesion detection and characterization, due to its offered real-time observation of microvascular perfusion. Accurate lesion segmentation is of great importance to the quantitative and qualitative perfusion analysis. In this paper, we propose a novel dynamic perfusion representation and aggregation network (DpRAN) for the automatic segmentation of lesions using dynamic CEUS imaging. The core challenge of this work lies in enhancement dynamics modeling of various perfusion areas. Specifically, we divide enhancement features into the two scales: short-range enhancement patterns and long-range evolution tendency. To effectively represent real-time enhancement characteristics and aggregate them in a global view, we introduce the perfusion excitation (PE) gate and cross-attention temporal aggregation (CTA) module, respectively. Different from the common temporal fusion methods, we also introduce an uncertainty estimation strategy to assist the model to locate the critical enhancement point first, in which a relatively distinguished enhancement pattern is displayed. The segmentation performance of our DpRAN method is validated on our collected CEUS datasets of thyroid nodules. We obtain the mean dice coefficient (DSC) and intersection of union (IoU) of 0.794 and 0.676, respectively. Superior performance demonstrates its efficacy to capture distinguished enhancement characteristics for lesion recognition.
Automated Brain Tumor Segmentation Using GAN Augmentation and Optimized U-Net

Children and adults with brain tumors are among the top causes of death in the world. Deep learning-based approaches have seen considerable success in brain tumor segmentation in recent years. Medical image tasks like tumor segmentation are severely limited by the time and effort necessary to collect paired medical imaging datasets. When gathering multi-modal image data, the problem becomes more complicated. However, this problem can be overcome by leveraging generative adversarial networks (GAN), which can generate synthesized images. The proposed automated brain tumor segmentation is made up of two modules. The first is a DCGAN network, which produces a binary tumor mask that is to be overlayed on a healthy brain image. Then, the overlayed brain image is used as an input for the pix2pix GAN network that applies for style transfer and generates realistic output that is indistinguishable from a real input. Thus, we are able to generate synthetic paired data to augment our dataset. The next module is the segmentation model that comprises a U-Net architecture that is optimized with residual blocks, the addition of residual blocks improves training time, training accuracy, and validation accuracy. The experimental results show that the synthesized dataset created by our proposed method and optimized U-Net model significantly improve the tumor segmentation performance and achieves the dice and IOU scores of 0.93 and 0.87, respectively.
A Crowd Counting Method Based on Multi-Scale Attention Network

The task of crowd counting in computer vision is quite difficult, and a lot of excellent work has emerged recently. Recent research has suggested network algorithms that utilize both shallow feature information and deep information that the network has gathered. However, how to efficiently use these different scales of information is a question worth considering. We propose a multi-scale attention network (MSANet), which consists of a convolution module and a spatial attention module. The feature information of different scales extracted from the middle and deep layers is fused to obtain richer semantic features for crowd counting task detection. At the same time, considering that the fusion of different scale information will introduce some additional background information, we filter the redundant background information by introducing the attention module. Experiments show that this method has achieved good performance improvement on public datasets JHU + + and UCF-QNRF.
Semantic Segmentation of Traffic Scene Based on DeepLabv3+ and Attention Mechanism

In the increasingly mature autonomous driving technology, acquiring traffic scene information is a guarantee to improve driving safety. The emergence of semantic segmentation technology enables the segmentation of images at the pixel level into regions associated with semantic categories of scenes. This technology improves the vehicle perception and analysis of real-time traffic environment information, thus enhancing the safety of autonomous driving technology. The currently accepted semantic segmentation model DeepLabv3+ still suffers from small targets being ignored and similarly shaped objects being incorrectly segmented in the segmentation task, resulting in a need for further improvement in the accuracy of the result presentation. To address this problem, this paper proposes an improved DeepLabv3+ method for semantic segmentation of road scenes with a fused spatial attention mechanism based on the structure of DeepLabv3+ network, combined with the attention mechanism to increase the weights of segmented regions. After determining the use of Focal Loss, the ASPP structure is improved. The segmentation effects of the models with different ways of introducing the attention mechanism are compared and tested on the Cityscapes dataset. The experimental results show that the improved model segmentation accuracy is improved by 1.56% on average compared to DeepLabv3+, which is an improvement in cross-comparison.
FedOES: An Efficient Federated Learning Approach

Federated Learning (FL) is a distributed approach for performing machine learning tasks. It prevents data sharing by aggregating the models trained by distributed clients on the central server, thereby maintaining data privacy. However, a major challenge in FL architecture is the high communication costs incurred by clients in multiple communication rounds, which can create communication bottlenecks. Additionally, the issue of non-independent and identical distribution (Non-IID) data poses a significant challenge. To address these challenges, our paper proposes a method based on in-cluster training and top-k gradient sparsification. The clients adopt the in-cluster training strategy to mitigate the negative effects of Non-IID data, while sparse gradient is implemented on both the clients and the server to reduce communication costs. Our experiments demonstrate that our proposed approach, FedOES, outperforms federated averaging in common FL scenarios. It not only improves communication efficiency, but also achieves excellent performance in scenarios where the data is Non-IID.
Boosted Gaze Gesture Recognition Using Underlying Head Orientation Sequence

People find it challenging to control smart systems with complex gaze gestures due to the vulnerability of eye saccades.Instead, the existing works achieved good recognition accuracy of simple gaze gestures because of sufficient eye gaze points but simple gaze gestures have limited applications compared to complex gaze gestures.Complex gaze gestures need a composition of multiple subunits of eye fixation to contain a sequence of gaze points that are clustered and rotated with an underlying complex head orientation relationship.This paper proposes a new set of eye gaze points and head orientation angles as new sequences to recognize complex gaze gestures.Eye gaze points and head orientation angles have a powerful influence on gaze gesture formation.The new sequence was obtained by aligning clustered gaze points and head orientation angles with a simple moving average (SMA) to denoise and interpolate the gap between successive eye fixations.The aligned new sequence of complex gaze gestures was utilized to train sequential machine learning (ML) algorithms.To evaluate the performance of the proposed method, we recruited and recorded the eye gaze and head orientation features of ten participants using an eye tracker.The results show that Boosted Hidden Markov Models (HMM) using Random Subspace methods achieved the best accuracies of 94.72% and 98.1% for complex, and simple gestures respectively, which outperformed the conventional methods.
Adapted Single Scale Retinex Algorithm for Nighttime Image Enhancement

Images captured at night with low-light conditions frequently have a loss of visible details, inadequate contrast, low brightness, and noise. Therefore, it is difficult to perceive, extract, and analyze important visual information from these images, unless they were properly processed. Different algorithms exist to process nighttime images, yet most of these algorithms are highly complex, generate processing artifacts, over-smooth the images, or do not improve the illumination adequately. Thus, the single scale retinex (SSR) algorithm is adopted in this study to provide better processing for nighttime images. The proposed algorithm starts by converting the color image from the RGB model to the HSV model and enhancing the V channel only while preserving the H and S channels. Then, it determined the image's illuminated version somewhat like the SSR, computes the logarithms of the illuminated and original images, then subtracts these two images by utilizing an altered procedure. Next, a modified gamma-adjusted Rayleigh distribution function is applied, and its outcome is processed once more by an automatic linear contrast stretching approach to produce the processed V channel that will be utilized with the preserved H and S channels to generate the output RGB image. The developed algorithm is assessed using a real dataset of nighttime images, evaluated using three dedicated image evaluation methods, and compared to ten dissimilar contemporary algorithms. The obtained results demonstrated that the proposed algorithm can significantly improve the perceptual quality of nighttime images and suppress artifact generation rapidly and efficiently, in addition to showing the ability to surpass the performance of different existing algorithms subjectively and objectively.
To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering

Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques.It suggests items to users based on the ratings of other users who share their preferences.Thus, one of the aims of CF is to find reliable neighbours.Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance.User reviews can be essential in overcoming those situations because of the diverse elements available in reviews.The most popular element is aspects, which can provide a fine-grained analysis of users' behaviours, thus improving personalised recommendations.However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance.As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect th e performance of CF systems.This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction.The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set.Our approach extracts aspects and represents them using Google's pre-trained Word2vec model.Then, aspects are organised into clusters using the K-means clustering algorithm.Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the kNN algorithm.This study also identifies the number of aspects that significantly impacts CF performance.Experiments are carried out using a real large-scale dataset: the Amazon movie dataset.Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches.Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.
An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.
Obstacle Avoidance for Trackless Rubber-Tired Vehicle Based on Risk-Grid Particle Swarm Optimization in Confined Space of Deep Well

Obstacle avoidance planning is the critical technology for driverless trackless rubber-tired vehicles (TRTV) in confined spaces of the deep wells. The narrow, long and circuitous characteristics of the confined spaces challenge the conventional obstacle avoidance planning methods for vehicle operation. Therefore, this article studies the obstacle avoidance planning method for the driverless TRTV. Firstly, a novel environment model is constructed with a basic grid map and a risk one. The basic grid map describes the obstacle information with multiple planes, while the risk grid map characterizes the safety of drivable areas with five levels. Then, an improved obstacle avoidance planning algorithm is proposed by designing a risk grid particle swarm optimization (RGPSO) algorithm. Finally, by resorting to four typical map benchmarks, our proposed RGPSO is comprehensively evaluated. Compared with the existing heuristic algorithm based obstacle avoidance planning algorithms, the proposed obstacle avoidance planning method has significant merits for complex environment with narrow detours and dead zone traps. Furthermore, based on our results, a two-layer environment model is build for the case mine, and RGPSO is used for obstacle avoidance planning. The results meet the practical mining requirement.