Reviewerly/exHarmony huggingface dataset:
Evaluation of a mandatory phishing training program for high-risk employees at a US healthcare system

Abstract Objective The study sought to understand the impact of a phishing training program on phishing click rates for employees at a single, anonymous US healthcare institution. Materials and Methods We stratified our population into 2 groups: offenders and nonoffenders. Offenders were defined as those that had clicked on at least 5 simulated phishing emails and nonoffenders were those that had not. We calculated click rates for offenders and nonoffenders, before and after a mandatory training program for offenders was implemented. Results A total of 5416 unique employees received all 20 campaigns during the intervention period; 772 clicked on at least 5 emails and were labeled offenders. Only 975 (17.9%) of our set clicked on 0 phishing emails over the course of the 20 campaigns; 3565 (65.3%) clicked on at least 2 emails. There was a decrease in click rates for each group over the 20 campaigns. The mandatory training program, initiated after campaign 15, did not have a substantial impact on click rates, and the offenders remained more likely to click on a phishing simulation. Discussion Phishing is a common threat vector against hospital employees and an important cybersecurity risk to healthcare systems. Our work suggests that, under simulation, employee click rates decrease with repeated simulation, but a mandatory training program targeted at high-risk employees did not meaningfully decrease the click rates of this population. Conclusions Employee phishing click rates decrease over time, but a mandatory training program for the highest-risk employees did not decrease click rates when compared with lower-risk employees.
Building an ontological knowledgebase for bridge maintenance

The operation stage has the biggest potential value in the bridge life cycle management, and it often critically influences the overall cost of the bridge. As such, changes in the efficiency of the project's operation stage could be of significant benefit to the overall project. However, current approaches in the operation stage often lack the effective support of computer-aided tools. This research presents a holistic method based on an ontology to achieve automatic rule checking and improve the management and communication of knowledge related to bridge maintenance. The developed ontology can also facilitate a smarter decision-making process for bridge management by informing engineers of choices with different considerations. Three approaches; semantic validation, syntactical validation, and case study validation, have been adopted to evaluate this ontology and demonstrate how the developed ontology can be used by engineers when dealing with different issues. The results showed that this approach can create a holistic knowledge base that can integrate various domain knowledge to enable bridge engineers to make more comprehensive decisions rather than a single objective-targeted delivery.
First-person view of one’s body in immersive virtual reality: Influence on episodic memory

Episodic memories (EMs) are recollections of contextually rich and personally relevant past events. EM has been linked to the sense of self, allowing one to mentally travel back in subjective time and re-experience past events. However, the sense of self has recently been linked to online multisensory processing and bodily self-consciousness (BSC). It is currently unknown whether EM depends on BSC mechanisms. Here, we used a new immersive virtual reality (VR) system that maintained the perceptual richness of life episodes and fully controlled the experimental stimuli during encoding and retrieval, including the participant's body. Our data reveal a classical EM finding, which shows that memory for complex real-life like scenes decays over time. However, here we also report a novel finding that delayed retrieval performance can be enhanced when participants view their body as part of the virtual scene during encoding. This body effect was not observed when no virtual body or a moving control object was shown, thereby linking the sense of self, and BSC in particular, to EMs. The present VR methodology and the present behavioral findings will enable to study key aspects of EM in healthy participants and may be especially beneficial for the restoration of self-relevant memories in future experiments.
Secure Multi-UAV Collaborative Task Allocation

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields.However, they are unable to meet large-scale and complex missions with a limited energy reserve.Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied.In this paper, a new systematic framework is proposed to solve the problem of multi-UAV collaborative task allocation.It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm.The purpose is to enable multi-UAV to complete tasks with lower energy consumption.As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed.Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution.We have also proposed an improved method to improve the accuracy of task allocation further.In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems.Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation; moreover, compared with other task allocation methods, our method has great performance.
Incentives in Ethereum’s Hybrid Casper Protocol

We present an overview of hybrid Casper the Friendly Finality Gadget FFG: a Proof-of-Stake checkpointing protocol overlaid onto Ethereum's Proof-of-Work blockchain. We describe its core functionalities and reward scheme, and explore its properties. Our findings indicate that Casper's implemented incentives mechanism ensures liveness, while providing safety guarantees that improve over standard Proof-of-Work protocols. Based on a minimal-impact implementation of the protocol as a smart contract on the blockchain, we discuss additional issues related to parametrisation, funding, throughput and network overhead and detect potential limitations.
Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection

In this paper, a hybrid fuzzy clustering techniques using Salp Swarm Algorithm (SSA) is proposed. The proposed fuzzy clustering method is used to optimize the cluster centroids obtained as an under sampling method. The performance of the proposed fuzzy clustering method is compared with some well-k nown clustering algorithms to shows the superiority of the proposed clustering algorithm. In addition, a novel hybrid Automobile Insurance Fraud Detection System is proposed in which undersampling of the majority class is performed by using the proposed fuzzy clustering algorithm which eliminates the outliers from the majority class samples. The balanced dataset for automobile fraud detection obtained after undersampling undergoes classification. Different classifiers used for this purpose are Random Forest Classifier, Logistic Regression Classifier and XGBoost Classifier. The performance of each of the three classifiers is evaluated by considering different performance metrics such as sensitivity, accuracy and specificity. The proposed fuzzy clustering method along with XGBoost outperforms the other methods presented.
A Hybrid Particle Swarm Optimization-Cuckoo Search Algorithm and Its Engineering Applications

This paper deals with the hybrid particle swarm optimization-Cuckoo Search (PSO-CS) algorithm which is capable of solving complicated nonlinear optimization problems. It combines the iterative scheme of the particle swarm optimization (PSO) algorithm and the searching strategy of the Cuckoo Search (CS) algorithm. Details of the PSO-CS algorithm are introduced; furthermore its effectiveness is validated by several mathematical test functions. It is shown that Lévy flight significantly influences the algorithm’s convergence process. In the second part of this paper, the proposed PSO-CS algorithm is applied to two different engineering problems. The first application is nonlinear parameter identification for the motor drive servo system. As a result, a precise nonlinear Hammerstein model is obtained. The second one is reactive power optimization for power systems, where the total loss of the researched IEEE 14-bus system is minimized using PSO-CS approach. Simulation and experimental results demonstrate that the hybrid optimal algorithm is capable of handling nonlinear optimization problems with multiconstraints and local optimal with better performance than PSO and CS algorithms.
Deep learning-based hand gesture recognition for collaborative robots

This paper is a first step towards a smart hand gesture recognition set up for Collaborative Robots using a Faster R-CNN Object Detector to find the accurate position of the hands in RGB images. In this work, a gesture is defined as a combination of two hands, where one is an anchor and the other codes the command for the robot. Other spatial requirements are used to improve the performances of the model and filter out the incorrect predictions made by the detector. As a first step, we used only four gestures.
Corpus-based dictionaries for sentiment analysis of specialized vocabularies

Abstract Contemporary dictionary-based approaches to sentiment analysis exhibit serious validity problems when applied to specialized vocabularies, but human-coded dictionaries for such applications are often labor-intensive and inefficient to develop. We demonstrate the validity of “minimally-supervised” approaches for the creation of a sentiment dictionary from a corpus of text drawn from a specialized vocabulary. We demonstrate the validity of this approach in estimating sentiment from texts in a large-scale benchmarking dataset recently introduced in computational linguistics, and demonstrate the improvements in accuracy of our approach over well-known standard (nonspecialized) sentiment dictionaries. Finally, we show the usefulness of our approach in an application to the specialized language used in US federal appellate court decisions.
TAS

As datacenter network speeds rise, an increasing fraction of server CPU cycles is consumed by TCP packet processing, in particular for remote procedure calls (RPCs). To free server CPUs from this burden, various existing approaches have attempted to mitigate these overheads, by bypassing the OS kernel, customizing the TCP stack for an application, or by offloading packet processing to dedicated hardware. In doing so, these approaches trade security, agility, or generality for efficiency. Neither trade-off is fully desirable in the fast-evolving commodity cloud.
Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems

This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities to integrate ML with tele-ICU CDSS. The enormous quantities of data generated by tele-ICU systems is a major driver in the development of the large, comprehensive, heterogeneous, and granular data sets necessary to develop generalizable ML CDSS algorithms, and deidentification of these data sets expands opportunities for ML CDSS research.
Multi-verse Optimizer: Theory, Literature Review, and Application in Data Clustering

Multi-verse optimizer (MVO) is considered one of the recent metaheuristics. MVO algorithm is inspired from the theory of multi-verse in astrophysics. This chapter discusses the theoretical foundation, operations, and main strengths behind this algorithm. Moreover, a detailed literature review is conducted to discuss several variants of the MVO algorithm. In addition, the main applications of MVO are also thoroughly described. The chapter also investigates the application of the MVO algorithm in tackling data clustering tasks. The proposed algorithm is benchmarked by several datasets, qualitatively and quantitatively. The experimental results show that the proposed MVO-based clustering algorithm outperforms several similar algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA) in terms of clustering purity, clustering homogeneity, and clustering completeness.
Financial innovation, economic growth, and the consequences of macroprudential policies

We leverage a ‘catch-all’ measure of financial innovation—research and development spending in the financial sector—to assess the net relationship between financial innovation and economic growth and evaluate the influence of macroprudential policy on this relationship. Using a panel of 23 countries over the period of 1996–2014, our results demonstrate a net-positive relationship between financial innovation and gross capital formation. We find no evidence of a net-negative impact of financial innovation on economic growth, challenging the popular and political stigma surrounding financial innovation. We also find little robust evidence of macroprudential policy influencing the relationship between financial innovation and economic growth. Our results support a functional approach to the regulation of financial innovation, which improves the intermediation process, leading to increased capital formation.
Reversible Data Hiding Based on Multiple Two-Dimensional Histograms Modification

In pairwise prediction-error expansion (pairwise PEE) based reversible data hiding (RDH), the correlations among prediction errors are considered and utilized. The obtained performance is better than traditional PEE-based RDH. However, their performance can further be improved. In this letter, a new RDH algorithm based on multiple two-dimensional (2-D) histograms modification is proposed. In our new algorithm, by considering the predetermined pixel pair and its neighbors, a new prediction strategy which can be utilized to predict each element in the pixel pair is proposed. And then, according to the local complexities of the predetermined pixel pairs, multiple 2-D prediction-error sub-histograms are constructed. Moreover, eight new 2-D-maps are designed, and for each kind of 2-D prediction-error sub-histogram, the corresponding optimal 2-D mapping is exhaustively searched to further improve the performance of the proposed algorithm. Extensive experiments demonstrate the superiority of our scheme.
The promise of peer-to-peer trading? The potential impact of blockchain on the actor configuration in the Dutch electricity system

This paper considers the potential of blockchain technology to empower distributed and decentralized local electricity markets. Although blockchain has gained considerable attention in the last few years as a facilitator for new electricity markets, no attention has yet been given to its potential influence on the configuration of the actors in the electricity system and its ability to transform the existing system. Based on a social network analysis, this paper investigates how blockchain can influence the actor configuration of the electricity system in the Netherlands. After describing the Dutch system, we compare the existing with the potential future system' actor configuration and the corresponding expected shifts in functions and network position of the actors. We conclude that although many functions are likely to remain and new central authorities may be formed, the impact of blockchain does not seem to be as disruptive and decentralizing as may be expected. This study provides first contributions to the ongoing discussion about the potential of blockchain to disrupt and reshape the electricity system.
Sentiment analysis of social images via hierarchical deep fusion of content and links

Sentiment analysis is crucial for many social media analytic tasks. Earlier researches mainly focus on single modality, e.g., text description or visual content. Recently, more and more works pay attention to the incorporation of multiple modalities. Different from the traditional image database, social images usually interconnect with each other, which makes the sentiment analysis nontrivial. Most existing methods consider different images independently, which cannot be directly applied to the interconnected images. In this paper, we propose a novel Hierarchical Deep Fusion (HDF) model to explore the cross-modal correlations among images, texts, and their social links, which can learn comprehensive and complementary features for more effective sentiment analysis. Specifically, we combine the visual content with different semantic fragments of textual content through a three-level hierarchical LSTMs (H-LSTMs) to learn the inter-modal correlations between image and text at different levels. To exploit the link information effectively, the linkages among social images are modeled by a weighted relation network and each node is embedded into a distributed vector. Then, the extracted image–text features and node embeddings are fused by a Multi-Layer Perceptron (MLP) to further capture the non-linear cross-modal correlations for sentiment prediction. Comprehensive experiments are conducted to demonstrate the effectiveness of our approach on both machine weakly labeled and manually labeled datasets.
Augmented reality based approach for on-line quality assessment of polished surfaces

Augmented reality is considered one of the enabling technologies of the fourth industrial revolution, within the Industry 4.0 program and beyond. Indeed, augmented reality solutions can increase the working quality and the productivity and allow a better use of the human resources. This technology can help the operator in the industrial applications during the crucial phases of the processes. Since the quality assessment of the surfaces is recognized to be a key phase in the polishing process, in this paper we propose a novel method that exploits augmented reality to support the operators during this phase. The metrology data measured by a surface measurement system are directly projected on the polished component through an augmented reality headset worn by the operators and used to assess the quality of the worked surfaces. Rather than imagine how a certain parameter change can affect the result achieved, the information is directly there on the component’s surface. Users can see from the data where refinements are required and make better and faster decisions, which is compelling for its potential beyond industrial polishing. The proposed method is implemented and validated on an industrial cell, where the robot automatically perform the polishing task and move the head of the surface measurement system along the surface to measure the metrology parameters. Thanks to the proposed approach, the end-user and the operator can directly see on the component if the quality reached satisfies the specifications or if some parts of the surface require further refinements through additional polishing steps.
Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.
Anomaly Detection in Raw Audio Using Deep Autoregressive Networks

Anomaly detection involves the recognition of patterns outside of what is considered normal, given a certain set of input data. This presents a unique set of challenges for machine learning, particularly if we assume a semi-supervised scenario in which anomalous patterns are unavailable at training time meaning algorithms must rely on non-anomalous data alone. Anomaly detection in time series adds an additional level of complexity given the contextual nature of anomalies. For time series modelling, autoregressive deep learning architectures such as WaveNet have proven to be powerful generative models, specifically in the field of speech synthesis. In this paper, we propose to extend the use of this type of architecture to anomaly detection in raw audio. In experiments using multiple audio datasets we compare the performance of this approach to a baseline autoencoder model and show superior performance in almost all cases.
Evaluating Sustainable Interaction Design of Digital Services

Recent research has advocated for a broader conception of evaluation for Sustainable HCI (SHCI), using interdisciplinary insights and methods. In this paper, we put this into practice to conduct an evaluation of Sustainable Interaction Design (SID) of digital services. We explore how SID can contribute to corporate greenhouse gas (GHG) reduction strategies. We show how a Digital Service Provider (DSP) might incorporate SID into their design process and quantitatively evaluate a specific SID intervention by combining user analytics data with environmental life cycle assessment. We illustrate this by considering YouTube. Replacing user analytics data with aggregate estimates from publicly available sources, we estimate emissions associated with the deployment of YouTube to be approximately 10MtCO2e p.a. We estimate emissions reductions enabled through the use of an SID intervention from prior literature to be approximately 300KtCO2e p.a., and demonstrate that this is significant when considered alongside other emissions reduction interventions used by DSPs.