Reviewerly/exHarmony huggingface dataset:
Modeling effective cybersecurity training frameworks: A delphi method-based study

Today, cybersecurity training is commonplace in both large companies and Small & Medium Enterprise (SME). Nonetheless, the effectiveness of many of the current training offerings is put into question by reports of increasing successful cyber-attacks. While a number of models for developing Cybersecurity (CS) training frameworks for industrial personnel or general audience have been proposed, these models often lack consideration for humans aspects of learning (cognitive abilities, learning styles, meta-cognition among others) during development. Additionally, the success of a CS training program highly depends on its ability to engage participants. To develop a CS training framework that is able to motivate participants, we must consider individual-specific factors that can affect the result of training, besides establishing optimal training delivery methods and assessment. For this, in this work we propose a CS training framework based on a revised version of the ADDIE model and more recent research personalised learning theory. The Delphi method was used to both develop and validate our decisions during the development of the training framework model. The results of the decision of the Delphi method have later been compared to recommendations in the literature to create the finalised framework. This work presents two major distinctions from other CS training frameworks models described in the literature. First, the developed model is strongly based in learning theory foundations and takes into consideration differences in learning styles, cognitive abilities and metacognition of individuals, to offer tailored solutions optimized for each group of employees and single individual. Second, the use of the Delphi method and the involvement of experts stakeholders from various sides of academia and industry gave a wide insight into current needs and recommendations for CS training, as well as formal validation for the final development.
Efficient quantum computation of molecular forces and other energy gradients

While most work on the quantum simulation of chemistry has focused on computing energy surfaces, a similarly important application requiring subtly different algorithms is the computation of energy derivatives. Almost all molecular properties can be expressed an energy derivative, including molecular forces, which are essential for applications such as molecular dynamics simulations. Here, we introduce new quantum algorithms for computing molecular energy derivatives with significantly lower complexity than prior methods. Under cost models appropriate for noisy-intermediate scale quantum devices, we demonstrate how low-rank factorization and other tomography schemes can be optimized for energy derivative calculations. We numerically demonstrate that our techniques reduce the number of circuit repetitions required by many orders of magnitude for even modest systems, and that the cost of estimating an entire force vector may in some systems be lower than the cost of estimating the energy. In the context of fault-tolerant algorithms, we develop new methods of estimating energy derivatives with Heisenberg limited scaling, incorporating state-of-the-art techniques for block encoding fermionic operators. In contrast to our near-term results, we find that the cost of estimating forces with any of our Heisenberg-limited methods is bounded by the cost of estimating energies, due to inner loops requiring either energy estimation or reflections around the ground state. This implies that applications such as geometry optimization, coupling parameter estimation, and spectral prediction may be practical on fault-tolerant quantum devices, but tractable molecular dynamics simulations of large-scale systems requires further algorithmic advances.
Investigating Tradeoffs in Real-World Video Super-Resolution

The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairing output quality. To balance the tradeoff between detail synthesis and artifact suppression, we found an image precleaning stage in-dispensable to reduce noises and artifacts prior to propagation. Equipped with a carefully designed cleaning module, our RealBasicVSR outperforms existing methods in both quality and efficiency (Fig. 1). Second, real-world VSR models are often trained with diverse degradations to improve generalizability, requiring increased batch size to produce a stable gradient. Inevitably, the increased computational burden results in various problems, including 1) speed-performance tradeoff and 2) batch-length trade-off. To alleviate the first tradeoff, we propose a stochastic degradation scheme that reduces up to 40% of training time without sacrificing performance. We then analyze different training settings and suggest that employing longer sequences rather than larger batches during training allows more effective uses of temporal information, leading to more stable performance during inference. To facilitate fair comparisons, we propose the new VideoLQ dataset, which contains a large variety of real-world low-quality video sequences containing rich textures and patterns. Our dataset can serve as a common ground for benchmarking. Code, models, and the dataset are publicly available at https://github.com/ckkelvinchan/RealBasicVSR.
Semantic Query-Featured Ensemble Learning Model for SQL-Injection Attack Detection in IoT-Ecosystems

Structured query language (SQL) has emerged as one of the most used databases, serving an array of Internet-of-Things (IoTs)-enabled services including web-transactions, grid networks, industrial activity log and proactive decision systems, smart-home, financial transactions, business communication etc. With high pace increase in SQL-driven IoT applications, the threat of SQL-injection attacks (SQLIAs) at the middleware layer has increased significantly. To address such issues, machine learning-based SQLIA-prediction systems are proposed; however, majority of the existing methods are found limited in terms of intrusion detection accuracy because of their complete-reliance on structural features and inferior learning model(s). On the contrary, intruders these days intrude the system by mimicking the normal queries and hence confuses most of the classical learning-based methods. To alleviate such problems, this article emphasizes on exploiting semantic features along with the state-of-art highly robust computing environment. We proposed a robust semantic query-featured ensemble learning model for SQLIA prediction. Unlike classical (query's) template-matching or term-assessment-based methods, our proposed SQLIA-prediction model exploits latent semantic features from large SQL-queries to train an ensemble learning model that classifies each query as the normal query or the SQLIA query. Functionally, it performs preprocessing over large set of SQL-queries using count-vectorizer and stopping word removal. Subsequently, it applies Word2Vec feature extraction method over each query using continuous bag of words (CBOW) and N-skip gram (SKG) algorithms, which obtained CBOW and SKG semantic features from each SQL-query. The extracted features were processed for data resampling so as to alleviate the problem of class-imbalance and skewness. To alleviate redundant computation, two feature selection algorithms named Mann-Whitney significance predictor test and principal component analysis were applied over the resampled features. Moreover, to eliminate over-fitting and convergence problem, Min-Max normalization was performed over the selected features which were later processed for learning using a state-of-art robust heterogeneous ensemble learning model. Unlike standalone classifier-based SQLIA, the proposed learning-model employed a set of nine base classifiers designed to serve maximum voting ensemble-based prediction. The proposed ensemble-learning method classified each SQL-query as the normal-query or the SQLIA-query. Simulation results affirmed superiority of the proposed SQLIA prediction model in terms of accuracy (98%), F-Score (0.989), AUC (0.999) signifying its efficacy toward real-world SQL-driven IoT-ecosystems.
Knowledge structure enhanced graph representation learning model for attentive knowledge tracing

Knowledge tracing (KT) is a fundamental personalized-tutoring technique for learners in online learning systems. Recent KT methods employ flexible deep neural network-based models that excel at this task. However, the adequacy of KT is still challenged by the sparseness of the learners' exercise data. To alleviate the sparseness problem, most of the exiting KT studies are performed at the skill-level rather than the question-level, as questions are often numerous and associated with much fewer skills. However, at the skill level, KT neglects the distinctive information related to the questions themselves and their relations. In this case, the models can imprecisely infer the learners' knowledge states and might fail to capture the long-term dependencies in the exercising sequences. In the knowledge domain, skills are naturally linked as a graph (with the edges being the prerequisite relations between pedagogical concepts). We refer to such a graph as a knowledge structure (KS). Incorporating a KS into the KT procedure can potentially resolve both the sparseness and information loss, but this avenue has been underexplored because obtaining the complete KS of a domain is challenging and labor-intensive. In this paper, we propose a novel KS-enhanced graph representation learning model for KT with an attention mechanism (KSGKT). We first explore eight methods that automatically infer the domain KS from learner response data and integrate it into the KT procedure. Leveraging a graph representation learning model, we then obtain the question and skill embeddings from the KS-enhanced graph. To incorporate more distinctive information on the questions, we extract the cognitive question difficulty from the learning history of each learner. We then propose a convolutional representation method that fuses these disctinctive features, thus obtaining a comprehensive representation of each question. These representations are input to the proposed KT model, and the long-term dependencies are handled by the attention mechanism. The model finally predicts the learner's performance on new problems. Extensive experiments conducted from six perspectives on three real-world data sets demonstrated the superiority and interpretability of our model for learner-performance modeling. Based on the KT results, we also suggest three potential applications of our model.
Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.
How far are we from reproducible research on code smell detection? A systematic literature review

Code smells are symptoms of wrong design decisions or coding shortcuts that may increase defect rate and decrease maintainability. Research on code smells is accelerating, focusing on code smell detection and using code smells as defect predictors. Recent research shows that even between software developers, agreement on what constitutes a code smell is low, but several publications claim the high performance of detection algorithms—which seems counterintuitive, considering that algorithms should be taught on data labeled by developers. This paper aims to investigate the possible reasons for the inconsistencies between studies in the performance of applied machine learning algorithms compared to developers. It focuses on the reproducibility of existing studies. A systematic literature review was performed among conference and journal articles published between 1999 and 2020 to assess the state of reproducibility of the research performed in those papers. A quasi-gold standard procedure was used to validate the search. Modeling process descriptions, reproduction scripts, data sets, and techniques used for their creation were analyzed. We obtained data from 46 publications. 22 of them contained a detailed description of the modeling process, 17 included any reproduction data (data set, results, or scripts) and 15 used existing data sets. In most of the publications, analyzed projects were hand-picked by the researchers. Most studies do not include any form of an online reproduction package, although this has started to change recently—8% of analyzed studies published before 2018 included a full reproduction package, compared to 22% in years 2018–2019. Ones that do include a package usually use a research group website or even a personal one. Dedicated archives are still rarely used for data packages. We recommend that researchers include complete reproduction packages for their studies and use well-established research data archives instead of their own websites.
Practical AgentChain: A compatible cross-chain exchange system

Blockchain is a decentralized ledger system that enables transactional consensus among untrusted nodes. Due to the independence between blockchains, it is tough to complete asset exchange tasks between diverse chains. Facing this problem, multiple cross-chain exchange schemes were proposed, but they have not been widely used due to various defects in terms of compatibility, flexibility, security, and practicability. In this paper, we construct a comprehensive cross-chain exchange system called Practical AgentChain. Various coins can be mapped to the corresponding tokens on Practical AgentChain for trading. One or multiple trading operators can spontaneously form a service deposit pool on existing blockchains and register a trading group for profit using the well-designed smart contract called Agent Contract. All the trading groups competitively provide cross-chain exchange services. Clients with exchange currency requirements can freely select an appropriate trading group to request services, such as mapping assets to tokens or withdraw tokens. The choice of the trading group is made according to the group’s deposit and the group members’ reputation recorded on the Agent Contract. This system also incorporates Town crier as the reliable Data Oracle system for obtaining the status of cross-chain transactions and combines a matching exchange protocol to ensure the fairness of on-chain token exchange. In addition, we design an impartial service arbitration mechanism and a deposit allocation scheme to ensure the reliability of the system. Through security analysis, our system can resist re-use attacks, denial-of-service attacks and resolve service availability issues. We modify the Town crier adaptively and combine it with Ethereum to implement the simulation. The experimental results show that Practical AgentChain is efficient and low-cost. Besides, our system can be deployed on any existing blockchain that supports smart contracts since the system’s architecture is highly portable.
Blockchain for Securing Healthcare Data Using Squirrel Search Optimization Algorithm

The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients' medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situation, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transaction size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies.
CyCMIS: Cycle-consistent Cross-domain Medical Image Segmentation via diverse image augmentation

Domain shift, a phenomenon when there exists distribution discrepancy between training dataset (source domain) and test dataset (target domain), is very common in practical applications and may cause significant performance degradation, which hinders the effective deployment of deep learning models to clinical settings. Adaptation algorithms to improve the model generalizability from source domain to target domain has significant practical value. In this paper, we investigate unsupervised domain adaptation (UDA) technique to train a cross-domain segmentation method which is robust to domain shift, and which does not require any annotations on the test domain. To this end, we propose Cycle-consistent Cross-domain Medical Image Segmentation, referred as CyCMIS, integrating online diverse image translation via disentangled representation learning and semantic consistency regularization into one network. Different from learning one-to-one mapping, our method characterizes the complex relationship between domains as many-to-many mapping. A novel diverse inter-domain semantic consistency loss is then proposed to regularize the cross-domain segmentation process. We additionally introduce an intra-domain semantic consistency loss to encourage the segmentation consistency between the original input and the image after cross-cycle reconstruction. We conduct comprehensive experiments on two publicly available datasets to evaluate the effectiveness of the proposed method. Results demonstrate the efficacy of the present approach.
Learning with augmented reality: Impact of dimensionality and spatial abilities

Three-dimensional (3D) representations are often more effective for learning about spatial objects than two-dimensional (2D) representations. In augmented reality (AR), which can include 2D and 3D visualizations, learners’ perceptions might differ from other visual media. To examine the specific influence of the dimensionality of AR visualizations on learning the spatial structure of components, 3D and 2D AR representations of the human heart were compared in an experimental laboratory study, otherwise keeping the conditions as comparable as possible. The participants (N = 150) received the respective AR representation and were instructed to look for the hearts’ components mentioned in an informational text. As expected, learning with the 3D compared to the 2D representation resulted in higher germane cognitive load and knowledge about spatial relations of components. Proposed effects on extraneous cognitive load, knowledge about spatial positions of components and mediation effects were not found. Higher mental rotation abilities were found to be more beneficial for learning with the 3D visualization, suggesting that these learners were better equipped for this task and supporting an ability-as-enhancer hypothesis. Overall, the study revealed that even in AR scenarios, 3D visualizations may be better to convey knowledge about spatial structures than 2D visualizations. Moreover, the results emphasize the specific moderating role of spatial abilities when learning with 3D AR material. In future research with various spatial learning material and spatial abilities, the generalizability of these results needs to be examined, so that ultimately more insights can be gained for the design of optimal AR learning experiences.
Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving

Feature selection is a significant preprocessing technique that involves discarding redundant and irrelevant features, so as to reduce the data dimensionality and build a better understanding of data. Pruning superfluous features tend to build better generalization models while improving the computation efficiency extremely. In practices, obtaining the labels of data is time-consuming and labor-intensive, which brings great challenges for feature selection. In this paper, we present a novel robust unsupervised feature selection method for unlabeled dimensionality reduction, which obtains feature importance information by predicting cluster labels of data with matrix factorization. The orthogonal constraints on two decomposing matrices facilitate achieving more accurate class labels of clusters so as to select features with highly discrimination power. Then, the local preserving term is integrated into the projected data for selecting features with local retention ability. Independent of that, an alternative iterative algorithm is incorporated into the optimization of ℓ2,1-norm objective function for efficient and robust feature selection. Extensive comparative experiments are carried out on six benchmark datasets to evaluate the performance of the proposed method. The results show that our method outperforms several well-known unsupervised feature selection methods in terms of both clustering accuracy and normalized mutual information.