Reviewerly/exHarmony huggingface dataset:
Multifaceted infrastructure for self-adaptive IoT systems

Internet of Things (IoT) enables the interaction among objects to provide services to their users. Areas such as eHealth, smart energy, and smart buildings have been benefiting from the IoT potential. However, the development of IoT systems is still complex because it deals with a highly dynamic, volatile, and heterogeneous environment. These characteristics require discovering devices, managing these devices' context, and self-adapt their behavior. : In this work, we propose a self-adaptive IoT infrastructure to support multiple facets, i.e., the contextual discovery of smart objects, the context management, and the self-adaptation process of the development of these systems. : We evaluated the proposed infrastructure by developing a smart building application with and without it. The evaluation focused on four issues: the feasibility of integrating the context management through middleware platforms with adaptation based on workflows in a request/response communication model, the impact of our infrastructure on the development of self-adaptive IoT systems considering cyclomatic complexity and coupling code metrics; the impact of using contextual filters on the orchestrator of self-adaptation; and the impact on the quality of the self-adaptation. : The results suggest that: (i) it is feasible to use the proposed infrastructure in the development of self-adaptive IoT systems; (ii) there is a reduction in the cyclomatic complexity and the coupling with our approach, (iii) there is a considerable decrease in the number of rules evaluated at runtime, (iv) our infrastructure reduces the execution time of the adaptations when using contextual filters, and (v) the self-adaptation process was effective when using the orchestrator of self-adaptations. : With these results, we observed that the proposed multifaceted infrastructure could reduce the complexity related to the development of IoT systems, in addition to optimizing their self-adaptation process.
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation

Recent high scores on pronoun translation using context-aware neural machine translation have suggested that current approaches work well.ContraPro is a notable example of a contrastive challenge set for English→German pronoun translation.The high scores achieved by transformer models may suggest that they are able to effectively model the complicated set of inferences required to carry out pronoun translation.This entails the ability to determine which entities could be referred to, identify which entity a sourcelanguage pronoun refers to (if any), and access the target-language grammatical gender for that entity.We first show through a series of targeted adversarial attacks that in fact current approaches are not able to model all of this information well.Inserting small amounts of distracting information is enough to strongly reduce scores, which should not be the case.We then create a new template test set Contracat, designed to individually assess the ability to handle the specific steps necessary for successful pronoun translation.Our analyses show that current approaches to context-aware nmt rely on a set of surface heuristics, which break down when translations require real reasoning.We also propose an approach for augmenting the training data, with some improvements.
Evolutionary computing for clinical dataset classification using a novel feature selection algorithm

The medical diagnostic decision support system uses machine learning and data mining algorithms to detect and diagnose diseases. Several deaths can be avoided if the diseases are detected and cured in the early stages of infection. Feature selection is a major pre-processing method used to obtain the most significant features, thereby enhancing the data mining model's classification accuracy. This work proposes a new feature selection algorithm to perform feature selection as a multi-objective optimization problem. The minimization of classification error rate and minimization of the feature subset's cardinality are the two contradictory objectives that need to be optimized simultaneously. The proposed work is applied for five clinical datasets such as lung cancer, breast cancer, diabetes, fertility, and immunotherapy and the results are compared with existing techniques based on 6 other datasets. This work converts the real-valued Jaya Optimization Algorithm into binary space. It also handles premature convergence and sensitivity–specificity trade-off. The proposed algorithm's efficiency is assessed and analyzed based on average classification accuracy, sensitivity, specificity, number of features selected, percentage feature selection, and CPU computation time. The proposed algorithm improves the effectiveness of data mining based medical diagnostic decision support system.
Taekwondo Sports Test and Training Data Management Using Blockchain

Data management and distributed transactions are characteristics of the Blockchain. Although the Blockchain has initially been a technology used for bitcoin, the Blockchain has now spread to various sectors, including the sports sector. Taekwondo Indonesia has a plan for Level Up Inspections held in different Cities / Regencies by the Indonesian Taekwondo Branch Management and overseen by the Administrators of the Taekwondo Province of Indonesia in the province. Data management, data access, and limited data transactions between Indonesian Taekwondo Branch Management in an area become an obstacle that needs to be identified. Dapps using the Vexanium public Blockchain is a solution to solve this problem, wherewith the Vexanium Rate Increase data among Indonesian Taekwondo Branch Officers in this area can be seen transparently and becomes a preventative measure in data loss or data manipulation that often occurs. This study discusses to prove the use of Dapps using Vexanium is a Level Up Testing system to be more productive and far more efficient.
An Anomaly Detection Scheme based on LSTM Autoencoder for Energy Management

This paper proposes an anomaly detection scheme based on LSTM autoencoder for energy management, which is to prevent anomaly states before they actually occur. When the prognosis of an anomaly state is detected, the anomaly state can be prevented by taking appropriate measures. However, it is difficult to determine normal and anomalous data, since energy consumption varies greatly depending on weather, time, day of the week and season. Thus, this paper proposes an anomaly detection scheme using LSTM autoencoder to detect a data pattern that deviates from the normal data pattern and to determine it as an anomaly state. Experimental results show that this scheme can discriminate anomaly from the observed multivariate data and can be used to prevent fault and incorrect operation in advance.
Blockchain-based deployment of product-centric information systems

Collecting and utilizing product life-cycle data is both difficult and expensive for products that move between different industrial settings at various points of the product life-cycle. Product-centric approaches that present effective solutions in tightly integrated environments have been problematic to deploy across multiple industries and over longer timespans. Addressing deployment costs, incentives, and governance, this paper explores a blockchain-based approach for the deployment of product-centric information systems. Through explorative design science and systematic combining, the deployment of a permissionless blockchain system for collecting product life-cycle data is conceptualized, demonstrated, and evaluated by experts. The purpose of the blockchain-based solution is to manage product data interactions, to maintain an accurate single state of product information, and to provide an economic incentive structure for the provision and the deployment of the solution. The evaluation by knowledgeable researchers and practitioners identifies the aspects limiting blockchain-based deployment of solutions in the current industrial landscape. Combining theory and practice, the paper lays the foundation for a blockchain-based approach to product information management, placing design priority on inter-industrial and self-sustained deployment.
Gradient Masking of Label Smoothing in Adversarial Robustness

Deep neural networks (DNNs) have achieved impressive results in several image classification tasks.However, these architectures are unstable for adversarial examples (AEs) such as inputs crafted by a hardly perceptible perturbation with the intent of causing neural networks to make errors.AEs must be considered to prevent accidents in areas such as unmanned car driving using visual object detection in Internet of Things (IoT) networks.Gaussian noise with label smoothing or logit squeezing can be used to increase the robustness against AEs in the training of DNNs.However, from a model interpretability aspect, Gaussian noise with label smoothing does not increase the adversarial robustness of the model.To resolve this problem, we tackle the AE instead of measuring the accuracy of the model against AEs.Considering that a robust model shows a small curvature of the loss surface, we propose a metric to measure the strength of the AEs and the robustness of the model.Furthermore, we introduce a method to verify the existence of the obfuscated gradients of the model based on the black-box attack sanity check method.The proposed method enables us to identify a gradient masking problem wherein the model does not provide useful gradients and exploits false defenses.We evaluate our technique against representative adversarially trained models using the CIFAR10, CIFAR100, SVHN, and Restricted ImageNet datasets.Our results show that the performance of some false defense models decreases by up to 32% compared to the previous evaluation metrics.Moreover, our metric reveals that traditional metrics used to measure the robustness of the model may produce false results.
A Hyperbolic Embedding Method for Weighted Networks

Network embedding, which is the task of learning low-dimensional representations of vertices, has attracted increasing attention recently. Evidences have been found that the hidden metric space of many realistic complex networks is hyperbolic. The topology and weight emerge naturally as reflections of the hyperbolic metric property. A common objective of hyperbolic embedding is to maximize the likelihood function of the hyperbolic network model. The difficulty is that the likelihood function is non-concave which is difficult to optimize. In this paper, we propose a hyperbolic embedding method for weighted networks. To prevent the optimization from falling into numerous local optima, initial embedding is obtained by approximation. A proposed gradient algorithm then improves the embedding according to the likelihood function. Experiments on synthetic and real networks show that the proposed method achieves good embedding performance with respect to different quality metrics and applications.
THE USE OF EDUCATION 4.0 TOOLS IN TERTIARY EDUCATION SYSTEM IN SLOVAKIA

This paper examines the use of digital learning tools in a technological higher education institution in Slovakia in the context of Industry 4.0 development. Based on a literature review and empirical findings from interviews with education professionals at higher education institutions, this article discusses the critical issues and challenges in the application of some of the Education 4.0 digital learning tools, namely Building Information Modeling (BIM) technology, augmented reality and digital game environment. In this paper, the use of digital learning tools and the collaboration of teachers and researchers are presented as a promising practice of the Slovak University of Technology in Bratislava. This higher education institution reflects current innovative trends in the Education 4.0 concept, e.g. Building Information Modeling technology and augmented reality methods are used as part of a modular learning system, using sensors in manufacturing, preventive maintenance, simulation, design, and 3D printing. Data collection was realized by web pages analysis including webpages of the faculties and by interviews in a sample of educational professionals. The key challenge for the Slovak education system is the improvement of the interconnection between the education system and the industry.The current effort of the scientists and teachers is to identify future trends, techniques, experiences, and skills needed to succeed in the education associated with the requirements of the Industry 4.0 environment. The paper discusses augmented reality with its benefits in time efficiency, ergonomics, and ecological issues. Specific challenges in Education 4.0 learning tools are analysed considering students’ age, attitude towards digital technologies, previous experiences, and situation awareness. The paper stresses the importance of strategical support of Education 4.0 digital learning tools application in the educational process in Slovakia.
Monte Carlo simulation fused with target distribution modeling via deep reinforcement learning for automatic high-efficiency photon distribution estimation

Particle distribution estimation is an important issue in medical diagnosis. In particular, photon scattering in some medical devices extremely degrades image quality and causes measurement inaccuracy. The Monte Carlo (MC) algorithm is regarded as the most accurate particle estimation approach but is still time-consuming, even with graphic processing unit (GPU) acceleration. The goal of this work is to develop an automatic scatter estimation framework for high-efficiency photon distribution estimation. Specifically, a GPU-based MC simulation initially yields a raw scatter signal with a low photon number to hasten scatter generation. In the proposed method, assume that the scatter signal follows Poisson distribution, where an optimization objective function fused with sparse feature penalty is modeled. Then, an over-relaxation algorithm is deduced mathematically to solve this objective function. For optimizing the parameters in the over-relaxation algorithm, the deep <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mi>Q</mml:mi> </mml:mrow> </mml:math> -network in the deep reinforcement learning scheme is built to intelligently interact with the over-relaxation algorithm to accurately and rapidly estimate a scatter signal with the large range of photon numbers. Experimental results demonstrated that our proposed framework can achieve superior performance with structural similarity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mo form="prefix">&gt;</mml:mo> <mml:mn>0.94</mml:mn> </mml:mrow> </mml:math> , peak signal-to-noise ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m3"> <mml:mrow> <mml:mo form="prefix">&gt;</mml:mo> <mml:mn>26.55</mml:mn> <mml:mtext> </mml:mtext> <mml:mi>dB</mml:mi> </mml:mrow> </mml:math> , and relative absolute error <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m4"> <mml:mrow> <mml:mo form="prefix">&lt;</mml:mo> <mml:mn>5.62</mml:mn> <mml:mi>%</mml:mi> </mml:mrow> </mml:math> , and the lowest computation time for one scatter image generation can be within 2 s.
A-HSG: Neural Attentive Service Recommendation based on High-order Social Graph

With the widespread application of Service-Oriented Architecture, the quantity of web services keeps increasing rapidly over the Internet. Providing personalized service recommendation to users remains to be an important research topic. Recent studies have proved social connections helpful for modeling users' potential preference thus improving the performance of service recommendation. To date, however, one special type of social relation, called high-order social relation, has not been thoroughly studied. In reality, a user's preference may not only be affected by the user's direct neighbors, but also indirect ones. Furthermore, such influences may not remain static in the context of various attentions. To tackle such issues, we have developed a novel neural Attentive network based on High-order Social Graph (A-HSG) toward offering social-aware service recommendation. First, a graph convolution-based, multi-hop propagation module is devised to extract the high-order similarity signals from users' local social networks, and inject them into the users' general representations. Second, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users' specific preference. Extensive experiments over a real-life service dataset show that A-HSG outperforms baseline methods in terms of prediction accuracy.
The Development of a Security Evaluation Model Focused on Information Leakage Protection for Sustainable Growth

This research establishes a security evaluation model from the insider leakage perspective and suggests an objective evaluation measurement. Organizational security risks are fused and compounded both inside and outside the organization. Although multiple security controls are implemented to minimize an organization’s security risk, effective security control requires management to preemptively check the organization’s security level. Existing criteria for evaluating security level are limited to external security risks and have improper limit points for dealing with security risks that are fused and compounded within an organization. The focus of this study is the prevention of technical information leakage. Furthermore, we propose a method for measuring the level at which the objectivity of certain items is secured. We compiled 26 detailed evaluation items, considering the security requirements to prevent technical information leakage. We not only performed suitability, reliability, and factor analyses and statistical validation, but also established a method to measure the security level. This measurement method ensures the effectiveness and objectivity of the evaluation of security level, mitigating the risks of security incidents caused by insiders. The results serve as a reference for organizations when designing security evaluation criteria and automated tools based on our evaluation model for future research.
On Combining Static, Dynamic and Interactive Analysis Security Testing Tools to Improve OWASP Top Ten Security Vulnerability Detection in Web Applications

The design of the techniques and algorithms used by the static, dynamic and interactive security testing tools differ. Therefore, each tool detects to a greater or lesser extent each type of vulnerability for which they are designed for. In addition, their different designs mean that they have different percentages of false positives. In order to take advantage of the possible synergies that different analysis tools types may have, this paper combines several static, dynamic and interactive analysis security testing tools—static white box security analysis (SAST), dynamic black box security analysis (DAST) and interactive white box security analysis (IAST), respectively. The aim is to investigate how to improve the effectiveness of security vulnerability detection while reducing the number of false positives. Specifically, two static, two dynamic and two interactive security analysis tools will be combined to study their behavior using a specific benchmark for OWASP Top Ten security vulnerabilities and taking into account various scenarios of different criticality in terms of the applications analyzed. Finally, this study analyzes and discuss the values of the selected metrics applied to the results for each n-tools combination.
Compressed Gradient Methods With Hessian-Aided Error Compensation

The emergence of big data has caused a dramatic shift in the operating regime for optimization algorithms. The performance bottleneck, which used to be computations, is now often communications. Several gradient compression techniques have been proposed to reduce the communication load at the price of a loss in solution accuracy. Recently, it has been shown how compression errors can be compensated for in the optimization algorithm to improve the solution accuracy. Even though convergence guarantees for error-compensated algorithms have been established, there is very limited theoretical support for quantifying the observed improvements in solution accuracy. In this paper, we show that Hessian-aided error compensation, unlike other existing schemes, avoids accumulation of compression errors on quadratic problems. We also present strong convergence guarantees of Hessian-based error compensation for stochastic gradient descent. Our numerical experiments highlight the benefits of Hessian-based error compensation, and demonstrate that similar convergence improvements are attained when only a diagonal Hessian approximation is used.
Unsupervised Deep Learning Network with Self-Attention Mechanism for Non-Rigid Registration of 3D Brain MR Images

In non-rigid registration for medical imaging analysis, computation is complicated, and the high accuracy and robustness needed for registration are difficult to obtain. Recently, many studies have been conducted for nonrigid registration via unsupervised learning networks. This study proposes a method to improve the performance of this unsupervised learning network approach, through the use of a self-attention mechanism. In this paper, the self-attention mechanism is combined with deep learning networks to identify information of higher importance, among large amounts of data, and thereby solve specific tasks. Furthermore, the proposed method extracts both local and non-local information so that the network can create feature vectors with more information. As a result, the limitation of the existing network is addressed: alignment based solely on the entire silhouette of the brain is mitigated in favor of a network which also learns to perform registration of the parts of the brain that have internal structural characteristics. To the best of our knowledge, this is the first such utilization of the attention mechanism in this unsupervised learning network for non-rigid registration. The proposed attention network performs registration that takes into account the overall characteristics of the data, thus yielding more accurate matching results than those of the existing methods. In particular, matching is achieved with especially high accuracy in the gray matter and cortical ventricle areas, since these areas contain many of the structural features of the brain. The experiment was performed on 3D magnetic resonance images of the brains of 50 people. The measured average dice similarity coefficient after registration was 70.40%, which is an improvement of 17.48% compared to that before registration. This improvement indicates that application of the attention block can further improve the performance by an additional 8.5%, as relative to that without attention block. Ultimately, through implementation of non-rigid registration via the attention block method, the internal structure and overall shape of the brain can be addressed, without additional data input. Additionally, attention blocks have the advantage of being able to easily connect to existing networks without a significant computational overhead. Furthermore, by producing an attention map, the area of the brain around which registration was more performed can be visualized. This approach can be used for non-rigid registration with various types of medical imaging data.