Reviewerly/exHarmony huggingface dataset:
Identifying Phished Website Using Multilayer Perceptron

Phishing is most popular in cybercrimes where a malicious individual or a group of individuals who scam users. The aim of identifying any phished website is to help the users/customers with more secure usage of online transactional websites. The research work focuses on the neural network concept which is implemented to identify phished websites. This concept is proved by multilayer perceptron (MLP)-based classification for 48 features. For result assessment, MLP is compared with other machine learning methods such as random forest, support vector machine (SVM), logistic regression and detected to have a higher accuracy of 96.80%.
Causal Bayesian optimization

This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications and, more generally, in all fields where the goal is to optimize an output metric of a system of interconnected nodes. Our approach combines ideas from causal inference, uncertainty quantification and sequential decision making. In particular, it generalizes Bayesian optimization, which treats the input variables of the objective function as independent, to scenarios where causal information is available. We show how knowing the causal graph significantly improves the ability to reason about optimal decision making strategies decreasing the optimization cost while avoiding suboptimal solutions. We propose a new algorithm called Causal Bayesian Optimization (CBO). CBO automatically balances two trade-offs: the classical exploration-exploitation and the new observation-intervention, which emerges when combining real interventional data with the estimated intervention effects computed via do-calculus. We demonstrate the practical benefits of this method in a synthetic setting and in two real-world applications.
Geometrical aspects and quantum brachistochrone problem for a collection of N spin-s system with long-range Ising-type interaction

Abstract We consider a physical system of N interacting qudits consisting of N spin-s particles coupled via the long-range interaction of Ising-type. We investigate the corresponding dynamics, define the associated quantum state manifold and we give the related Fubini-Study metric. We derive the Gaussian curvature and using the Gauss-Bonnet theorem, we show that the dynamics happen on a two-parametric manifold of spherical topology. We examine the geometrical phase acquired by the system under arbitrary and cyclic evolutions. Further, we study the quantum brachistochrone problem concerning the determination of the smallest possible time to realize a time-optimal evolution. By restricting our study to a two-qubit system under the Ising interaction, a detailed analysis is performed for the Fubini-Study metric, the Gaussian curvature, the geometrical phase and the optimal time in relation with the entanglement of the two qubits.
Applying Monte-Carlo Tree Search in HTN Planning

Search methods are useful in hierarchical task network (HTN) planning to make performance less dependent on the domain knowledge provided, and to minimize plan costs. Here we investigate Monte-Carlo tree search (MCTS) as a new algorithmic alternative in HTN planning. We implement combinations of MCTS with heuristic search in PANDA. We furthermore investigate MCTS in JSHOP, to address lifted (non-grounded) planning, leveraging the fact that, in contrast to other search methods, MCTS does not require a grounded task representation. Our new methods yield coverage performance on par with the state of the art, but in addition can effectively minimize plan cost over time.
An Atom-Centric Perspective on Stubborn Sets

Stubborn sets are an optimality-preserving pruning technique for factored state-space search, for example in classical planning. Their applicability is limited by their computational overhead. We describe a new algorithm for computing stubborn sets that is based on the state variables of the state space, while previous algorithms are based on its actions. Typical factored state spaces tend to have far fewer state variables than actions, and therefore our new algorithm is much more efficient than the previous state of the art, making stubborn sets a viable technique in many cases where they previously were not.
Key Assignment Schemes with Authenticated Encryption, revisited

A popular cryptographic option to implement Hierarchical Access Control in organizations is to combine a key assignment scheme with a symmetric encryption scheme. In brief, key assignment associates with each object in the hierarchy a unique symmetric key, and provides all higher-ranked “authorized” subjects with a method to recover it. This setup allows for encrypting the payloads associated with the objects so that they can be accessed by the authorized and remain inaccessible for the unauthorized. Both key assignment and symmetric encryption have been researched for roughly four decades now, and a plethora of efficient constructions have been the result. Surprisingly, a treatment of the joint primitive (key assignment combined with encryption, as used in practice) in the framework of provable security was conducted only very recently, leading to a publication in ToSC 2018(4). We first carefully revisit this publication. We then argue that there are actually two standard use cases for the combined primitive, which also require individual treatment. We correspondingly propose a fresh set of security models and provably secure constructions for each of them. Perhaps surprisingly, the two constructions call for different symmetric encryption primitives: While standard AEAD is the right tool for the one, we identify a less common tool called Encryptment as best fitting the other.
The Impact of Eye Tracking Technology

Eye tracking is a way to measure human eye movements. By using eye tracking technology, the information related to how human eyes are moving from one location to another location and where the person is looking at any given moment can be understood by researchers. For that reason, many cognitive scientists and engineers have already used the eye tracking technology in their studies. The purpose of this is to address how eye tracking technology has been employed in different fields of study and what eye movements measures have been analyzed to develop applications of the eye tracking technology. In this paper, a systematic review of eye tracking studies has been conducted to support other researchers for selecting appropriate eye tracking devices and methods for their studies. Also, the study highlighted the analysis of eye tracking data in various applications.
Artificial neural networks in surgical research

Clinical surgical research typically attempts to find associations among identifiable factors and particular events. Examples of such would be risk factors and a particular outcome (e.g., patient co-morbidities and postoperative complications), or a surgical procedure and clinical result (e.g., lymph node dissection and survival after a cancer operation). Such research is based on direct associations of the presence of these factors with the occurrence of the outcome of interest. For example, clean surgical wounds had an incidence of postoperative infection of 2%, while dirty wounds had an infection rate of 25%. These associations are typically discovered or verified using statistical methods, such as regression for discovery and Chi-square or student’s t-test for verification. The drawback of statistical methods is their requirement for specific data or error distributions, which are often ignored.
Effect of Smart PBL on Meta-cognition, Academic Self-efficacy, and Practice Satisfaction in Nursing Students Taking Fundamental Nursing Skills and Applying Flipped Learning

Purpose: This is a group pretest-posttest design study that aim to examine the effect of smart problem-based learning (PBL) teaching method using flipped learning to meta-cognition, academic self-efficacy, and satisfaction level of nursing students. Method: The subjects were 76 students who took basic nursing practice, which is a major subject, from August 29 to December 13, 2018. A questionnaire was used to measure meta-cognition in web-based self-directed learning. The self-efficacy measurement used a scale consisting of self-regulating efficacy, task difficulty preference, and confidence. The measurement of practical satisfaction is a 5-point Likert scale that employs a questionnaire. Flipped learning uploaded a 10-minute lecture video, and the subjects learned through PC or smartphone. PBL teaching was developed in two modules: gavage and indwelling urinary tract. Results: Result showed a significant improvement of meta-cognition after education than before education (t=3.21, p=.043). Academic self-efficacy was not significantly changed before and after education. Practice satisfaction was significantly improved after training than before training (t=-2.19, p=.032). Conclusion: The smart PBL teaching method using flipped learning is an effective teaching and learning strategy for nursing majors. Nursing students with high meta-cognition will be able to perform well in their learning outcomes; therefore, this positive performance experience will enhance self-efficacy.
Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering

Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semi-supervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and confirmed the outperformance of our clustering approach.
Hybrid Program Recommendation Algorithm Based on Spark MLlib in Big Data Environment

The ratings of TV programs are irregular, and most of the viewers do not score every program they have watched, which leads to uneven distribution of user-program ratings matrix and low sparseness of the matrix in the recommendation system. For the recommendation algorithm, the sparsity of the input matrix has a great impact on the accuracy of the recommendation algorithm. Especially in the face of big data sets, the problems are enlarged. Aiming at these problems, a program recommendation algorithm based on LDA topic model and improved ALS collaborative filtering is proposed. This algorithm combines the program similarity matrix obtained from program features, preprocesses the score matrix to get the prescore matrix as input, and then dynamically weights the user and program features to improve the collaborative filtering algorithm to achieve the effect of “stable recommendation” and “multiple recommendation.” The simulation results show that the parallel operation of Spark MLlib algorithm library not only solves the problem of low timeliness of big data sets but also stabilizes the average RMES of hybrid recommendation algorithm at about 0.52. Compared with the traditional ALS collaborative filtering recommendation algorithm, the effect is significantly improved.
Epilogue – The rhymes, musings and riddles of the International Community of Geological Surveys (ICOGS)

Abstract Building on the present volume, which provides a snapshot of Geological Survey organizations (GSOs) from around the world in 2020, this epilogue provides a retrospective on past efforts to form an international consortium of GSOs. These efforts have had the noble aim of bringing GSOs together to address problems of global scale, but have not fully succeeded in building a sustainable organization. The paper summarizes international discussions held over the past three decades, provides some analysis and makes some suggestions on how a world association of GSOs may become useful, credible and workable.
[Assessment and Spatial Characteristics Analysis of Human Health Risk of Heavy Metals in Cultivated Soil].

Spatial characteristics analysis of the human health risk posed by heavy metals in cultivated soils is of great significance, with the potential to prevent and control soil pollution, protect human health, provide a basis for risk management, etc. In this paper, a methodological system, including a USEPA health risk assessment model, differentiation and factor detector within a geographical detector, and optimized initial model of rank-size theory, was constructed from a geographical perspective. Taking a city in Jiangsu province as the research object, we obtained the spatial differentiation and relative level of human health risk of Cr, Pb, Hg, and Cd in cultivated land by using the methodological system constructed and SPSS and ArcGIS software. The average concentrations of heavy metals (mg·kg-1) in the research area were Cr (65.207 mg·kg-1), Pb (25.486 mg·kg-1), Cd (0.238 mg·kg-1), and Hg (0.045 mg·kg-1), which were lower than the risk control standards for soil contamination of agricultural land in China. The children's non-cancer risk of Cr and Pb and the cancer risk to children and adults of Cr were 2.914385, 1.337503, 4.312679×10-6, and 8.137130×10-6, respectively, all of which exceeded the maximum acceptable limit in the research. Meanwhile, the spatial differentiation (q) of heavy metal health risk was between 0.005523 and 0.204238, which indicated that the high health risk posed by heavy metals should be paid attention to. The health risk rankings (R) of the children's non-cancer risk of Cr and Pb and the cancer risk of Cr in subregions 1, 2, 3, and 4 approached or exceeded 1, and were higher than in subregions 5, 6, and 7, for which R was lower than 0.1. The R values indicated than the high health risk is concentrated in the research region. This research has great significance in measuring the health risk of heavy metals in cultivated soil at different scales, and in forming control strategies with local conditions.
Bayesian Anomaly Detection and Classification for Noisy Data

Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines.
The effects of Physical Activity Intervention in Mobile Healthcare on Obesity, Cardiorespiratory fitness, Metabolic Syndrome Risk factors

This study was investigated that effects of the change of Physical Activity Intervention in Mobile Healthcare on Obesity, Cardiorespiratory fitness, Metabolic Syndrome Risk factors in low physical activity worker : 20 subjects in Self-monitoring group, 20 subjects in Mobile Healthcare group, 20 subjects in control group. Comparisons of obesity factors, Cardiorespiratory fitness, Metabolic Syndrome Risk factors according to changes in 12 week Physical Activity Intervention in Mobile Healthcare, Mobile Healthcare group showed a significant decrease in weight, BMI, fat mass, %body fat and WHR, showed increase in skeletal muscle mass. Self-monitoring group showed a significant decrease in weight, %body WHR. Control group showed increase in weight, BMI, fat mass, %body fat and WHR. Mobile Healthcare group and Self-monitoring group showed a significant increase in VO2max. Mobile Healthcare group showed a significant decrease in SBP, LDL-C, TG showed increase in HDL-C. Self-monitoring group showed a significant decrease in DBP. These results suggest that the changes in 12 week Physical Activity Intervention in Mobile Healthcare have positive effects on Obesity, Cardiorespiratory fitness and Metabolic Syndrome Risk factors suggesting the usefulness of physical activity in daily life.
A Comparison of Augmented and Virtual Reality Features in Industrial Trainings

Short-term qualification for temporary workers is a constant challenge for manufacturing companies. Cycle times of machines often have to be reduced for training processes, which demands time and financial resources. This increases the need for near-the-job trainings without manipulating cycle times of the machine. Digital visualization tools using Mixed Reality (MR) promise opportunities for application-oriented practical training. However, especially for industrial applications, where procedural knowledge has to be transferred, it is not clear which MR technology should be used for which purpose. In order to answer this question, this paper examines the underlying MR-features of the technology. In an experimental setting, the same virtual training for the assembly of a pneumatic cylinder is examined with an augmented reality/augmented virtuality (AR/AV) based application in comparison to a virtual reality (VR) based application. Based on the carried out study, there are significant differences in the evaluation of the system usability, but no differences in the evaluation of the ergonomics and the perceived task load during the task. Out of 16 test persons, 14 would choose the VR system in the final analysis. The results are discussed in the paper and recommendations for the design of MR based systems in an industrial context are given.
Optimizing High-Dimensional Functions with an Efficient Particle Swarm Optimization Algorithm

The optimization of high-dimensional functions is an important problem in both science and engineering. Particle swarm optimization is a technique often used for computing the global optimum of a multivariable function. In this paper, we develop a new particle swarm optimization algorithm that can accurately compute the optimal value of a high-dimensional function. The iteration process of the algorithm is comprised of a number of large iteration steps, where a large iteration step consists of two stages. In the first stage, an expansion procedure is utilized to effectively explore the high-dimensional variable space. In the second stage, the traditional particle swarm optimization algorithm is employed to compute the global optimal value of the function. A translation step is applied to each particle in the swarm after a large iteration step is completed to start a new large iteration step. Based on this technique, the variable space of a function can be extensively explored. Our analysis and testing results on high-dimensional benchmark functions show that this algorithm can achieve optimization results with significantly improved accuracy, compared with traditional particle swarm optimization algorithms and a few other state-of-the-art optimization algorithms based on particle swarm optimization.
Spatio-Temporal Action Instance Segmentation and Localisation

Current state-of-the-art human action recognitionAction recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame. In this work we address the problem of action localisationAction localisation and instance segmentation in which multiple concurrent actions of the same class may be segmented out of an image sequence. We cast the action tubeAction tube extraction as an energy maximisation problem in which configurations of region proposalsRegion proposals in each frame are assigned a cost and the best action tubesAction tube are selected via two passes of dynamic programming. One pass associates region proposalsRegion proposals in space and time for each action categoryAction categories, and another pass is used to solve for the tube’s temporal extent and to enforce a smooth label sequence through the video. In addition, by taking advantage of recent work on action foreground-background segmentation, we are able to associate each tube with class-specific segmentations. We demonstrate the performance of our algorithm on the challenging LIRIS-HARL dataset and achieve a new state-of-the-art result which is 14.3 times better than previous methods.
Towards an Artificial Argumentation System

Computational Argumentation studies the definition of models able to either have a debate, persuade users in decision making or assist humans with argument analysis. In this work, some of our initial contributions and the foundations of this research field are presented.
AC Operation Hardware Learning Neural Circuit Using V-F Converter System

In the artificial intelligence, machine learning and neural network field, many practical use models have been proposed. However, these models are based on a digital Von Neumann model’s computer. There are minority studies in the field of analog learning neural networks. Early analog hardware neural network models were configured with the operational amplifier and the resistance element. It is difficult to change the value of solid resistance for the learning process. In the present paper, proposed is a learning neural network using electronic alternating current (AC) circuits including a voltage-frequency (V-F) converter. These circuits are composed of an amplifier, additional circuit, inverter, subtract circuit, rectifier circuit and V-F converter. The input voltage value was described as the input signal and the input frequency considered the connecting weights. It is easy to change the connecting weights with a V-F converter. Finally, the input frequency converges to a constant value after only several learning process. The learning count time is extremely small. The learning time is quite fast in this AC transmission circuit. The model works using pure analog electronic circuits. The learning time is quite short compare to with a digital process computer.
Benchmarking Data-driven Automatic Text Simplification for German

Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian. For German, no data-driven approach exists to this date, due to a lack of training data. In this paper, we present a parallel corpus of news items in German with corresponding simplifications on two complexity levels. The simplifications have been produced according to a well-documented set of guidelines. We then report on experiments in automatically simplifying the German news items using state-of-the-art neural machine translation techniques. We demonstrate that despite our small parallel corpus, our neural models were able to learn essential features of simplified language, such as lexical substitutions, deletion of less relevant words and phrases, and sentence shortening.