Reviewerly/exHarmony huggingface dataset:
Multivariate analysis of CO2, H2S and CH4 diffuse degassing and correlation with fault systems in Agua Caliente - Tzitzio, Michoacán, México

The quantitative relationships between soil gas flux anomalies and faults systems in Agua Caliente-Tzitzio geothermal system were analyzed statistically. The main degassing features are related with thermal springs. The data used in the multivariate analysis are geothermal gas fluxes and spatial structural data (i.e., local fault strike). Exploratory Data Analysis (EDA) and Multivariate Analysis of Variance (MANOVA) were implemented, for the first time, to statistically describe the relation between the location of degassing features and the presence of some local fault systems, discriminating the direction of the main discharge channels and pointing to higher permeability faults. The results show that there is a statistically significant correlation between diffuse degassing of CO2 and CH4 and the N-S and E-W fault systems. Conversely, H2S degassing does not show any correlation with the fault systems, which may be related with the higher H2S reactivity compared with that of CO2 and CH4. It is highly recommended to use EDA and MANOVA techniques in order to establish the main degassing directions in geothermal and volcanic areas.
System design and error correction for 300 mm aperture vertical Fizeau spatial-temporal phase-shifting interferometer

With the development of high-power lasers for aerospace, electronics, etc., the demand for large-aperture planar optical elements has become more urgent, along with the demand for measurement methods. In this paper, the design of a 300 mm aperture vertical Fizeau spatial-temporal phase-shifting interferometer is discussed. Based on position difference between laser sources, the spatial phase-shifting technique is achieved by generating a laser source array on the focal plane of the collimation lens, and four pairs of coherent beams with different phase shifts are integrated in a vertical Fizeau interference system. Combined with a tunable laser diode, a temporal phase-shifting technique can be realized in any pair of coherent beams through wavelength tuning. The key techniques, which include laser duplication to introduce different phase shifts, conjugate imaging, and separation for interferograms, and assembly for a transmission flat, are demonstrated. The systematic error and position mismatch error of interferograms are eliminated. Comparison experiments are conducted between spatial and temporal phase-shifting techniques. A dynamic water surface is also measured to verify its capacity for detecting dynamic objects.
Proterozoic orogenesis and crustal evolution in the Central Indian Tectonic Zone: current understanding from recent works

In the past few years, a huge amount of geological, geochemical and geochronological data have been generated from the southern margin of the Central Indian Tectonic Zone (CITZ) which gives enough clues for understanding the tectonothermal history and Proterozoic orogenesis of the central Indian craton and its implication for the Proterozoic supercontinent assembly.Relevant works published in last four years (2015-2019), with the background geological and tectonic context, are briefly reviewed here.The outstanding geological controversies are also outlined.
Forecasting of Daily Total Horizontal Solar Radiation Using Grey Wolf Optimizer and Multilayer Perceptron Algorithms

Solar radiation data is an indispensable input for photovoltaic and solar-thermal systems. In this regard, the consistent solar radiation forecasting is a primary task in solar energy applications. In this paper, grey wolf optimizer algorithm is integrated to the multilayer perceptron algorithm in order to forecast the daily total horizontal solar radiation. In the forecasting phase, air temperature, relative humidity and diffuse horizontal solar radiation parameters are evaluated in 3-tupled and 2-tupled input structure. In addition, the accuracy of the hybrid forecasting model developed is also tested on the basis of the sigmoid, sinus and hyperbolic tangent activation functions employed in the multilayer perceptron algorithm. The forecasting results show that grey wolf optimizer-based multilayer perceptron model is appropriate to predict the daily total horizontal solar radiation, efficiently.
Shepherd: a Generic Approach to Automating Website Login

To gauge adoption of web security measures, largescale testing of website security is needed.However, the diversity of modern websites makes a structured approach to testing a daunting task.This is especially a problem with respect to logging in: there are many subtle deviations in the flow of the login process between websites.Current efforts investigating login security typically are semi-automated, requiring manual intervention which does not scale well.Hence, comprehensive studies of post-login areas have not been possible yet.In this paper, we introduce Shepherd, a generic framework for logging in on websites.Given credentials, it provides a fully automated attempt at logging in.We discuss various design challenges related to automatically identifying login areas, validating correct logins, and detecting incorrect credentials.The tool collects data on successes and failures for each of these.We evaluate Shepherd's capabilities to login on thousands of sites, using unreliable, legitimately crowd-sourced credentials for a random selection from the Alexa Top websites list.Notwithstanding parked domains, invalid credentials, etc., Shepherd was able to automatically log in on 7,113 sites from this set, an order of magnitude beyond previous efforts at automating login.
Augmented Reality for Operator Training on Industrial Workplaces – Comparing the Microsoft HoloLens vs. Small and Big Screen Tactile Devices

The digital revolution towards the industry standard 4.0 offers many ways to improve established methods and processes. In this paper, we report on the lessons learned about the pros and cons of Augmented-Reality-based operator training using the Microsoft HoloLens as compared to small and big screen tactile devices. Together with our industrial partner, we have chosen an encapsulation assembly task as use case. We have enriched the original training material with digital twins of the workplace, animations, videos, and contemporary forms of interaction, all of which made available in an optimised fashion on three different support technologies. Feedback from our testers, and those in charge of designing training courses, is suggesting that notably the HoloLens version of our prototype has the potential not only to replace current training methods, but to go beyond them up to the point where even novices can pass the training autonomously. It thus seems promising to integrate Augmented Reality into training programmes and so to complete the digital chain within the industry life management.
A Standalone FPGA-Based Miner for Lyra2REv2 Cryptocurrencies

Lyra2REv2 is a hashing algorithm that consists of a chain of individual hashing algorithms, and it is used as a proofof-work function in several cryptocurrencies.The most crucial and exotic hashing algorithm in the Lyra2REv2 chain is a specific instance of the general Lyra2 algorithm.This work presents the first hardware implementation of the specific instance of Lyra2 that is used in Lyra2REv2.Several properties of the aforementioned algorithm are exploited in order to optimize the design.In addition, an FPGA-based hardware implementation of a standalone miner for Lyra2REv2 on a Xilinx Multi-Processor System on Chip is presented.The proposed Lyra2REv2 miner is shown to be significantly more energy efficient than both a GPU and a commercially available FPGA-based miner.Finally, we also explain how the simplified Lyra2 and Lyra2REv2 architectures can be modified with minimal effort to also support the recent Lyra2REv3 chained hashing algorithm.
Relating relative Rényi entropies and Wigner-Yanase-Dyson skew information to generalized multiple quantum coherences

Quantum coherence is a crucial resource for quantum information processing. By employing the language of coherence orders largely applied in NMR systems, quantum coherence has been currently addressed in terms of multiple quantum coherences (MQCs). Here we investigate $\ensuremath{\alpha}$-MQCs, a class of multiple quantum coherences which is based on $\ensuremath{\alpha}$-relative purity, an information-theoretic quantifier analogous to quantum fidelity and closely related to R\'enyi relative entropy of order $\ensuremath{\alpha}$. Our framework enables linking $\ensuremath{\alpha}$-MQCs to Wigner-Yanase-Dyson skew information, an asymmetry monotone-finding application in quantum thermodynamics and quantum metrology. Furthermore, we derive a family of bounds on $\ensuremath{\alpha}$-MQCs, particularly showing that $\ensuremath{\alpha}$-MQCs define a lower bound to quantum Fisher information. We illustrate these ideas for quantum systems described by single-qubit states, two-qubit Bell-diagonal states, and a wide class of multiparticle mixed states. Finally, we investigate the time evolution of the $\ensuremath{\alpha}$-MQC spectrum and the overall signal of relative purity by simulating the time-reversal dynamics of a many-body all-to-all Ising Hamiltonian and comment on applications to physical platforms such as NMR systems, trapped ions, and ultracold atoms.
Memetic algorithm for short messaging service spam filter using text normalization and semantic approach

Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier.
Genetic Programming-Based Simultaneous Feature Selection and Imputation for Symbolic Regression with Incomplete Data

Symbolic regression via genetic programming has been used successfully for empirical modeling from given data sets. However, real-world data sets might contain missing values. Although there are different approaches to dealing with incomplete data sets for classification, symbolic regression with missing values has been rarely investigated. Similarly, only a few studies have been conducted on feature selection for symbolic regression, but none of them addresses the incompleteness issue. In this work, a genetic programming-based method for simultaneous imputation and feature selection is developed. This method selects the predictive features for the incomplete features whilst constructing their imputation models. Such models are designed to be suitable for data sets with mixed numerical and categorical features. The performance of the proposed method is compared with state-of-the-art widely used imputation methods from three aspects: the imputation accuracy, the feature selection effectiveness, and the symbolic regression performance.
FUSE: Finding File Upload Bugs via Penetration Testing

An Unrestricted File Upload (UFU) vulnerability is a critical security threat that enables an adversary to upload her choice of a forged file to a target web server.This bug evolves into an Unrestricted Executable File Upload (UEFU) vulnerability when the adversary is able to conduct remote code execution of the uploaded file via triggering its URL.We design and implement FUSE, a penetration testing tool designed to discover UFU and UEFU vulnerabilities in server-side PHP web applications.The goal of FUSE is to generate upload requests; each request becomes an exploit payload that triggers a UFU or UEFU vulnerability.However, this approach entails two technical challenges: (1) it should generate an upload request that bypasses all content-filtering checks present in a target web application; and (2) it should preserve the execution semantic of the resulting uploaded file.We address these technical challenges by mutating standard upload requests with carefully designed mutations that enable the bypassing of content-filtering checks and do not tamper with the execution of uploaded files.FUSE discovered 30 previously unreported UEFU vulnerabilities, including 15 CVEs from 33 real-world web applications, thereby demonstrating its efficacy in finding code execution bugs via file uploads.
BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework

Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the idea that computational simulation of such cognitive functions and brain operations suspected to correspond to them, can help to further discover knowledge about those functions and operations. Specifically, how they might work together. It is also partly premised on the idea that empirical neuroscience research, whether following on from such a simulation or otherwise—as indeed simulation and empirical research are complementary—could help us build better artificially intelligent systems. This is based on the assumption that principles by which the brain seemingly operates, to the extent that it can be understood as computational, should at least be tested as principles for the operation of artificial systems. This paper explores some of the principles of the brain that seem to be responsible for its autonomous, problem-adaptive nature. The brain operating system (BrainOS) explicated here is an introduction to ongoing work aiming to create a robust, integrated model, combining between the connectionist paradigm underlying neural networks and the symbolic paradigm underlying much else of AI. BrainOS is an automatic approach that selects the most appropriate model based on the (a) input at hand, (b) prior experience, i.e. a history of results of prior problem solving attempts and (c) world knowledge (represented in the symbolic way, and used as a means to explain its approach). It is able to accept diverse and mixed input data types, process histories, and objectives, to extract knowledge and infer a situational context. BrainOS is designed to be efficient through its ability to not only choose the most suitable learning model but to effectively calibrate it based on the task at hand.
Prioritization of Software Functional Requirements: A Novel Approach using AHP and Spanning Tree

Requirements prioritization is an important activity conducted during requirements management phase of requirement engineering.Prioritization of requirements is necessary to include certain features in software while exclude unnecessary requirements.Similarly for developers of software project, prioritization of requirements is also important.Prioritization of small size requirements is easy but difficult process when deal with large size requirements due to time complexity of prioritization technique.AHP (Analytic hierarchy process) is an efficient technique which has been used from several years by many authors.AHP is simple in use and yield accurate results but one of difficulty with AHP is too much comparisons that make is difficult to use for large size requirements.In this research study, an improved AHP technique is designed to diminish number of comparisons and time complexity of AHP with spanning tree based approach.Reducing total comparisons with AHP will make it more suitable to prioritize large size software requirements.
Protecting Patient Data Is the New Scope of Practice: A Recommended Cybersecurity Curricula for Healthcare Students to Prepare for this Challenge.

Protection of patient data has become a critical part of the scope of practice of all healthcare professionals. Routine data breaches underscore the importance of training clinical employees in protecting these data. However, beyond exposure to HIPAA regulations, little is done to educate the healthcare student about the risks and vulnerabilities of the online environment as it pertains to health data. Most individuals receive training upon employment, and compliance with regulations and policies is problematic. This article supports the belief that educating the student prior to entering into the profession may result in improved compliance with state and federal regulations and local policies, thus providing better protections to their patients. We propose a curriculum for both undergraduate and graduate healthcare students to prepare them for understanding and complying with institutional policies once they begin their clinical rotations or are hired as employees. This curriculum addresses the roles of information technology and health information management personnel in securing patient data, local and federal legislation, and the limitation of technical security measures. This curriculum should prepare future clinicians to use their own judgment and to better understand the role of intuitional policies in protecting patient data.
Consumers Loyalty Indicator as a Drivers for Satisfaction

This study focuses on understanding the leading indicators of loyalty among food and nourishments shoppers in two Syrian Malls, Cham City Centre and Massa Plaza, both located in Damascus, the capital of Syria. The research uses descriptive approach; the data were collected from a questionnaire distributed to 110 shoppers in the two shopping malls, 100 valid responses divided (50 answers from Cham City Centre, and 50 from Massa Plaza Mall). The data were analyzed using Statistical Package for Social Science to test the hypothesis. The result showed that shopper attitudes toward the store positively related to his fulfilment and loyalty. Also, shoppers call to the store is an indicator of his satisfaction and commitment towards the store. Finally, perceived value is positively related to the four loyalty indicators. The novelty of this article comes from the analysis of the four loyalty indicators as an essential factor for the sustainable customer. Loyal customers are free marketing tools that keep business running and can increase market share.
Cloudde-based Distributed Differential Evolution for Solving Dynamic Optimization Problems

Although evolutionary algorithms (EAs) have been widely applied in static optimization problems (SOPs), it is still a great challenge for EAs to solve dynamic optimization problems (DOPs). This paper proposes a Cloudde-based differential evolution (CDDE) algorithm based on Message Passing Interface (MPI) technology to solve DOPs. During the evolutionary process, different populations are sent to different slave processes to perform mutation and crossover operations independently using different evolution strategies and then return to the master process to apply migration operation under an adaptive probability. Experimental studies were taken on several DOPs generated by the Generalized Dynamic Benchmark Generator (GDBG) which was used in 2009 IEEE Congress on Evolutionary Computation (CEC2009). The simulation result indicates that the proposed algorithm achieves promising performance in a statistical efficient manner.
Incremental Construction for Scalable Component-Based Systems

The availability of smart and intelligent sensors has changed the monitoring, control and maintenance of a conventional and advanced cyber-physical system used in public or private sectors of a society. For example, internet of things (IoT)-based health, agricultural and weather management systems. With the emergence of such sensors, along with the new ways to communicate or coordinate with them, we need to analyze and optimize the system construction processes. In this paper, to address the issue of scalability for bigger and complex systems based on sensors, we redefine an incremental construction process with an emphasis on behavior preservation and study the effectiveness of the use of software component models from the component-based development domain. In this paper, to deal with the issue of scalability, we investigate component-based development approaches with respect to our defined process and propose a taxonomy of component models with respect to component/system behavior. Moreover, based on the outcome of our analysis, we recommend the EX-MAN component model as the most suitable approach. We investigate incremental construction in the context of the three main categories of current component models, namely models where components are: (i) objects, (ii) architectural units and (iii) encapsulated components. Furthermore, to evaluate our defined process and selection of EX-MAN, we designed three examples of systems using our proposed process in EX-MAN component model.
Imprecise Data and Their Impact on Translational Research in Medicine

The medical field expects from big data essentially two main results: the ability to build predictive models and the possibility of applying them to obtain accurate patient risk profiles and/or health trajectories. Note that the paradigm of precision has determined that similar challenges need to be faced in both population and individualized studies, namely the need of assembling, integrating, modeling and interpreting data from a variety of information sources and scales potentially influencing disease from onset to progression. In many cases, data require computational treatment through solutions for otherwise intractable problems. However, as precision medicine remains subject to a substantial amount of data imprecision and lack of translational impact, a revision of methodological inference approaches is needed. Both the relevance and the usefulness of such revision crucially deal with the assimilation of data features dynamically interconnected.
Integrating Flash Cards with Narratives for Mobile Learning of English Vocabulary

The world has seen a rapid development in mobile devices in recent years. Mobile devices have the advantage of immediacy and convenience. Their uses are not restricted to desktops and classrooms. Many assisted learning systems run on mobile devices. These systems give students the opportunity to learn anytime and anywhere. Language learning is one of the key research issues underlying the development of such systems. There are many ways to use mobile devices to teaching English. This includes the use of pictured flash card to learn English vocabulary, a teaching approach that has been shown to be effective. In the current research, an assisted learning system was developed for learning English vocabulary. It combines pictured flash cards with stories. The goal was to see whether the addition of stories to pictured flash cards for learning English vocabulary could have a significant effect on learning motivation, learning achievement, and anxiety. As the result of an associated educational experiment showed, students generally agreed with the use of such a system to help them learn English vocabulary, and they also made use of leisure time to learn. Compared to students using “plain” pictured flash card for learning, students using pictured flash cards with stories had a significantly lower level of anxiety, and they also learn better as far as learning motivation and achievement were concerned.