Reviewerly/exHarmony huggingface dataset:
Modeling perspectives of forest growth and yield: Framework of multivariate diffusion process

Today’s approaches to modeling of forest trees and stands are in most cases based on that the models are divided into static regression models describing individual trees and stands variables. This study proposes a general stochastic dynamical model of a forest stand with the object to include random forces governing the dynamic of multivariate distribution of tree (stand) size variables. The dynamic of the multivariate probability density function of tree size components (state variables) is described by a mixed effect parameters Vasicek-type multivariate stochastic differential equation (SDE). The advantages of SDE method are that it do not need to choose many different equations to be tried, it relates the tree size components dynamic against the age dimension (time), and consider the underlying covariance structure driving changes in the tree (stand) size variables. SDE model allows us a better understanding of biological processes driving the dynamic of natural phenomena. The new derived multivariate probability density function and its marginal univariate, bivariate and trivariate distributions, and conditional univariate, bivariate and trivariateprobability density functions can be applied for the modeling of stand attributes such as the mean diameter, height, crown base height, crown width, volume, basal area, slenderness ratio, their increments and much more. This study introduces general multivariate mutual information measures based on the differential entropy to capture multivariate interactions between state variables. The purpose of the present study is therefore to experimentally confirm the effectiveness of using multivariate mutual information measures to reconstruct multivariate interactions in state variables. In this regard, the SDE model was fitted using measurements obtained from permanent experimental pine-dominated stands. The results are implemented in the symbolic algebra system MAPLE.
Decision Trees in Data Stream Mining

A decision tree [1] is a data mining tool commonly used in data classification tasks. Apart from providing satisfactorily high accuracies, the results produced by decision trees are easily interpretable. A decision tree, in fact, divides attribute values space X into disjoint subspaces. The most common decision tree induction algorithms for static data sets are the ID3 algorithm [2], the C4.5 algorithm [3, 4], and the CART algorithm [5].
How VR 360º Impacts the Immersion of the Viewer of Suspense AV Content

Virtual Reality (VR) is increasingly a tempting option for creators seeking more immersive VR audiovisual experiences. In the suspense genre, VR promises to deliver a more impactful experience to the viewers. Nevertheless, it's needed to confirm if such promise is real. This study focused on the creation of a suspense genre content to understand the impact on the immersive experience of the viewer if presented in stereoscopy VR 360º. An evaluation was developed, with a convenience sample of 36 participants. The immersive differences were evaluated when viewing the same audiovisual content of suspense in different formats: VR 360º; 360º; 2D. The results showed that the VR 360º intensifies the perceptual immersion, but diminishes the narrative immersion, a consequence of the 360º.
TLBO with variable weights applied to shop scheduling problems

The teaching–learning-based optimisation (TLBO) algorithm is a population-based metaheuristic inspired on the teaching–learning process observed in a classroom. It has been successfully used in a wide range of applications. In this study, the authors present a variant version of TLBO. In the proposed version, different weights are assigned to students during the student phase, with higher weights being assigned to students with better solutions. Three different approaches to assign weights are investigated. Numerical experiments with benchmark instances of the flow-shop and the job-shop scheduling problems are carried out to investigate the performance of the proposed approaches. They compare the proposed approaches with the original TLBO algorithm and with two variants of TLBOs proposed in the literature in terms of solution quality, convergence speed and simulation time. The results obtained by the application of a Friedman statistical test showed that the proposed approaches outperformed the original version of TLBO in terms of convergence, with no significant losses in the average makespan. The additional simulation time required by the proposed approaches is small. The best performance was achieved with the approach of assigning a fixed weight to half the students with the best solutions and assigning zero to other students.
Co-Channel Multi-Signal Modulation Classification Based on Convolution Neural Network

The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi-signal modulation classification problem. In this paper, we developed a method for co-channel multi-signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.
Sampling the Integers with Low Relative Error

Randomness is an essential part of any secure cryptosystem, but many constructions rely on distributions that are not uniform. This is particularly true for lattice based cryptosystems, which more often than not make use of discrete Gaussian distributions over the integers. For practical purposes it is crucial to evaluate the impact that approximation errors have on the security of a scheme to provide the best possible trade-off between security and performance. Recent years have seen surprising results allowing to use relatively low precision while maintaining high levels of security. A key insight in these results is that sampling a distribution with low relative error can provide very strong security guarantees. Since floating point numbers provide guarantees on the relative approximation error, they seem a suitable tool in this setting, but it is not obvious which sampling algorithms can actually profit from them. While previous works have shown that inversion sampling can be adapted to provide a low relative error (Pöppelmann et al., CHES 2014; Prest, ASIACRYPT 2017), other works have called into question if this is possible for other sampling techniques (Zheng et al., Eprint report 2018/309). In this work, we consider all sampling algorithms that are popular in the cryptographic setting and analyze the relationship of floating point precision and the resulting relative error. We show that all of the algorithms either natively achieve a low relative error or can be adapted to do so.
Can an Online Service Predict Gender? On the State-of-the-Art in Gender Identification from Texts

Gender equality initiatives are often faced with a problem: In order to determine whether initiatives are successful the gender of individuals in the target group must be known. As self-identification inherently has the problems that individuals have to respond and results may, therefore, be biased and incomplete, the temptation to use automated gender identification methods is evident. In the scientific literature, multiple sources ranging from the individual's name, their social media choices, biological features (e.g., brain scans or fingerprints), to texts attributed to the individual are used for automated gender identification with varying success. In this paper, we systematically inspect scientific publications for gender prediction based on textual data which are published between January 2017 and January 2019 in order to determine if such approaches may supply viable means to reliably determine an author's gender. However, we find that the best approach in the current state-of-the-art works with an accuracy of only 93.4%. Moreover, we discuss the possible harm that gender identification systems might entail due to their inaccuracy and also given that they are assuming a binary gender model. We conclude that gender identification based on textual data is currently no reliable substitute for self-identification.
A Winning Team - What Personality Has To Do With Software Engineering

Nowadays, software is developed in teams. But how should teams be put together in order to build a high quality team? This study shows how the different roles in a software development team - project leader, requirements engineer, architect/ designer, and developer/tester/maintainer - should be staffed with respect to HEXACO personality traits in order to form a high quality team. We conducted a qualitative analysis by leading 12 semi-structured interviews with interviewees who work in the software engineering sector and have working experience of at least 2 years. We followed a Grounded Theory approach to derive personality traits and link them to software engineering roles. Our study shows that different personality profiles are indeed beneficial for the different roles. A project leader e.g. should have a more pronounced Agreeableness domain whereas the Extraversion domain should be pronounced with a requirements engineer. Our results should support the composition of software engineering teams with the aim to successfully build high quality teams.
Translating CAMEO verbs for automated coding of event data

The Conflict and Mediation Event Observations (CAMEO) framework is the industry standard for computerized event coding that produces massive data on political conflict at a global scale. However, despite their focus on conflict in foreign locations, most coding efforts have the limitation of exclusively relying on text written in English. This paper advances the frontier of computerized event coding by presenting the CAMEO Verb Translation Application (VTA), a new technology to enable the translation of CAMEO verb dictionaries into non-English languages. CAMEO VTA integrates automated translation with human-in-the-loop features to increase the validity of the translation task. This development opens the possibility of expanding the leverage of CAMEO in a systematic and efficient manner. The first application of this tool focuses on translating CAMEO into Spanish.
A weighted bootstrap approach to logistic regression modelling in identifying risk behaviours associated with sexual activity

The latest population estimates released by Statistics South Africa indicate that 25.03% of all deaths in 2017 in South Africa were AIDS-related. Along with these results, it is also reported that 7.06% of the population were living with HIV, with the HIV-prevalence among youth (aged 15–24) at 4.64% for 2017 (STATSSA. (2018). Retrieved from Statistics South Africa: http://www.statssa.gov.za/publications/P0302/P03022017.pdf). The data used in the study contained information related to the risk-taking behaviours associated with the sexual activity of entering first-year students at the University of the Western Cape. In this study, a logistic regression modelling procedure was carried out on those students that were determined to be sexually active, therefore, in the modelling procedure significant risk behaviours of sexually active first-year students could be identified. Of the 14 variables included in the modelling procedure, six were found to be significantly associated with sexually active students. The significant variables included; the age and race of the student, whether the student had ever taken an HIV test, the importance of religion in influencing the sexual behaviour of the student, whether the student consumed alcohol and lastly whether the student smoked. This study further investigated the impact of introducing sample weighting, bootstrap sampling as well as variable selection methods into the logistic regression modelling procedure. It is shown that incorporating these techniques into the modelling procedure produces logistic regression models that are more accurate and have an increased predictive capability. The bootstrapping procedure is shown to produce logistic regression models that are more accurate than those produced without a bootstrap procedure. A comparison between 200, 500 and 1000 bootstrap samples is also incorporated into the modelling procedure with the models produced from 200 bootstrap samples shown to be just as accurate those produced from 500 or 1000 bootstrap samples. Of the five variable selection methods used, it is shown that the Newton–Raphson and Fisher methods are unreliable in producing logistic regression models. The forward, backward and stepwise variable selection methods are shown to produce very similar results.
Learning user interface design and the development of mobile applications in middle school

HCI education reflects the continual evolution of HCI, embracing the changing landscapes of technology, infrastructure, and technology use. This forum aims to provide a platform for HCI educators, practitioners, researchers, and students to share their perspectives, reflections, and experiences related to HCI education. --- Sukeshini Grandhi, Editor
Analysis of Types, Positioning and Appearance of Visualizations in Online Teaching Environments to Improve Learning Experiences

In this paper we investigate different visualizations of learners’ data related to collaborative online learning in terms of suitability and attractiveness to students. Furthermore, we analyze whether positioning and color appearance of these data visualizations might have an effect on learners’ behavior. To that end, we conducted an online study (n = 120) as well as an eye tracking study (n = 20) to compare different types of visualizations. Results show that students prefer classical data visualizations like bar charts. Visualizations placed in the sidebar of a two column web interface get less attention than visualizations in the header of the main content area. Color schemes do not seem to influence the perception of visualizations. We discuss possible explanations and implications for designing data visualizations in learning environments.
Ordovician strata in the Cliefden Caves area, New South Wales: a case study in the preservation of a globally significant paleontological site

The Cliefden Caves area in central-western New South Wales includes the scientifically most important and irreplaceable examples of fossiliferous Ordovician rocks in the State. Exposures of the stratigraphically lower parts of the Cliefden Caves Limestone Subgroup on the aptly named Fossil Hill are world-famous among paleontologists and internationally significant for preserving the earliest in situ shell beds documented in the literature. They also contain some of the oldest known rugose corals, and an exceptional example of one of the oldest coralline biostromes, as well as many examples of invertebrate fossils and cyanobacterial mat structures that either are unique to this locality or were first described from here. Other stratigraphic levels throughout the total 363 m-thick Cliefden Caves Limestone Subgroup are similarly endowed with highly significant fossils, such as a globally unique in situ shell bank with rare examples of the trimerellide brachiopod Belubula spectacula, a wealth of shelly fossils and trilobites on Dunhill Bluff (adjacent to Fossil Hill to the east), and the appropriately named Trilobite Hill. Less well known to the general public, but of international importance to paleontologists, is the unique deep-water sponge fauna of the overlying Malongulli Formation that occurs at several levels in limestone lenses within this unit. Fossils from the Cliefden Caves Limestone Subgroup and the Malongulli Formation have been documented in more than 60 scientific papers and monographs since paleontological investigations into the site were first published in 1895. Despite concerted scientific endeavour in the region over the past 50 years, much more study needs to be done to fully document the paleontological riches of the Cliefden Caves area. These sites are interpreted as the remains of a tropical island, fringed by limestone and flanked by deep-water environments in which the Malongulli Formation was deposited. Preservation of such islands is exceptionally rare in the geological record. It is therefore vital for the area to remain accessible to scientific researchers to continue their studies. Flooding of the Belubula Valley by a proposed dam downstream from the Cliefden Caves area would hinder future research work on this unique geoheritage resource. Fortunately, a successful public campaign has led to listing of the site on the State Heritage Register that will provide essential protection of the caves from inundation while ensuring continued access to researchers.
Modernizing historical documents: A user Study

Accessibility to historical documents is mostly limited to scholars. This is due to the language barrier inherent in human language and the linguistic properties of these documents. Given a historical document, modernization aims to generate a new version of it, written in the modern version of the document's language. Its goal is to tackle the language barrier, decreasing the comprehension difficulty and making historical documents accessible to a broader audience. In this work, we proposed a new neural machine translation approach that profits from modern documents to enrich its systems. We tested this approach with both automatic and human evaluation, and conducted a user study. Results showed that modernization is successfully reaching its goal, although it still has room for improvement.
Low power millimeter wave radar system for the visually impaired

In order to help visually impaired people to avoid obstacles, a K-band millimetre wave (MMW) radar system based on the principle of frequency-modulated continuous wave (FMCW) is proposed in this study. The FMCW basic principle and the signal processing algorithm are elaborated in detail. The prototype of the radar system has been designed and manufactured. The experiment results show the qualified detection accuracy in view of low deviation. The maximum deviations from the range measurement experiments for a person and a car are 0.14 and 0.47 m, respectively. As a wearable system, the radar system has the characteristics of low power and small size, which is very suitable for blind navigation application. In addition, the MMW radar has broad application prospects in the field of automotive blind spot detection and small robots.
Combining Neural Networks and Global Gabor Features in a Hybrid Face Recognition System

Face recognition is the preferred mode of identification by humans: it is natural, robust and non-intrusive. Face recognition is the most effective and natural technique to identify a person, since it is the same as the way human does and there is no need to use any special equipment. In this paper, a novel face recognition approach is proposed based on Global Facial Features and Neural Networks. The Global Facial Features are extracted using a Gabor wavelet filter; by applying it on the whole image. The face registration is done with the help of Neural Networks and the classification with a nearest-neighbor classifier. The hybrid algorithm was tested on multiple face databases, ORL, Caltech, Yale and Yale B, in order to validate the face recognition rate. The results show that the new face recognition algorithm out-performs the conventional methods such as global Gabor face recognition with PCA in term of recognition rate.
Building Bridges between AI and Cognitive Psychology

My goal in this article is to encourage greater integration of the fields of AI and cognitive psychology by reviewing work on shared interests. I begin with examples that link my early research related to AI with my current efforts to organize knowledge in the cognitive sciences. I then describe how cognitive psychologists have contributed to the methods explained in The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Domingos, 2015), including how these methods can be combined. The final section discusses three benefits of building bridges: using computational models in AI as theoretical models in cognitive psychology, solving joint computational problems, and facilitating the interactions between people and machines.
Improving the quality and quantity of functional and non‐functional requirements obtained during requirements elicitation stage for the development of e‐commerce mobile applications: an alternative reference process model

Many of the software development projects are not successfully finished. In many cases, this is because the software requirements were not correctly identified. One of the factors that affect the elicitation of requirements is the proper selection of requirements elicitation techniques. Even when there is a lot of literature that proposes several requirements elicitation techniques, there is no reference process model that indicates the techniques that must be applied by a requirement's engineer to elicit the requirements of a mobile application for e-commerce. This gap covers both the elicitation of functional and non-functional requirements. Therefore, the authors propose a new model called reference process model for requirements elicitation for e-commerce mobile applications development (RPM-REFEMAD), which allows the elicitation of requirements based on the use of four elicitation techniques combined in ten steps. The proposed model was applied in the requirements elicitation of two e-commerce mobile applications in different scenarios. To visualise the results of the model, a survey was designed and applied to users/stakeholders who participated in the reference process model evaluation. The survey results indicated that RPM-REFEMAD model helps to elicit more quality and quantity functional and non-functional requirements, in comparison with the use of traditional requirements elicitation techniques.
CadaML: A Modeling Language for Multi-Tenant Cloud Application Data Architectures

Multi-tenancy is used for efficient resource utilization when cloud resources are shared across multiple customers. In cloud applications, the data layer is often the prime candidate for multi-tenancy, and usually comprises a combination of different cloud storage solutions such as relational and non-relational databases, and blob storage. Each of these storage types is different, requiring its own partitioning schemes to ensure tenant isolation and scalability. Current multi-tenant data architectures are implemented mainly through manual coding techniques that tend to be time consuming and error prone. As an alternative, we propose a domain-specific modeling language, CadaML, that provides concepts and notations to model a multi-tenant data architecture in an abstract way. CadaML also provides tools to validate the data architecture and automatically produce application code to implement said architecture.