Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative assessment of polymer concrete with different types of resin

Polymer concrete is gaining increased popularity as a new construction material due to its high compressive, tensile and flexural strengths, short curing time, impact resistance, chemical resistance and freeze-thaw durability. It can be used to repair concrete structures, build slabs and beams of small cross sections and sleepers. There is a great potential of using this material in other structural applications as well. Research work in polymer concrete originated from well over couple of decades. In all of these research programs, either polymer concrete or mortar was made using different types and percentages of resin combined with sand, fly ash and coarse aggregate. The samples thus made were tested for the compressive strength, stress-strain relationships, tensile strength, modulus of elasticity and flexural strength. However, there is no common agreement among the research community about the effect of fly ash on these mechanical properties of polymer concrete. Therefore, this paper investigates the available literature and the experimental results from authors' experimental programs in order to generalize the effect of fly ash on the mechanical properties of polymer concrete. The addition of fly ash as filler material, results in a reduction in the amount of resin, and an increment in the compressive strengths and an increase in the modulus of elasticity. Split tensile strength and flexural strength exhibits a decreasing trend with the increasing fly ash content.
A Secure Environment for Untrusted Helper Applications Con ning the Wily Hacker

Many popular programs, such as Netscape, use untrusted helper applications to process data from the network. Unfortunately, the unauthenticated network data they interpret could well have been created by an adversary, and the helper applications are usually too complex to be bug-free. This raises signi cant security concerns. Therefore, it is desirable to create a secure environment to contain untrusted helper applications. We propose to reduce the risk of a security breach by restricting the program's access to the operating system. In particular, we intercept and lter dangerous system calls via the Solaris process tracing facility. This enabled us to build a simple, clean, user-mode implementation of a secure environment for untrusted helper applications. Our implementation has negligible performance impact, and can protect pre-existing applications.
Combining Bagging and Boosting

sampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noise-free data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 sub-classifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.
Implementation of Division and Replication of Data in Cloud

In cloud system the data is outsourced on the cloud, this may create security issues. In this paper we propose Division and Replication of Data in Cloud (DRDC) which can take care of security issues without compromising the performance. In this system, file uploaded by the client is first encrypted then divided into fragments. Then these fragments are replicated over the cloud nodes. Fragmentation and replication is carried out in such a way that each node contains only a single fragment. Thus if any one of the node is intruded by hacker, no significant information is revealed, and thus security is maintained. To further increase the security, nodes are separated by T-coloring graph method. Due to the T-coloring, the effort needed by an attacker to breach the security is increased multiple times. In addition to this, in this paper we compare this system (DRDC) with other methodologies.
Cart-Load-O-Fun : Designing Digital Games for Trams

3 Statement of Authorship . 4 Acknowledgements . 5 Foreword . 6 Cart-Load-O-Fun . 7 Appendix A. List of publications within year of Honours (2012) . 18 Appendix B. Bubble Popper: Considering Body Contact in Games . 19 Appendix C. Bubble Popper: Considering Body Contact as Game Ingredient . 24 Appendix D. Bubble Popper: The Body Contact Experience. 30 Appendix E. Designing Digital Games for Public Transport . 32 Appendix F. Musical Embrace: Using Social Awkwardness as a Game Ingredient . 36 Appendix G. Hanging off a Bar . 42 Appendix H. Future User Research for Exertion Games . 47
multimodal deep learning framework for automatic fake news detection using capsule neural network and

The progressive growth of today’s digital world has made news spread exponentially faster on social media platforms like Twitter, Facebook, and Weibo. Unverified news is often disseminated in the form of multimedia content like text, picture, audio, or video. The dissemination of such false news deceives the public and leads to protests and creates troubles for the public and the government. Hence, it is essential to verify the authenticity of the news at an early stage before sharing it with the public. Earlier fake news detection (FND) approaches combined textual and visual features, but the semantic correlations between words were not addressed and many informative visual features were lost. To address this issue, an automated fake news detection system is proposed, which fuses textual and visual features to create a multimodal feature vector with high information content. The proposed work incorporates the bidirectional encoder representations from transformers (BERT) model to extract the textual features, which preserves the semantic relationships between words. Unlike the convolutional neural network (CNN), the proposed capsule neural network (CapsNet) model captures the most informative visual features from an image. These features are combined to obtain a richer data representation that helps to determine whether the news is fake or real. We investigated the performance of our model against different baselines using two publicly accessible datasets, Politifact and Gossipcop. Our proposed model achieves significantly better classification accuracy of 93% and 92% for the Politifact and Gossipcop datasets, respectively, compared to 84.6% and 85.6% for the SpotFake+ model.
habCluster: Identifying Geographical Boundary among Intraspecific Units Using Community Detection Algorithm in R

Conservation management for a species generally rests on intraspecific units, while identification of their geographic boundaries is necessary for the implementation. Intraspecific units can be discriminated using population genetic methods, yet an analytical approach is still lacking for detecting their geographic boundaries. Here, based on landscape connectivity, we present a geographical boundary delineation method, habCluster, using community detection algorithm. Community detection is an algorithm in graph theory used to identify clusters of highly connected nodes within a network. We assume that the habitat raster cells with better connections tend to form a continuous habitat patch than the others, thus making the range of an intraspecific unit. The method is tested on grey wolf (Canis lupus) habitat in Europe and on giant panda (Ailuropoda melanoleuca) habitat in China. The habitat suitability for grey wolves and giant pandas were evaluated using species distribution modeling. Each cell in the habitat suitability index (HSI) raster is treated as a node and directly connected with its eight neighbor cells. The edge weight between nodes is the distance between the center of them weighted by the average of their HSI values. We implement habCluster using R programming language with inline C++ code to speed up the computing. The geographical clusters detected were compared with the HSI maps for both species and with the nature reserves for giant panda. We found the boundaries of the clusters delineated using habCluster could serve as a good indicator of habitat patches, and they match generally well with nature reserves in the giant panda case. habCluster can provide spatial analysis basis for conservation management plans such as monitoring, translocation and reintroduction, as well as for population structure research.
Vulnerabilidad física de cubiertas de edificaciones de uso de ocupación normal ante caídas de ceniza en la zona de influencia del volcán Galeras

The main focus of this research is a method for assessing the physical vulnerability of building roofs due to overloading of ashfall by eruptions of Galeras volcano. In order to know the possible affectation of roofs and buildings, damage relationships are established for types of roofs based on their typology, distances between supports and state. The vulnerability assessment allows estimating the likely degree of damage of the roof and building. The methodology starts from characterizing the hazard to establish the intensity pattern to analyze the resistance of the exposed element, which for ash, is the load transferred by a thickness accumulated on the roof. Additionally, an inventory of exposed elements was made, defining type of roofs, materials, geometric configuration and state, as well as of the building, which allow to categorize in roof types. Then the resistance is analyzed in order to determine the maximum stress that leads to failure, evaluating also, the impact on the entire building. The probability of damage is estimated using a cumulative distribution function, taking into account the variance of the boundary stress due to factors such as material heterogeneities, element dimensions, constructive effects or ash load distribution according to the wind path, roof style, as well as the moisture present in the ash. The vulnerability assessment in Galeras zone, shows that although light and moderate roofs are more vulnerable to ash loads, the effect of overall damage to the building is less, as opposed to heavy roofs and slabs. This study formulates considerations in the design of building roofs located in adjacent areas to active volcanoes, which could be the basis for the creation of a standard for design and construction of overload ash-resistant roofs.
Investigation of short-term effects of winter cover crops on compaction and total soil carbon in a long-term no-till agricultural system

Implementation of winter cover crops (CC) is a conservation practice aimed at improving soil health and increasing water infiltration. Reduced tillage (e.g., no-till), another, more common conservation practice, shares these aims. While the literature concerning the main effects of CC and reduced tillage is rich, the additive effects of CC and reduced tillage are unclear and understudied—specifically, the adoption of reduced tillage long before (20+ years) CC implementation, which is common in the last half-century. This study assessed two of the potential short-term effects of CC when added in a long-term no-till system: reduction of soil compaction (SC) and addition of soil total carbon (STC). Four field plots in northeast Mississippi were planted with CC while two were utilized as controls having no CC. All plots were under no-till for the last 20+ years. Soil penetration resistance measurements (SC) and soil cores were collected in April of 2017, following two years of CC implementation, from 20 semirandomly selected locations in each plot. Model comparisons were used to explore variation in SC and STC between CC and control plots, and the contributions of covariates to the observed variation. Results of the selected models (model with highest Akaike's weight) indicated there was no difference in SC between CC and control plots; however, slight variation in STC did exist between the two treatment levels (d ± 95% CI = 0.46 ± 0.39, p < 0.05). Cover crops explained ≃0.7% and ≃5.0% of the variation in SC and STC, respectively. Results showed that soil moisture and unidentified field-scale variables (plots were divided between two adjacent fields) explained an additional ≃20% of the variation in SC and ≃13% in STC. The results in this study suggest that the addition of CC in a long-term no-till system helps to conserve STC over a shorter period than previously expected. Further investigation with more control in the experimental design is needed to better understand how the addition of CC to long-term no-till systems may compound benefits, lead to better management, and, potentially, increase adoption by producers.
(Dis)Similarity-Based Correlation Functions on Polar Scales

We observe the methods of constructing (dis)similarity-based correlation functions defined on scales with polarity given by an involutive operation. We consider basic polar scales: binary scales and bipolar scales with more than two grades. Correlation functions are constructed using similarity and dissimilarity functions defined on such scales and satisfying suitable properties.
Mapping neighbourhood typologies for social-ecological urbanism : A spatial experiential analysis of Stockholm

Studies on urban environments often display contradictory evidence regarding social and ecological outcomes, asserting conflicting development trajectories. In this thesis, affordance theory is applied with the aim of developing a method for relating high-precision mapping of urban structural characteristics to inhabitants’ experiences. I analyse neighbourhood scale trade-offs and synergies between residential populations (RP), working populations (WP) and the ecosystem service temperature regulation (TR) in Stockholm municipality. Neighbourhood typology is introduced as an empirical classification of neighbourhoods based on these structural characteristics. I further analyse experiential outcome in different typologies by applying inhabitant experience data (N = 1828) from an online public participatory geographic information system survey. Analyses reveal strong trade-off patterns between populations and TR capacity. No typologies feature a large RP, a large WP and high TR capacity. Positive experiences are more likely in neighbourhoods with high TR capacity and negative experiences are more likely in neighbourhoods with a large WP, while most neighbourhoods are equally well experienced despite differences in services. The thesis concludes that affordance theory provides methodological tools that when combined can close the gap between structural characteristics of the environment and experiential outcome, in turn leading to a better understanding of what constitutes social-ecological urbanism.
AIS Electronic

The management of digital innovations represents a major challenge for organizations, because they evolve in non-linear, reflexive, and distributed ways. Since digital innovations’ logic is so different, the demands regarding institutionalized anchoring of the innovation management function may be substantially different. However, there has been little attention to organizational design’s role aimed at building appropriate structures to accommodate digital innovation management. Drawing on the organizational information-processing theory, we address this gap by proposing a conceptual framework to understand the interplays between organizational design and digital innovations’ logic. Our framework proposes that, depending on their combination with others, organizational design strategies may have either positive or negative effects on digital innovation performance, and that they may appear in configurations that exhibit high and low performance. It further suggests that while the general ability to innovate is context-specific, multiple configurations of strategies may be equally effective in the same setting. Our paper contributes to the field, since it enables us to achieve a theoretically framed understanding of a key contemporary phenomenon and helps us to identify ideal-type configurations of digital innovation management.
Cutting Edge Transcriptome Analysis : It ’ s All about Design

Since DNA microarrays came on the scene nearly 10 years ago, the scientific community has struggled to come to grips with this powerful technology. We have seen an expanding range of chip design and improvements in signal detection and data analysis, the creation of public databases and the Microarray Gene Expression Data Society (http://www.mged.org/), and the promotion of community standards for conducting microarray experiments (Brazma et al., 2002). One question that continues to challenge researchers daring to enter the microarray fray is, how do we get meaningful biological information from a minimum of hybridizations, given the high cost of performing these experiments? Small scale microarray experiments with little or no biological replication or sophisticated statistical analysis have been used successfully to generate hypotheses about gene function that are then tested and verified by independent means (a good example of this is provided in Mele et al., 2003). It is nonetheless increasingly recognized that rigorous experimental design and statistical analysis, together with adequate replication, is critical for studies that seek to draw broad conclusions about the biology of a system (such as plant development and environmental responses) based principally on microarray gene expression data (Meyers et al., 2004). However, the development of statistical tools for microarray analysis has lagged behind the development and application of microarray technology itself, which has made it even more difficult for researchers to determine what constitutes adequate experimental design and analysis. Results from microarray experiments have the typical characteristics of data that calls for statistically based experimental design and analysis; namely, huge amounts of data associated with potentially large amounts of variation from multiple and diverse sources (i.e., there are numerous sources of both technical and biological variation). However, microarray data differs from data associated with most other types of conventional statistical experiments in a critical respect that has challenged statisticians to develop new analytical tools (or to impose new twists on old analytical tools). That is, conventional statistics typically deals with many observations (e.g., thousands) made on relatively few parameters (e.g., several), whereas microarray data deals with relatively few observations
NUMERICAL SIMULATION DYNAMICAL MODEL OF THREE-SPECIES FOOD CHAIN WITH LOTKA-VOLTERRA LINEAR FUNCTIONAL RESPONSE

In this paper, we study an ecological model with a tritrophic food chain with a classical Lotka-Volterra functional response. There are three equilibrium points of the system. In the parameter space, there are passages from instability to stability, which are called Hopf bifurcation points. For the equilibrium point, it is possible to find bifurcation points analytically and to prove that the system has periodic solutions around these points. Furthermore, the dynamical behaviours of this model are investigated. It has been found that the extinction of the predator depends on the prey-predator parameter in the equation. The dynamical behaviour is found to be very sensitive to the parameter values and the initial condition as well as the parameters of the practical life. Computer simulations are carried out to explain the analytical findings.
Results from the Quebec Groundwater Knowledge Acquisition Program

Results from the Quebec Groundwater Knowledge Acquisition Program Marie Larocque* , Vincent Cloutier , Jana Levison and Eric Rosa Département des sciences de la Terre et de l’atmosphère, Université du Québec à Montréal, Montréal, Canada; Centre de recherche GEOTOP, Université du Québec à Montréal, Montréal, Canada; Groupe de recherche sur l’eau souterraine, Institut de Recherche en Mines et en Environnement, Université du Québec en Abitibi-Témiscamingue, Amos, Canada; School of Engineering, University of Guelph, Guelph, Canada
Extending the Multi-Criteria Decision Making Method

The purpose of this work is to propose a plan for future research and development of the qualitative decision support method DEX. DEX is a qualitative multi-attribute modelling method used to evaluate and analyse multiple decision alternatives in order to select the best alternative. We propose six extensions to DEX: supporting full hierarchies, using numeric attributes, probabilistic and fuzzy evaluations, general aggregation functions, modularization and using relational models. These will be implemented in a new decision support platform.
Matrix Inversion and Subset Selection (MISS): A pipeline for mapping of diverse cell types across the murine brain

Significance The current state-of-the-art mappings of cell types fall short regarding finely resolved subtypes of neural cells, especially γ-aminobutyric acidergic and glutamatergic subtypes. Most such maps compromise on either the number or specificity of unique cell types quantified in each study. Others only use qualitative validation for their maps and fail to address whether gene subset selection is necessary for optimal maps. The Matrix Inversion and Subset Selection pipeline uses publicly available in situ hybridization and single-cell RNA sequencing gene expression data to infer cell-type distributions to map diverse cell types across the murine brain. Most importantly, we demonstrate that data-driven feature selection is necessary to arrive at quantitatively optimal cell-type maps using inversion-, deconvolution-, and correlation-based mapping approaches.
Knowledge-Based Jobs and the Boundaries of Firms Agent-based simulation of Firms Learning and Workforce Skill Set Dynamics 1

The article explores emergence and survival of human resource management strategies and organisational types in a knowledge-based job market. The analysis considers a dynamic environment in which skill requirements change rapidly. We built an agentbased model to simulate a market where firms post job offers to fill vacancies and decide how to select and reward employees; employees, bearing skills, select firms comparing job offers. Taking an evolutionary approach, we explore how hiring strategies, which guarantee survival, emerge from interconnected variation, selection and retention processes. The simulation experiments suggest that, as the rate of change of the environment increases, long-term employment and firm-specific knowledge building emerge as the survival strategy.
Applying Wavelet Transform to Ferroresonance Detection and Protection

Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method. Keywords—Ferroresonance, Wavelet Transform, Intelligent Electronic Device, Transformer.
Computing education for sustainability

This paper presents policy on Computing Education for Sustainability for adoption by SIGCSE. The paper describes results from a survey of Computing Educators who attended ITiCSE 2008 where such a policy statement was mooted. This survey also provides a comparison of understandings of sustainability held by those computing educators against recognized benchmarks. From these findings and understandings an action plan to integrate Education for Sustainability into computing education is proposed.