Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Analysis of Anger and Persuasion: An Empirical Integration of Four Models

Despite the increasing use of anger in persuasive messaging, such as political ads and health campaigns, very little is known about when and how anger affects persuasion. Building on theoretical propositions derived from four theoretical models that have addressed the link between anger and persuasion, the current meta-analysis (k = 55, N = 6,805) finds a weak impact of anger on behavior (r = .15, p = .04) and nonsignificant effects on attitudes (r = -.03, p = .30) and intent (r = .06, p = .13). Yet, a closer look reveals a more complicated reality where positive effects are identified with the presence of strong arguments, relevant anger, and the inclusion of efficacy appeals. Further, the study identifies an interplay between emotional intensity and argument strength, such that argument strength plays an important role only at lower levels of anger. The study concludes by integrating the results proposing three promising areas for future research into anger and persuasion.
Tangent spaces and Gromov-Hausdorff limits of subanalytic spaces

Abstract It is shown that the Gromov-Hausdorff limit of a subanalytic 1-parameter family of compact connected sets (endowed with the inner metric) exists. If the family is semialgebraic, then the limit space can be identified with a semialgebraic set over some real closed field. Different notions of tangent cones (pointed Gromov-Hausdorff limits, blow-ups and Alexandrov cones) for a closed connected subanalytic set are studied and shown to be naturally equivalent. It is shown that geodesics have well-defined Euclidean directions at each point.
Cognitive Bare Bones Particle Swarm Optimisation with Jumps

The 'bare bones' BB formulation of particle swarm optimisation PSO was originally advanced as a model of PSO dynamics. The idea was to model the forces between particles with sampling from a probability distribution in the hope of understanding swarm behaviour with a conceptually simpler particle update rule. 'Bare bones with jumps' BBJ proposes three significant extensions to the BB algorithm: i two social neighbourhoods, ii a tuneable parameter that can advantageously bring the swarm to the 'edge of collapse' and iii a component-by-component probabilistic jump to anywhere in the search space. The purpose of this paper is to investigate the role of jumping within a specific BBJ algorithm, cognitive BBJ cBBJ. After confirming the effectiveness of cBBJ, this paper finds that: jumping in one component only is optimal over the 30 dimensional benchmarks of this study; that a small per particle jump probability of 1/30 works well for these benchmarks; jumps are chiefly beneficial during the early stages of optimisation and finally this work supplies evidence that jumping provides escape from regions surrounding sub-optimal minima.
PORO-VISCO-ELASTIC MODELS IN BIOMECHANICS: SENSITIVITY ANALYSIS

The main goal of this work is to numerically investigate the sensitivity of biomechanical responses of deformable, porous media to applied external loads. Fluid flows through deformable porous media are relevant for many applications in biology, medicine and bioengineering, like perfusion of tissues in the human body, or fluid flows inside cartilages, bones, and engineered tissue scaffolds. Sensitivity analysis provides valuable insights about how robust the system is with respect to changes in parameters and data and reveals which ones are the most influential for the solutions, and could potentially be used as control agents. AMS Subject Classification: 35Q74, 35B44 Received: August 19, 2018 ; Accepted: December 6, 2018 ; Published: December 15, 2018. doi: 10.12732/caa.v23i1. 5 Dynamic Publishers, Inc., Acad. Publishers, Ltd. http://www.acadsol. eu/caa 62 L. BOCIU AND M. NOORMAN
Unsupervised learning of neural network ensembles for image classification

In the field of pattern recognition, the combination of an ensemble of neural networks has been proposed as an approach to the development of high performance image classification systems. However, previous work clearly showed that such image classification systems are effective only if the neural networks forming them make different errors. Therefore, the fundamental need for methods aimed to design ensembles of "error-independent" networks is currently acknowledged. In this paper, an approach to the automatic design of effective neural network ensembles is proposed. Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets. Reported results on the classification of multisensor remote-sensing images show that this approach allows one to design effective neural network ensembles.
Constraint programming for itemset mining

The relationship between constraint-based mining and constraint programming is explored by showing how the typical constraints used in pattern mining can be formulated for use in constraint programming environments. The resulting framework is surprisingly flexible and allows us to combine a wide range of mining constraints in different ways. We implement this approach in off-the-shelf constraint programming systems and evaluate it empirically. The results show that the approach is not only very expressive, but also works well on complex benchmark problems.
Improving MintRoute Protocol at Different Scenarios

Outstanding features such as low-rate and short-range wireless radio communication on a small device often hamper high reliability in wireless sensor networks. Despite of these constraints, high reliability becomes one of the essential requirements since it is highly demanded by more and more network services. To meet this requirement, various approaches have been proposed in each layer. Among those, MintRoute is a well-known network layer approach that develops a new metric based on link quality for selecting path towards the sink. By choosing a link with the highest measured value, the possibility to transmit packets without error becomes high. However, there are still several issues to be mentioned during operations. In this paper, we focus on network stability in MintRoute. We analyze different features which make MintRoute unstable and propose the revised algorithm by developing new metric together with adjusting parent selection procedure at different scenarios. Simulation results and analysis validate the suitability of stabilized time to enhance the reliability of communication.
Robustness Check for Multiple Faults Using Formal Techniques

Feature sizes in VLSI circuits are steadily shrinking. This results in increasing susceptibility to soft errors, e.g. due to environmental radiation. Precautions against soft errors can be taken on all design stages, e.g. the architectural level, algorithmic level, or on the layout level. Whether the final implementation contains flaws or really provides robustness to soft errors remains to be checked. Here, we propose an approach to formally verify the robustness of a circuit with respect to multiple soft errors. We propose a fault model that prunes the exponentially sized space of multiple soft errors and an algorithm that automatically analyzes a given circuit.
FEM simulation of 3D deformable solids: a practitioner's guide to theory, discretization and model reduction

A practical guide to finite-element-method (FEM) simulation of 3D deformable solids reviews essential offline FEM simulation techniques: complex nonlinear materials, invertible treatment of elasticity, and model-reduction techniques for real-time simulation. Simulations of deformable solids are important in many applications in computer graphics, including film special effects, computer games, and virtual surgery. FEM has become a popular method in many applications. Both offline simulation and real-time techniques have matured in computer graphics literature. This course is designed for attendees familiar with numerical simulation in computer graphics who would like to obtain a cohesive picture of the various FEM simulation methods available, their strengths and weaknesses, and their applicability in various simulation scenarios. The course is also a practical implementation guide for the visual-effects developer, offering a very lean yet adequate synopsis of the underlying mathematical theory. The first section introduces FEM deformable-object simulation and its fundamental concepts, such as deformation gradient, strain, stress, and elastic energy, discusses corotational FEM models, isotropic hyperelasticity, and numerical methods such as conjugate gradients and multigrid. The second section presents the state of the art in model reduction techniques for real-time FEM solid simulation. Topics include linear modal analysis, modal warping, subspace simulation, domain decomposition, and which techniques are suitable for which application.
Multi-channel feedforward ANC system combined with noise source separation

In this paper, we examine the effectiveness of Multi-channel feedforward active noise control (ANC) system combined with noise source separation through some simulations. Multi-channel feedforward ANC system can reduce various noise such as the broad-band noise by arranging the reference microphones close to noise sources. However, the performance of ANC system deteriorates when the reference microphones cannot be arranged close to noise sources because each microphone catches undesired noise in addition to desired noise. The proposed system separates noises by using microphone arrays as reference microphones and the separated signals are used as the reference signals of the ANC system. In simulation results, the proposed system can improve the noise reduction performance in case where the ANC system uses two reference microphones and the reference microphones do not need to be arranged close to the noise sources.
Interphase FISH of Chromosome 1 and 13 in Newly Diagnosed Myeloma and the Disease Prognosis

Due to the heterogeneity of the myeloma genomes, it is unlikely to remedy the disease with a standard therapeutic protocol. In principle, all therapies are considered palliative. To achieve the best clinical outcomes, the therapeutic protocols were implemented with high-dose chemotherapies followed by autologous hematopoietic stem cell transplantation as the standard treatments for newly diagnosed myeloma cases. In parallel with the clinical protocols, bone marrow specimens were collected at the diagnosis stage of more than 500 patients. The assessment of the prognostic and predictive impact of cytogenetic anomalies in the tumor cells was carried out using fluorescent in situ hybridization (FISH) procedure to probe chromosome 1q21, 1p13, 13q14, and 13q34 regions. The cytogenetic aberrations in myeloma cells are corroborated with clinical observation for the prognostic significance. The current study revealed that the amplification of 1q21 and deletion of 1p13 chromosome regions are the significant risk factors when considering overall survival and eventfree survival of the patients.
Hierarchical Volumetric Multi-view Stereo Reconstruction of Manifold Surfaces based on Dual Graph Embedding

This paper presents a new volumetric stereo algorithm to reconstruct the 3D shape of an arbitrary object. Our method is based on finding the minimum cut in an octahedral graph structure embedded into the volumetric grid, which establishes a well defined relationship between the integrated photo-consistency function of a region in space and the corresponding edge weights of the embedded graph. This new graph structure allows for a highly efficient hierarchical implementation supporting high volumetric resolutions and large numbers of input images. Furthermore we will show how the resulting cut surface can be directly converted into a consistent, closed and manifold mesh. Hence this work provides a complete multi-view stereo reconstruction pipeline. We demonstrate the robustness and efficiency of our technique by a number of high quality reconstructions of real objects.
Combining a Probabilistic Sampling Technique and Simple Heuristics to Solve the Dynamic Path Planning Problem

Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be very efficient in solving high dimensional problems. Even though several RRT variants have been proposed to tackle the dynamic replanning problem, these methods only perform well in environments with infrequent changes. This paper addresses the dynamic path planning problem by combining simple techniques in a multi-stage probabilistic algorithm. This algorithm uses RRTs as an initial solution, informed local search to fix unfeasible paths and a simple greedy optimizer. The algorithm is capable of recognizing when the local search is stuck, and subsequently restart the RRT. We show that this combination of simple techniques provides better responses to a highly dynamic environment than the dynamic RRT variants.
MulCNeT: Network Management Cloud

Cloud Computing is evolving as a key computing platform for sharing infrastructures and applications with flexible scalable computing processing power. Virtualization is a core technology for enabling cloud sharing ability. The management of cloud core infrastructure is essential to provide cloud computing environments. And the management, monitoring, and configuring of network from the cloud is another key topic in this paper. This paper presents a new management model architecture named MulCNeT(Multi-tenant, Cloud-based, Network Property trusteeship ). A system, which has been developing based on this architecture, is introduced. The architecture is based on cloud model, focusing on monitoring, configuring, and managing network with property management business model.
Analysis of correlated EEG activity during motor imagery for brain-computer interfaces

Overall accuracy of noninvasive brain-computer interfaces (BCIs) based on motor imagery electroencephalography (EEG) is highly dependent on the extraction of features from the oscillation of sensorimotor rhythms (SMRs) during imagination of movements. In this study, we statistically evaluated whole-brain connectivity using the measurement of linear correlation coefficients (CCs) between EEG channel pairs instead of using conventional spectral analysis. We showed distinct patterns of temporal variations of CCs of all channel pairs and significant channel connections for four motor imageries, including left hand, right hand, both feet, and tongue, in two subjects. Contralateral connectivity was observed in the motor imagery of left and right hands, whereas central connectivity was observed in the motor imagery of both feet. Our results suggest to the implementation of the state-of-the-art BCIs based on whole-brain channel connectivity in motor imagery.
A Formal Approach to prevent Attacks on Mobile Agent Systems

One of the most important issues in mobile agent systems is the security aspect. For the purpose of ensuring security, we provide in this paper a formal model for secure mobile agent system. This model supports the specification of numerous security policy types which control the behaviour of system entities and protect them, as far as possible, from attacks that may occur. In order to verify whether a security policy could overcome a given kind of attack, we present a formal approach to this end. Thus, we propose a precise and formal representation of possible attacks that may occur in a mobile agent systems. According to this representation, we define a set of proved theorems which describe under what conditions a policy succeeds to prevent those attacks. The proposed specifications have been formalized using Z notation and checked using the Z-EVES tool.
Architecture-Based Reliability Prediction with the Palladio Component Model

With the increasing importance of reliability in business and industrial software systems, new techniques of architecture-based reliability engineering are becoming an integral part of the development process. These techniques can assist system architects in evaluating the reliability impact of their design decisions. Architecture-based reliability engineering is only effective if the involved reliability models reflect the interaction and usage of software components and their deployment to potentially unreliable hardware. However, existing approaches either neglect individual impact factors on reliability or hard-code them into formal models, which limits their applicability in component-based development processes. This paper introduces a reliability modeling and prediction technique that considers the relevant architectural factors of software systems by explicitly modeling the system usage profile and execution environment and automatically deriving component usage profiles. The technique offers a UML-like modeling notation whose models are automatically transformed into a formal analytical model. Our work builds upon the Palladio Component Model (PCM), employing novel techniques of information propagation and reliability assessment. We validate our technique with sensitivity analyses and simulation in two case studies. The case studies demonstrate effective support of usage profile analysis and architectural configuration ranking, together with the employment of reliability-improving architecture tactics.
Data mining for isotopic mapping of bioarchaeological finds in a central european alpine passage

Isotopic mapping has become an indispensable tool for the assessment of mobility and trade of the past. However, modeling and understanding spatio-temporal isotopic variation is complicated by the small number of available samples, potential mobility of the investigated samples, sample preservation quality, uncertainty of measurements, and so forth. In this work, we use data mining techniques to build an isotopic map (descriptive modeling) and to determine the spatial origin of new samples (predictive modeling). In particular, we propose a clustering-based isotope ratio model and a scoring function for the origin prediction of new samples. Our data was extracted from real animal finds from an Alpine passage that spans three countries (Germany, Austria, and Italy) and comprises a high variety of isotopes and geological characteristics. Our results and evaluation by domain experts show that it is possible to derive a model of the area for both descriptive and predictive purposes.
The Megadiverse Australian Ant Genus Melophorus: Using CO1 Barcoding to Assess Species Richness

Melophorus is an exceptionally diverse ant genus from arid Australia that has received little taxonomic attention, such that just a fraction of its remarkable number of species is described. The Commonwealth Scientific and Industrial Research Organization’s Tropical Ecosystems Research Centre (TERC) in Darwin holds by far the most extensive collection of Melophorus, and as of September 2016 this comprised >850 sorted morphospecies. However, the reliability of such morphospecies is open to question because species delimitation is extremely challenging due to highly generalized morphology and worker polymorphism. Here we use CO1 barcoding of 401 Melophorus specimens from 188 morphospecies in the TERC collection to determine the reliability of morphologically-based species delimitations as a basis for assessing true diversity within the genus. Our CO1 data confirm the extremely challenging nature of morphologically-based species delimitation within Melophorus, and suggest substantially higher diversity than that indicated by morphospecies. We found many cases where combinations of high (>10%) CO1 divergence, polyphyly, sympatric association, and morphological differentiation indicated that single morphospecies represented multiple lineages. Overall, our analysis indicates that the 188 morphospecies barcoded represent at least 225 independent CO1 lineages. We discuss these results in terms of both their limitations and implications for estimating the total number of species in this exceptionally diverse, arid-adapted ant genus.
Investigating multi-label classification for human values

This paper describes the development of a scalable process for people and machines working together to identify sections of text that reflect specific human values. A total of 2,005 sentences from 28 prepared testimonies presented before hearings on Net neutrality were manually annotated for one or more of ten human values using an annotation frame based on experience annotating similar content using the Schwartz Values Inventory. Moderately good agreement suggests that meaningful distinctions can often be drawn by human annotators. Several k-Nearest-Neighbor classifiers were compared in this preliminary study, yielding results that appear promising and that clearly point to productive directions for future work.
Fast image segmentation based on boosted random forests, integral images, and features on demand

The paper addresses the tradeoff between speed and quality of image segmentation typically found in real-time or high-throughput image analysis tasks. We propose a novel approach for high-quality image segmentation based on a rich and high-dimensional feature space and strong classifiers. To enable fast feature extraction in color images, multiple integral images are used. A decision tree based approach based on twostage Random Forest classifiers is utilized to solve several binary as well as multiclass segmentation problems. It is an intrinsic property of the tree based approach, that any decision is based on a small subset of input features only. Hence, analysis of the tree structures enables a sequential feature extraction. Runtime measurements with several real-world datasets show that the approach enables fast high-quality segmentation. Moreover, the approach can be easily used in parallel computation frameworks because calculation of integral images as well as computation of individual decisions can be done separately. Also, the number of base classifiers can be easily adapted to meet a certain time constraint.
Efficiently managing large-scale raster species distribution data in PostgreSQL

Species distribution data play an important role in biodiversity related research, especially in exploring relationships with the environment. In the recent years, both the number of species being explored and the spatial resolution of species distribution data are increasing fast. It is thus imperative to develop database systems that allow users to efficiently query such large-scale data based on spatial and non-spatial (e.g., taxonomic and phylogenetics) criteria. In this paper, we present our approach to building such a system by integrating several components, including a quadtree representation of binary raster data, tree path indexing and query processing in PostgreSQL, and window decomposition techniques for spatial queries. Our unique contribution is in associating species identifiers with intermediate quadtree nodes and query optimization for multiple independent queries after window query decomposition. Our system enables PostgreSQL to support binary raster data without requiring any changes to the database backend and is suitable for managing large-scale species distribution data. Our experiments using 4000+ bird species distribution data related to the Western hemisphere show that the proposed approach in associating species identifiers with quadtree nodes reduces the number of database tuples by more than 1/3 and the average identifiers to be associated with each tuple from 110.6 to 4.8, a significant improvement compared to classic quadtree-based approaches. With respect to query optimization, optimized queries are 6--9.5 times faster than the baseline queries for average query response times and 5.5--8.3 times faster than the baseline queries for maximum query response times for four query window sizes ranging from 0.1 to 5.0 degrees. Our query optimization techniques thus make the system suitable for many interactive applications for querying and exploring species distribution data.