Miracle-dz/UniFAR-FTUs huggingface dataset:
Sketched Interaction Metaphors for Character Animation

Department of Computer Science, University of Auckland, Private Bag 92019, Auckland, New Zealandfksch075, svan071, wyue013g@aucklanduni.ac. nz, burkhard@cs.auckland. ac.nzKeywords: sketch-based interfaces, character animation, skeletal animationAbstract: The use of 3D virtual worlds is increasing rapidly and new tools are necessary to enable untrained users to cre-ate 3D content and interact with it. In this paper we present and evaluate sketch-based interaction metaphorsfor specifying complex animations of 3D skeletally animated models. Sketched interactions include bone ro-tation, motion path translation, sequencing and synchronisation of animations, and an undo-functionality. Thesketches are drawn directly onto a model in a 3D view and are translated into time-dependent joint transfor-mations. We present the design concepts, the implementation of a prototype, and a user study assessing theusability of our animation metaphors. The user study demonstrates that the animation metaphors are intuitive,with the exception of animation ordering. A large variety of animations were successfully created by users.More work is necessary to synchronise animations better. Overall our research demonstrates that sketched-based animations can be useful for applications requiring rapid prototyping containing a limited number ofjoint animations. Examples are the programming of household robots and the creation of simple animatedscenes in education and social network applications.
Image segmentation through combined methods: Watershed transform, unsupervised distance learning and Normalized Cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.
High-order Discontinuous Galerkin Methods for Turbulent High-lift Flows

In this work a robust discontinuous Galerkin (DG) solver for turbulent high-lift aerodynamic flows using the turbulence model of Spalart and Allmaras (SA) is developed. The application of DG discretizations to turbulent RANS flows is one of the most pressing issues facing high-order methods on unstructured grids. The issue is the result of non-smooth behavior of the turbulence model equation, which often causes solver failure for high-order discretizations. Herein a modification to the turbulence model of Spalart and Allmaras is im- plemented within a DG-based unstructured CFD solver for the RANS equations and utilized to compute high-lift aerodynamic flows. Additionally, the convective discretization of the SA turbulence model equation as it relates to solver robustness is discussed at length.
Wireless Access Network Selection Game with Negative Network Externality

Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is to study the rational strategy considering the negative network externality, i.e, the influence of subsequent users' decisions on an individual's throughput due to the limited available resources. In this work, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modelled as a multi-dimensional Markov Decision Process (MDP). A modified value iteration algorithm is proposed to efficiently obtain the optimal decision rule with a simple threshold structure, which enables us to reduce the storage space of the strategy profile. We further investigate the mechanism design problem with incentive compatibility constraints, which enforce the networks to reveal the truthful state information. The formulated problem is a mixed integer programming problem which in general lacks an efficient solution. Exploiting the optimality of substructures, we propose a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multi-network scenario, the proposed algorithm can outperform the heuristic greedy approach in a polynomial-time complexity. Finally, simulation results are shown to validate the analysis and demonstrate the effectiveness of the proposed algorithms.
Evaluating the Mind’s Eye

Can people evaluate phenomenal qualities of internally generated experiences, such as whether a mental image is vivid or detailed? This question exemplifies a problem of metacognition: How well do people know their own thoughts? In the study reported here, participants were instructed to imagine a specific visual pattern and rate its vividness, after which they were presented with an ambiguous rivalry display that consisted of the previously imagined pattern plus an orthogonal pattern. On individual trials, higher ratings of vividness predicted a greater likelihood that the imagined pattern would appear dominant when the participant was subsequently presented with the binocular rivalry display. Off-line self-report questionnaires measuring imagery vividness also predicted individual differences in the strength of imagery bias over the entire study. Perceptual bias due to mental imagery could not be attributed to demand characteristics, as no bias was observed on catch-trial presentations of mock rivalry displays. Our findings provide novel evidence that people have a good metacognitive understanding of their own mental imagery and can reliably evaluate the vividness of single episodes of imagination.
Active Galactic Nuclei

From detailed spectral analysis of a large sample of low-redshift active galactic nuclei (AGNs) selected from the Sloan Digital Sky Survey, we demonstrate— statistically for the first time—that narrow optical Fe II emission lines, both permitted and forbidden, are prevalent in type 1 AGNs. Remarkably, these optical lines are completely absent in type 2 AGNs, across a wide luminosity range, from Seyfert 2 galaxies to type 2 quasars. We suggest that the narrow Fe II-emitting gas is confined to a disk-like geometry in the innermost regions of the narrow-line region on physical scales smaller than the obscuring torus. Subject headings: accretion — galaxies: active — line: formation — line: identification — quasars: emission lines — radiation mechanisms: general Key Laboratory for Research in Galaxies and Cosmology, The University of Sciences and Technology of China, Chinese Academy of Sciences, Hefei, Anhui 230026, China The Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101, USA National Astronomical Observatories/Yunnan Observatory and Key Laboratory of the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650011, China Steward Observatory, The University of Arizona, Tucson, AZ 85721, USA Graduate School of the Chinese Academy of Sciences, 19A Yuquan Road, P.O. Box 3908, Beijing 100039, China
The School Timetabling Problem: A Focus on Elimination of Open Periods and Isolated Classes

Genetic algorithms have successfully been used to solve real timetabling problems. Satisfactory solutions, focused on the elimination of open periods and isolated classes, allowed the "Escola Estadual Padre Rog¿rio Abdala" to process automatization for 2004 and 2005 school terms consecutively. In this paper, a hybrid variation is proposed (memetic algorithm) in order to improve the algorithm robustness. The comparative analysis through statistical methods (t-test and confidence interval) indicates that hybridization increased the robustness and the quality of the results, presenting more appropriate solutions to that real problem.
Active Galactic Nucleus Emission-Line Properties Versus the Eddington Ratio

We analyze UV spectra for a large sample of 578 type 1 active galactic nuclei and derive Eddington ratios, L/LEdd, from the bolometric luminosities and emission-line widths for each object in the sample. The sample spans 5 orders of magnitude in supermassive black hole (SMBH) mass, 7 orders of magnitude in luminosity, and a redshift range 0 ≤ z ≤ 5. We include a sample of 26 low-redshift narrow-line Seyfert 1 galaxies (NLS1's) for comparative analysis. The NLS1's have slightly larger than average L/LEdd ratios (and smaller SMBH masses) for their luminosities, but those L/LEdd values are still substantially below the average for luminous quasars. A large fraction (27%) of the objects overall have L/LEdd > 1, which might be explained by nonspherically symmetric accretion. We find no trend between L/LEdd and either redshift or SMBH mass. Composite spectra sorted by L/LEdd show an unusual emission-line behavior: nearly constant peak heights and decreasing FWHMs with increasing L/LEdd. This is in marked contrast to the emission-line behaviors with luminosity, SMBH mass, and FWHM(C IV), which clearly show trends analogous to the Baldwin effect: decreasing line peaks and equivalent widths with increasing luminosity, SMBH mass, and FWHM. The origins of the unusual behavior with L/LEdd are not understood, but one implication is that metallicity estimates based on emission-line ratios involving nitrogen show no trend with L/LEdd in the composite spectra created from different ranges in L/LEdd. The NLS1 composite, however, shows a slightly high metallicity for its SMBH mass and luminosity. Our earlier work suggests that host galaxy mass, correlated with SMBH mass and AGN luminosity, is the fundamental parameter affecting broad emission line region metallicities. Some secondary effect, not related to L/LEdd, must be enhancing the metallicities in NLS1's.
3-bit Dictator Testing: 1 vs. 5/8

In the conclusion of his monumental paper on optimal inapproximability results, Hastad [13] suggested that Fourier analysis of Dictator (Long Code) Tests may not be universally applicable in the study of CSPs. His main open question was to determine if the technique could resolve the approximability of satisfiable 3-bit constraint satisfaction problems. In particular, he asked if the "Not Two" (NTW) predicate is non-approximable beyond the random assignment threshold of 5/8 on satisfiable instances. Around the same time, Zwick [30] showed that all satisfiable 3-CSPs are 5/8-approximable and conjectured that the 5/8 is optimal. In this work we show that Fourier analysis techniques can produce a Dictator Test based on NTW with completeness 1 and soundness 5/8. Our test's analysis uses the Bonami-Gross-Beckner hypercontractive inequality. We also show a soundness lower bound of 5/8 for all 3-query Dictator Tests with perfect completeness. This lower bound for Property Testing is proved in part via a semidefinite programming algorithm of Zwick [30]. Our work precisely determines the 3-query "Dictatorship Testing gap". Although this represents progress on Zwick's conjecture, current PCP "outer verifier" technology is insufficient to convert our Dictator Test into an NP-hardness-of-approximation result.
Real-time detection of twitter social events from the user's perspective

Over the last 40 years, automatic solutions to analyze text documents collection have been one of the most attractive challenges in the field of information retrieval. More recently, the focus has moved towards dynamic, distributed environments, where documents are continuously created by the users of a virtual community, i.e., the social network. In the case of Twitter, such documents, called tweets, are usually related to events which involve many people in different parts of the world. In this work we present a system for real-time Twitter data analysis which allows to follow a generic event from the user's point of view. The topic detection algorithm we propose is an improved version of the Soft Frequent Pattern Mining algorithm, designed to deal with dynamic environments. In particular, in order to obtain prompt results, the whole Twitter stream is split in dynamic windows whose size depends both on the volume of tweets and time. Moreover, the set of terms we use to query Twitter is progressively refined to include new relevant keywords which point out the emergence of new subtopics or new trends in the main topic. Tests have been performed to evaluate the performance of the framework and experimental results show the effectiveness of our solution.
Precise static type analysis for object oriented programs

A precise static type analysis is important to make available dynamic aspects of object-oriented programs (OOPs) approximately known at compile-time. Many techniques have been proposed for static type analysis depending upon the tradeoff of cost and precision; the techniques may generate spurious possible types for a particular dynamic dispatch which makes the static type analysis imprecise. In this paper, we propose a symbolic execution based type analysis technique that analyzes the dynamic type inter-procedurally by keeping the flow of the program in consideration. We analyze test cases with different class hierarchies. The proposed technique was capable to resolve the target method for most of the dynamic dispatches at reduced computational cost.
Virtual Environments for Mathematics and Geometry Education.

Since ancient times mathematicians and geometricians have used visualisations to describe, discuss, study and teach mathematics. In mathematics education, visualisations are still used whenever possible to support teaching, to inspire students and feed their need to actually see abstract mathematical facts. In our times, virtual reality presents a fascinating, extremely motivating new tool in teachers’ hands which allows students to see mathematics in three dimensions. This article gives an overview of various, mainly immersive, virtual environments that have been developed in the previous 10 years to support mathematics and geometry education. The focus lies on one advanced application for geometry education that has been used and evaluated with over 500 students throughout the years. Findings and teaching experiences are described and discussed.
New measures for the evaluation of interactive information retrieval systems: Normalized task completion time and normalized user effectiveness

User satisfaction, though difficult to measure, is the main goal of Information Retrieval (IR) systems. In recent years, as Interactive Information Retrieval (IIR) systems have become increasingly popular, user effectiveness also has become critical in evaluating IIR systems. However, existing measures in IR evaluation are not particularly suitable for gauging user satisfaction and user effectiveness. In this paper, we propose two new measures to evaluate IIR systems, the Normalized Task Completion Time (NT) and the Normalized User Effectiveness (NUE). The two measures overcome limitations of existing measures and are efficient to calculate in that they do not need a large pool of search tasks. A user study was conducted to investigate the relationships between the two measures and the user satisfaction and effectiveness of a given IR system. The learning effects described by NT, NUE, and the task completion time were also studied and compared. The results show that NT is strongly correlated with user satisfaction, NUE is a better indicator of system effectiveness than task completion time, and both new measures are superior to task completion time in describing the learning effect of the given IR system.
Efficient algorithms for centers and medians in interval and circular‐arc graphs

The p‐center problem is to locate p facilities on a network so as to minimize the largest distance from a demand point to its nearest facility. The p‐median problem is to locate p facilities on a network so as to minimize the average distance from a demand point to its closest facility. We consider these problems when the network can be modeled by an interval or circular‐arc graph whose edges have unit lengths. We provide, given the interval model of an n vertex interval graph, an O(n) time algorithm for the 1‐median problem on the interval graph. We also show how to solve the p‐median problem, for arbitrary p, on an interval graph in O(pn log n) time and on a circular‐arc graph in O(pn2 log n) time. We introduce a spring representation of the objective function and show how to solve the p‐center problem on a circular‐arc graph in O(pn) time, assuming that the arc endpoints are sorted. © 2002 Wiley Periodicals, Inc.
Signature-Based Malware Detection Using Sequences of N-grams

The focus of our study is on one set of malware family known as Brontok worms. These worms have long been a huge burden to most Windows-based user platforms. A prototype of the antivirus was able to scan files and accurately detect any traces of the Brontok malware signatures in the scanned files. In this study, we developed a detection model by extracting the signatures of the Brontok worms and used an n-gram technique to break down the signatures. This process makes the task to remove redundancies between the signatures of the different types of Brontok malware easier. Hence, it was used in this study to accurately differentiate between the signatures of both malicious and normal files. During the experiment, we have successfully detected the presence of Brontok worms while correctly identifying the benign ones. The techniques employed in the experiment provided some insight on creating a good signature-based detector, which could be used to create a more credible solution that eliminates any threats of old malware that may resurface in the future.
Contributions to Theoretical Economics

This paper studies a model of memory. The model takes into account that memory capacity is limited and imperfect. We study how agents with such memory limitations, who have very little information about their choice environment, play games. We introduce the notion of a Limited Memory Equilibrium (LME) and show that play converges to an LME in every generic normal form game. Our characterization of the set of LME suggests that players with limited memory do (weakly) better in games than in decision problems. We also show that agents can do quite well even with severely limited memory, although severe limitations tend to make them behave cautiously.
The compliance testing of software tools with respect to the UML standards specification - The ArgoUML case study

In ICSE'08 we demonstrated the Java UML Lightweight Enumerator (JULE) tool, which supports compliance test generation from modeling standards specifications. When employed in our framework for software tool certification, JULE provides a powerful technology to enumerate a set of test cases that exhaustively test a modeling tool. JULE avoids combinatorial explosion by generating test cases only up to non-isomorphism. In this paper, a case study presented is an experiment on the use of a test suite generated from JULE to assess the compliance of an open source software tool - ArgoUML. This case study illustrates how ArgoUML is tested and reveals some previously unknown non-compliance issues. The case study highlights how software modeling tools can be tested for standards compliance and how test results can be analyzed to diagnose the causes of non-compliance in a software tool.
ON THE PERFORMANCE OF AN APPLICATION LAYER MULTICAST PROTOCOL

In this study, the performance of an application layer multicast protocol, namely Adaptive Overlay Multicast (AOM) protocol was evaluated. In this paper, the authors introduce the concepts of fan-outs and foster limits in building application layer multicast trees that exemplify the performance and adaptability characteristics of AOM and network dynamics with extensive simulation results.
Spectral Clustering with Unbalanced Data

Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $\epsilon$-graphs. This is because the objectives such as Ratio-Cut (RCut) or normalized cut (NCut) attempt to tradeoff cut values with cluster sizes, which are not tailored to unbalanced data. We propose a novel graph partitioning framework, which parameterizes a family of graphs by adaptively modulating node degrees in a $k$-NN graph. We then propose a model selection scheme to choose sizable clusters which are separated by smallest cut values. Our framework is able to adapt to varying levels of unbalancedness of data and can be naturally used for small cluster detection. We theoretically justify our ideas through limit cut analysis. Unsupervised and semi-supervised experiments on synthetic and real data sets demonstrate the superiority of our method.
Evolution of Regulatory Motifs of Bacterial Transcription Factors

Unlike evolution of genes and proteins, evolution of regulatory systems is a relatively new area of research. In particular, little systematic study has been done on evolution of DNA binding motifs in transcription factor families. We suggest an algorithm that reconstructs the most parsimonious scenario for changes in DNA binding motifs along an evolutionary tree of transcription factor binding sites. The algorithm was validated on several artificial datasets and then applied to reconstruct the evolutionary history of the NrdR, MntR, LacI, FNR, Irr, Fur and Rrf2 transcription factor families. The algorithm seems to be sufficiently robust to be applicable in realistic situations. In most transcription factor families the changes in binding motifs are limited to several branches. Changes in consensus nucleotides proceed via an intermediate stage when the respective position is not conserved.
Development and validation of the five-factor LAMBI measure of God representations.

God representations are complex and there is no standard, relatively short, and easy to administer measure reflecting both anthropomorphic and abstract representations of God. We developed a new measure with five dimensions: Limitless, Authoritarian, Mystical, Benevolent, and Ineffable (the LAMBI scale). In Study 1, we used exploratory factor analysis to examine the factor structure of a preliminary list of 41 common adjectives that people might use to describe God. In Study 2, we identified the 25 best-fitting items, using confirmatory factor analysis to show that a five-factor model fit well, and began to validate the new scale using measures of religious commitment, individualistic spirituality, and Quest. In Study 3, we found the scale has good testretest reliability. In Study 4, we examined the contribution of the LAMBI scales in predicting conservatism, values, and beliefs, above and beyond two existing measures of God representations. In Study 5, we used latent profile analysis to identify four response patterns across the five dimensions: Relational, Abstract, Unbelief, and Amorphous (no differences across the five dimensions) and show little relation between these profiles and religious group membership. We conclude that the LAMBI scale assesses important individual differences in thinking about God and can potentially be used to predict beliefs and social attitudes.
Quadratic harnesses, q-commutations, and orthogonal martingale polynomials

We introduce the quadratic harness condition and show that integrable quadratic harnesses have orthogonal martingale polynomials with a three step recurrence that satisfies a -commutation relation. This implies that quadratic harnesses are essentially determined uniquely by five numerical constants. Explicit recurrences for the orthogonal martingale polynomials are derived in several cases of interest.