Miracle-dz/UniFAR-FTUs huggingface dataset:
TR 12-023 Information lifetime aware analysis for dynamic social networks

Given a dynamic social network, a discrete time interval, and a bound on the lifetime of a unit of information , the Dynamic INFormation Liaison (DINFL) problem aims to find a set of individuals that includes the most " information relaying " (or central) person for each time in the given interval. This problem is important for several societal applications related to viral marketing, word-of-mouth marketing, finding key influences etc. However, DINFL poses both semantic and computational challenges. Semantic challenges requires us to model realistic pathways through which information can flow. This involves modeling semantics of information flow such as bounded lifetime of information (i.e. discussions on a topic last only finite amount of time). On the other hand, computational challenges arise due the inherent non-stationarity within a dynamic social network. Consequently, centrality of individuals in the network change with time. Related work in this area has been limited due to two reasons. First, existing computational methods to solve DINFL are likely to incur exorbitant computational costs as they would have to compute the centrality value of each time instant in the given time interval to ensure correctness. Second, they lack adequate models to capture the semantics of information flow, such lifetime of information. In contrast, this paper proposes a novel centrality metric called, Information lifetime aware (or InLife) betweenness centrality, which considers the semantic issues of information flow. A detailed case study shows how our metric compares with the traditional notion of betweenness centrality. We also propose novel flow epoch computational technique that allows us to prune certain time intervals where centrality would not change. This helps us to compute the value of metric over large dynamic social networks spread over long periods of time. We prove our computational technique is correct and complete and provide experimental results showing it outperforms an alternative method by orders of magnitude.
Vlogging campus community stories

Student voice is a powerful signifier for sharing the institutional habitus of a campus. With our new Caboolture Campus Community Stories initiative, we place students in the role of vloggers (video bloggers) to capture and distribute the stories, activities and events of the QUT environment. These stories present visual narratives through the eyes of students about university experience, academic practice and the transition from High School to first year, all intending to promote a sense of community and belonging, normalize academic practices and build an inclusive institutional habitus. These stories are placed on community websites and digital signage around campus as resources for first year students and prospective students.
Declarative Approximate Graph Matching Using a Constraint Approach

Graph pattern matching is a central application in many fields. However, in many cases, the structure of the pattern can only be approximated and exact matching is then far too accurate. This work aims at proposing a CSP approach for approximate subgraph matching where the potential approximation is declaratively included in the pattern graph as optional nodes and forbidden edges. The model, covering both monomorphism and isomorphism problem, also allows additional properties, such as distance properties, between pairs of nodes in the pattern graph. Such properties can be either stated by the user, or automatically inferred by the system. The model is built through the definition of parametric morphism constraints, allowing an efficient implementation of propagators. An Oz/Mozart implementation has been developped. Experimental results show that our general framework is competitive with a specialized C++ Ullman (exact) matching algorithm, while also offering approximate matching.
ANN queries: covering Voronoi diagram with hyperboxes

Given a set $S$ of $n$ points in $d$-dimensional Euclidean metric space $X$ and a small positive real number $\epsilon$, we present an algorithm to preprocess $S$ and answer queries that require finding a set $S' \subseteq S$ of $\epsilon$-approximate nearest neighbors (ANNs) to a given query point $q \in X$. The following are the characteristics of points belonging to set $S'$: - $\forall s \in S'$, $\exists$ a point $p \in X$ such that $|pq| \le \epsilon$ and the nearest neighbor of $p$ is $s$, and - $\exists$ a $s' \in S'$ such that $s'$ is a nearest neighbor of $q$. During the preprocessing phase, from the Voronoi diagram of $S$ we construct a set of box trees of size $O(4^d\frac{V}{\delta}(\frac{\pi}{\epsilon})^{d-1})$ which facilitate in querying ANNs of any input query point in $O(\frac{1}{d}lg \frac{V}{\delta} + (\frac{\pi}{\epsilon})^{d-1})$ time. Here $\delta$ equals to $(\frac{\epsilon}{2\sqrt{d}})^d$, and $V$ is the volume of a large bounding box that contains all the points of set $S$. The average case cardinality of $S'$ is shown to rely on $S$ and $\epsilon$.
Research on visual odometry and terrain reconstruction for quadruped robot

The paper proposes a set of visual odometry and terrain reconstruction algorithms for quadruped robot in unstructured environments. Visual odometry can accurately measure the change value of the robot body's 6 degrees of freedom in 3D space. Terrain reconstruction can aid the robot to determine foot location around obstacles. With the visual odometry, the robot can remember the obstacle position even if the obstacles are out of vision. In order to simplify the process, two parts will also use the same sparse stereo visual image processing methods. The detail of the algorithms and the implementation process are introduced briefly. At last, the experiment results show that the algorithms are accurate and robust for quadruped robot navigation.
Spatial Smoothness and Image Analysis in Statistical Brain Mapping for functional Magnetic Resonance (fMRI) and Positron Emission Tomography (PET)

A considerable effort has been placed into developing image analysis for the spatial statistical tools for evaluating positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. The goal for these statistical brain mapping tools is to create a standardized pipe-line for the analysis. However, this pipeline has many factors that are interrelated, vary between sites, and are undergoing continued development. As an example of an interrelated process that varies between scanning sites and experiments is the image interpolation component in image registration used for movement correction. However, this alters the final results through a change in the spatial smoothness of the data. We review spatial smoothness and other overall analysis issues of fMRI and PET data in addition to illustrating this interrelated processing step in subvoxel image registration
Real time prediction of unoccupied parking space using time series model

Parking guidance system is an important mean to alleviate status quo of urban static traffic, improve the level of city traffic management and protect the urban environment. Timely and accurate information of remaining berths plays an important role in the parking guidance system which guides the driver to find a parking space efficiently. Therefore, this paper focused on the prediction methods of the unoccupied parking space. Then ARIMA model was selected to forecast the unoccupied parking space. And residual berths forecast model was established based on the general process of ARIMA model. At last, the paper combined the actual data to test the accuracy of forecast and compared with the effect of neural network prediction. Thus, the effectiveness and applicability of ARIMA model to predict residual berths were verified.
Event strictness for components with complex bindings

Component Based Software Engineering has been investigated to improve software modularity. Various component models exist, but improvements are still needed to provide architecture validation. In this paper, we present a hierarchical component model using explicit protocols defined with the Symbolic Transition System (STS) formalism. This model is based on an N-party rendezvous and provides three new kinds of bindings. We formalise our model and define two orthogonal properties: behavioural compatibility and event strictness. We also present preliminary results about the check of these properties.
A User Experience Perspective of Design for Context-Aware Adaption

During the last fifteen years, more and more context-aware devices, like smart shutters and GPS navigator, have been introduced to our daily life. Many of such devices have been developed to improve quality of life, and furthermore, to engage the end users with a richer environment by using multiple sensing technologies, such as GPS, brightness and gravity sensors. Unlike many desktop applications, context-aware devices have usually been used to offer always-on services to end users. In addition to taking advantage of context-awareness, a number of variables involved in highly dynamic environments also bring new challenges to Human-Computer Interaction design for such devices. One important concept of context-awareness is to improve the user experience by utilizing the resources available in the physical environment. Many context-aware devices use automated methods to generate the Quality of Service measurement for alternative reconfiguration when a change in the context occur but is that still competent when subjective issues are considered? This research takes a user experience perspective, which focuses on basic needs fulfillment and user preferences. The aim of this research is to clarify the relationship between users' motivations and preferences within alternative context-aware adaption, in order to help the designers improve the subjective user experience.
An experimental comparison between NMF and LDA for active cross-situational object-word learning

Humans can learn word-object associations from ambiguous data using cross-situational learning and have been shown to be more efficient when actively choosing the learning sample order. Implementing such a capacity in robots has been performed using several models, among which are the latent-topic learning models based on Non-Negative Matrix Factorization and Latent Dirichlet Allocation. We compare these approaches on the same data in a batch and in an incremental learning scenario to analyze their strength and weaknesses and furthermore show that they can be the basis for efficient active learning strategies. The proposed modeling deals with both the referential ambiguity and the noisy linguistic descriptions and is grounding meanings of object's modal features (color and shape) and not only the object identity. The resulting active learning strategy is briefly discussed in comparison with active cross-situational learning of object names performed by humans.
Dependency Parsing on Source Language with Reordering Information in SMT

In statistical machine translation, many translation errors may easily occur especially when the word orders are very different between source language and target language, especially with asymmetric morphological structures. The paper investigates combining a rule-based reordering model with conventional dependency parsing at the source side, which can alleviate both the asymmetry of morphological structures and the word orders between source language and target language. Experiments show that this method can improve the performance of the statistical machine translation system.
Thermodynamics of adiabatic feedback control

We study adaptive control of classical ergodic Hamiltonian systems, where the controlling parameter varies slowly in time and is influenced by the system's state (feedback). An effective adiabatic description is obtained for slow variables of the system. A general limit on the feedback-induced negative-entropy production is uncovered. It relates the quickest negentropy production to fluctuations of the control Hamiltonian. The method deals efficiently with the entropy-information trade-off.
Geometrical Analysis of Inverse Kinematics Solutions and Fuzzy Control of Humanoid Robot Arm under Kinematics Constraints

This paper presents inverse kinematics analysis of a humanoid robot arm under geometric constraints. Given Cartesian position variables without orientation information, all joint values of the humanoid robot arm can be obtained analytically. This method is specially useful for joint control of the human like robot arm structure. Simulation studies are performed to verify the kinematics analysis. The Cartesian trajectory tracking task of the robot arm using a fuzzy controller is conducted to confirm the proposed analysis.
Effect of Lipid Sources with Different Fatty Acid Profiles on Intake, Nutrient Digestion and Ruminal Fermentation of Feedlot Nellore Steers

The present study was conducted to determine the effect of lipid sources with different fatty acid profiles on nutrient digestion and ruminal fermentation. Ten rumen and duodenal fistulated Nellore steers (268 body weight±27 kg) were distributed in a duplicated 5×5 Latin square. Dietary treatments were as follows: without fat (WF), palm oil (PO), linseed oil (LO), protected fat (PF; Lactoplus), and whole soybeans (WS). The roughage feed was corn silage (600 g/kg on a dry matter [DM] basis) plus concentrate (400 g/kg on a DM basis). The higher intake of DM and organic matter (OM) (p<0.001) was found in animals on the diet with PF and WF (around 4.38 and 4.20 kg/d, respectively). Treatments with PO and LO decreased by around 10% the total digestibility of DM and OM (p<0.05). The addition of LO decreased by around 22.3% the neutral detergent fiber digestibility (p = 0.047) compared with other diets. The higher microbial protein synthesis was found in animals on the diet with LO and WS (33 g N/kg OM apparently digested in the rumen; p = 0.040). The highest C18:0 and linolenic acid intakes occurred in animals fed LO (p<0.001), and the highest intake of oleic (p = 0.002) and C16 acids (p = 0.022) occurred with the diets with LO and PF. Diet with PF decreased biohydrogenation extent (p = 0.05) of C18:1 n9,c, C18:2 n6,c, and total unsaturated fatty acids (UFA; around 20%, 7%, and 13%, respectively). The diet with PF and WF increased the concentration of NH3-N (p<0.001); however, the diet did not change volatile fatty acids (p>0.05), such as the molar percentage of acetate, propionate, butyrate and the acetate:propionate ratio. Treatments PO, LO and with WS decreased by around 50% the concentration of protozoa (p<0.001). Diets with some type of protection (PF and WS) decreased the effects of lipid on ruminal fermentation and presented similar outflow of benefit UFA as LO.
A Fault Diagnosis Method for Industrial Gas Turbines Using Bayesian Data Analysis

This paper presents an offline fault diagnosis method for industrial gas turbines in a steady-state. Fault diagnosis plays an important role in the efforts for gas turbine owners to shift from preventive maintenance to predictive maintenance, and consequently to reduce the maintenance cost. Ever since its birth, numerous techniques have been researched in this field, yet none of them is completely better than the others and perfectly solves the problem. Fault diagnosis is a challenging problem because there are numerous fault situations that can possibly happen to a gas turbine, and multiple faults may occur in multiple components of the gas turbine simultaneously. An algorithm tailored to one fault situation may not perform well in other fault situations. A general algorithm that performs well in overall fault situations tends to compromise its accuracy in the individual fault situation. In addition to the issue of generality versus accuracy, another challenging aspect of fault diagnosis is that, data used in diagnosis contain errors. The data is comprised of measurements obtained from gas turbines. Measurements contain random errors and often systematic errors like sensor biases as well. In this paper, to maintain the generality and the accuracy together, multiple Bayesian models tailored to various fault situations are implemented in one hierarchical model. The fault situations include single faults occurring in a component, and multiple faults occurring in more than one component. In addition to faults occurring in the components of a gas turbine, sensor biases are explicitly included in the multiple models so that the magnitude of a bias, if any, can be estimated as well. Results from these multiple Bayesian models are averaged according to how much each model is supported by data. Gibbs sampling is used for the calculation of the Bayesian models. The presented method is applied to fault diagnosis of a gas turbine that is equipped with a faulty compressor and a biased fuel flow sensor. The presented method successfully diagnoses the magnitudes of the compressor fault and the fuel flow sensor bias with limited amount of data. It is also shown that averaging multiple models gives rise to more accurate and less uncertain results than using a single general model. By averaging multiple models, based on various fault situations, fault diagnosis can be general yet accurate. DOI: 10.1115/1.3204508
INTEGRAL observations of PSR J1811−1925 and its associated pulsar wind nebula

We present spectral measurements made in the soft (20–100 keV) ? -ray band of the region containing the composite supernova remnant G11.2-0.3 and its associated pulsar PSR J1811? 1925. Analysis of INTEGRAL/IBIS data allows characterization of the system above 10 keV. The IBIS spectrum is best fitted by a power law having photon index ? = 1.8+0.4? 0.3 and a 20–100 keV flux of 1.5 × 10? 11 erg cm? 2 s? 1 . Analysis of archival Chandra data over different energy bands rules out the supernova shell as the site of the soft ? -ray emission while broadband (1–200 keV) spectral analysis strongly indicates that the INTEGRAL/IBIS photons originate in the central zone of the system which contains both the pulsar and its nebula. The composite X-ray and soft ? -ray spectrum indicates that the pulsar provides around half of the emission seen in the soft ? -ray domain; its spectrum is hard with no sign of a cut off up to at least 80 keV. The other half of the emission above 10 keV comes from the pulsar wind nebula; with a ? = 1.7 its spectrum is softer than that of the pulsar. From the IBIS/ISGRI mosaics we are able to derive 2? upper limits for the 20–100 keV flux from the location of the nearby TeV source HESS J1809? 193 to be 4.8 × 10? 12 erg cm? 2 s? 1 . We have also examined the likelihood of an association between PSR J1811? 1925 and HESS J1809? 193. Although PSR J1811? 1925 is the most energetic pulsar in the region, the only one detected above 10 keV and thus a possible source of energy to fuel the TeV fluxes, there is no morphological evidence to support this pairing, making it an unlikely counterpart.
COMPARISON AND EVALUATION ON METRICS BASED APPROACH FOR DETECTING CODE CLONE

Many techniques were proposed for detecting code clone. In this paper we have compared the metrics based on speed, quality and their cost as these are the most that affects due to code clone. The strength and weakness of some of the metrics based are compared and evaluated. This paper suggests an approach based on metric and the quality goals are accessed by the use of metrics.
Adaptive estimation of error sigma using cumulative density ratio test in RANSAC

One of the most important issues in RANSAC is to estimate error threshold robustly. In this work, we propose an adaptive method for estimating error distribution in RANSAC using a cumulative density ratio test. In the proposed method, we first compute cumulative histogram of residual errors of data and then estimate error distribution by maximizing a similarity measure between the computed cumulative distribution and the standard normal cumulative distribution. The similarity between two cumulative distributions is measured by a cumulative density ratio test. The experimental result confirms the effectiveness of the proposed method.
Efficient Graph Kernels for Textual Entailment Recognition

One of the most important research area in Natural Language Processing concerns the modeling of semantics expressed in text. Since foundational work in Natural Language Understanding has shown that a deep semantic approach is still not feasible, current research is focused on shallow methods combining linguistic models and machine learning techniques. The latter aim at learning semantic models, like those that can detect the entailment between the meaning of two text fragments, by means of training examples described by specific features. These are rather difficult to design since there is no linguistic model that can effectively encode the lexico-syntactic level of a sentence and its corresponding semantic models. Thus, the adopted solution consists in exhaustively describing training examples by means of all possible combinations of sentence words and syntactic information. The latter, typically expressed as parse trees of text fragments, is often encoded in the learning process using graph algorithms. In this paper, we propose a class of graphs, the tripartite directed acyclic graphs (tDAGs), which can be efficiently used to design algorithms for graph kernels for semantic natural language tasks involving sentence pairs. These model the matching between two pairs of syntactic trees in terms of all possible graph fragments. Interestingly, since tDAGs encode the association between identical or similar words (i.e. variables), it can be used to represent and learn first-order rules, i.e. rules describable by first-order logic. We prove that our matching function is a valid kernel and we empirically show that, although its evaluation is still exponential in the worst case, it is extremely efficient and more accurate than the previously proposed kernels.
Beyond Interdependence: The Meshing of the World's Economy and the Earth's Ecology

Jim MacNeill was the principal author of Our Common Future, hailed as `the most important document of the decade on the future of the world'. Beyond Interdependence builds on that report to demonstrate the relationship between the global environment, the world's economy, and the international order. Predicting that environmental and resource depletion will become the primary source of human and interstate conflict in the near future, the authors propose a range of new national security strategies that will lift the `ecological shadow' and alleviate world poverty.
Pedestrian counting in video sequences using optical flow clustering

Existing systems for counting pedestrians in video sequences have the problem that counting accuracy is worse when many pedestrians are present and occlusion occurs frequently. In this paper, we introduce a method of clustering optical flows in video frames to improve the counting accuracy in cases where occlusion occurs. The proposed method counts the number of pedestrians by using pre-learned statistics, based on the strong correlation between the number of optical flow clusters detected by our method and the actual number of pedestrians. We evaluate the accuracy of the proposed method using several video sequences, focusing in particular on the effect of parameters for optical flow clustering. We find that the proposed method improves the counting accuracy by up to 25% as compared with a non-clustering method. We also report that using a clustering threshold of angles less than 1° is effective for enhancing counting accuracy.