Miracle-dz/UniFAR-FTUs huggingface dataset:
ViSA: virtual spotlighted advertising

With the constraint of limited intrusiveness, maximizing the advertising efficiency in sports video has been known as a challenging problem. In this paper, we propose a virtual advertisement system with novel advertising strategy, called Virtual Spotlighted Advertising (ViSA), for broadcasting tennis videos. We take psychology, advertising theory, and computational aesthetics into account for improving the effectiveness of advertising. ViSA system automatically detects the candidate insertion points in both temporal and spatial domains and estimates the maximum effective region for message communication. Then, the harmonically re-colored advertisements with foveation model based non-uniform transparency, are projected on the court in tennis videos. The experiments and evaluation results showed the effectiveness of ViSA, for sports video advertising, in terms of recall and recognition.
Structural ICP algorithm for pose estimation based on local features

In this paper we present a new variant of the ICP (iterative closest point) algorithm for finding correspondences between image and model points. This new variant uses structural information from the model points and contour segments detected in images to find better conditioned correspondence sets and to use them to compute the 3D pose. A local representation of 3D free-form contours is used to get the structural information in 3D space and in the image plane. Furthermore, the local structure of free-form contours is combined with orientation and phase as local features obtained from the monogenic signal. With this combination, we achieve a more robust correspondence search. Our approach was tested on synthetical and real data to compare the convergence and performance of our approach against the classical ICP approach.
High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks

In this work we analyze basic properties of Random Apollonian Networks \cite{zhang,zhou}, a popular stochastic model which generates planar graphs with power law properties. Specifically, let $k$ be a constant and $\Delta_1 \geq \Delta_2 \geq . \geq \Delta_k$ be the degrees of the $k$ highest degree vertices. We prove that at time $t$, for any function $f$ with $f(t) \rightarrow +\infty$ as $t \rightarrow +\infty$, $\frac{t^{1/2}}{f(t)} \leq \Delta_1 \leq f(t)t^{1/2}$ and for $i=2,. ,k=O(1)$, $\frac{t^{1/2}}{f(t)} \leq \Delta_i \leq \Delta_{i-1} - \frac{t^{1/2}}{f(t)}$ with high probability (\whp). Then, we show that the $k$ largest eigenvalues of the adjacency matrix of this graph satisfy $\lambda_k = (1\pm o(1))\Delta_k^{1/2}$ \whp. Furthermore, we prove a refined upper bound on the asymptotic growth of the diameter, i.e., that \whp the diameter $d(G_t)$ at time $t$ satisfies $d(G_t) \leq \rho \log{t}$ where $\frac{1}{\rho}=\eta$ is the unique solution greater than 1 of the equation $\eta - 1 - \log{\eta} = \log{3}$. Finally, we investigate other properties of the model.
EKF Based Pose Estimation using Two Back-to-Back Stereo Pairs

In this work, we solve the pose estimation problem for robot motion by placing multiple cameras on the robot. In particular, we use four cameras arranged as two back-to-back stereo pairs combined with the extended Kalman filter (EKF). The reason for using multiple cameras is that the pose estimation problem is more constrained for multiple cameras than for a single camera. Back-to-back cameras are used since they provide more information. Stereo information is used in self initialization and outlier rejection. Different approaches to solve the long-sequence-drift have been suggested. Both the simulations and the real experiments show that our approach is fast, robust, and accurate.
Is the cure worse than the disease? overfitting in automated program repair

Automated program repair has shown promise for reducing the significant manual effort debugging requires. This paper addresses a deficit of earlier evaluations of automated repair techniques caused by repairing programs and evaluating generated patches' correctness using the same set of tests. Since tests are an imperfect metric of program correctness, evaluations of this type do not discriminate between correct patches and patches that overfit the available tests and break untested but desired functionality. This paper evaluates two well-studied repair tools, GenProg and TrpAutoRepair, on a publicly available benchmark of bugs, each with a human-written patch. By evaluating patches using tests independent from those used during repair, we find that the tools are unlikely to improve the proportion of independent tests passed, and that the quality of the patches is proportional to the coverage of the test suite used during repair. For programs that pass most tests, the tools are as likely to break tests as to fix them. However, novice developers also overfit, and automated repair performs no worse than these developers. In addition to overfitting, we measure the effects of test suite coverage, test suite provenance, and starting program quality, as well as the difference in quality between novice-developer-written and tool-generated patches when quality is assessed with a test suite independent from the one used for patch generation.
The Knowledge CORE: A New Model to Challenge the Knowledge Management Field

This paper derives from a research project, which sets out to address practitioner dissatisfaction in the area of Knowledge Management. The author discusses common weaknesses in existing thinking about Knowledge Management and in prevailing models in particular. Modelling processes are considered and underlying assumptions that are required to be addressed in any attempt to create a Knowledge Management Model are examined. An overview is provided of the initial stages in the development of a new, synthetic and general model, The Knowledge CoreiÂ¾©, with assumptions underlying this signposted and their influences upon modelling are discussed.
A Study of the Correlation Between Learning Outcomes and Self-efficacy of Crafts Skills

Self-efficacy, according to Hutchins, is a key factor to affect learners’ getting and maintaining of skills. The purposes of the study are: (1) to explore the correlations between learning outcomes of crafts skill knowledge, forms and self-efficacy; (2) to compare the self-efficacy of different learning outcomes of crafts skill knowledge; (3) to compare the self-efficacy of different learning outcomes of crafts skill forms. The study develops a questionnaire for 347 students of crafts curriculum and uses Pearson Product-Moment Correlation. The conclusions are: (1) there are positive correlations between the learning outcomes of crafts skill knowledge, forms and self-efficacy; (2) the self-efficacy of better learning outcomes regarding skill knowledge is significantly higher than worse learning outcomes; (3) the self-efficacy of better learning outcomes of crafts skill forms is significantly higher than worse learning outcomes. Two suggestions are made. First, instructors of skill curriculum must pay more attentions to self-efficacy of students rather than curriculum development and instructional strategies. Second, instructors must develop effective strategies to boost self-efficacy.
Secure On-Off Transmission Design With Channel Estimation Errors

Physical layer security has recently been regarded as an emerging technique to complement and improve the communication security in future wireless networks. The current research and development in physical layer security are often based on the ideal assumption of perfect channel knowledge or the capability of variable-rate transmissions. In this paper, we study the secure transmission design in more practical scenarios by considering channel estimation errors at the receiver and investigating both fixed-rate and variable-rate transmissions. Assuming quasi-static fading channels, we design secure on-off transmission schemes to maximize the throughput subject to a constraint on secrecy outage probability. For systems with given and fixed encoding rates, we show how the optimal on-off transmission thresholds and the achievable throughput vary with the amount of knowledge on the eavesdropper's channel. In particular, our design covers the interesting case where the eavesdropper also uses the pilots sent from the transmitter to obtain imperfect channel estimation. An interesting observation is that using too much pilot power can harm the throughput of secure transmission if both the legitimate receiver and the eavesdropper have channel estimation errors, while the secure transmission always benefits from increasing pilot power when only the legitimate receiver has channel estimation errors but not the eavesdropper. When the encoding rates are controllable parameters to design, we further derive both a non-adaptive and an adaptive rate transmission schemes by jointly optimizing the encoding rates and the on-off transmission thresholds to maximize the throughput of secure transmissions.
Incremental deployment for traffic engineering in hybrid SDN network

Traffic engineering is a method to balance the flows and optimize the routing in the network. Software defined networking is a new network architecture and we can gain great benefit by migrating the traditional IP network to the SDN-enabled network from the perspective of traffic engineering. However, due to the economical, organizational and technical challenges, migrating to the network with a full deployment of SDN routers is impractical in the short term. It is a desirable choice to deploy SDN incrementally. In this paper, we seek to search for an optimal migration sequence of the legacy routers to SDN-enabled routers so that we can decide where and how many routers to migrate firstly. Our main contribution is that we propose a heuristic algorithm, i.e., genetic algorithm, to seek a migration sequence of the routers that obtains the most of the benefit from the perspective of traffic engineering. We evaluate the algorithm by conducting simulation experiments, making comparison to the greedy migration algorithm and static migration algorithms that we propose. The experiments exhibit that the genetic algorithm, outperforms the other migration algorithms in searching for a migration sequence. When properly deployed, about a migration of 40% of routers reaps most of the benefit.
Graphene Nanoplatelets Doped with N at its Edges as Metal‐Free Cathodes for Organic Dye‐Sensitized Solar Cells

Challenging precious Pt-based electrocatalysts for dye-sensitized solar cells (DSSCs), graphene nanoplatelets that are N-doped at the edges (NGnPs) are prepared via simply ball-milling graphite in the presence of nitrogen gas. DSSCs based on specific nanoplatelets designated "NGnP5" display superior photovoltaic performance (power conversion efficiency, 10.27%) compared to that of conventional Pt-based devices (9.96%). More importantly, the NGnP counter electrode exhibits outstanding electrochemical stability and electrocatalytic activity with a cobalt-complex redox couple.
Estrogen-Like Properties of Fluorotelomer Alcohols as Revealed by MCF-7 Breast Cancer Cell Proliferation

We investigated estrogen-like properties of five perfluorinated compounds using a combination of three in vitro assays. By means of an E-screen assay, we detected the proliferation-promoting capacity of the fluorotelomer alcohols 1H,1H,2H,2H-perfluorooctan-1-ol (6:2 FTOH) and 1H,1H,2H,2H-perfluoro-decan-1-ol (8:2 FTOH). The more widely environmentally distributed compounds perfluoro-1-octane sulfonate, perfluorooctanoic acid, and perfluorononanoic acid did not seem to possess this hormone-dependent proliferation capacity. We investigated cell cycle dynamics using flow cytometric analyses of the DNA content of the nuclei of MCF-7 breast cancer cells. Exposure to both fluorotelomer alcohols stimulated resting MCF-7 cells to reenter the synthesis phase (S-phase) of the cell cycle. After only 24 hr of treatment, we observed significant increases in the percentage of cells in the S-phase. In order to further investigate the resemblance of the newly detected xenoestrogens to the reference compound 17β-estradiol (E2), gene expression of a number of estrogen-responsive genes was analyzed by real-time polymerase chain reaction. With E2, as well as 4-nonylphenol and the fluorotelomer alcohols, we observed up-regulation of trefoil factor 1, progesterone receptor, and PDZK1 and down-regulation of ERBB2 gene expression. We observed small but relevant up-regulation of the estrogen receptor as a consequence of exposures to 6:2 FTOH or 8:2 FTOH. The latter finding suggests an alternative mode of action of the fluorotelomer alcohols compared with that of E2. This study clearly underlines the need for future in vivo testing for specific endocrine-related end points.
Exploring affective technologies for the classroom with the subtle stone

Constructive emotional experiences are strongly related to effective learning. Yet, it is challenging for teachers, researchers and students alike to understand the emotions experienced in the classroom setting. Advances in wireless and sensor technologies open up possibilities for better supporting emotions. However, little work has explored how affective technologies in the classroom might operate. This paper describes a study where 15 high school students used the Subtle Stone: a tangible technology designed to support students' active emotional communication in the classroom. We report on how the students used and experienced this technology, and the values they demonstrated through this use: flexibility, privacy, agency, voice and reflection. We conclude by examining future possibilities for affective technologies in the classroom.
Contextual Policy Enforcement in Android Applications with Permission Event Graphs

The difference between a malicious and a benign Android application can often be characterised by context and sequence in which certain permissions and APIs are used. We present a new technique for checking temporal properties of the interaction between an application and the Android event system. Our tool can automatically detect sensitive operations being performed without the user’s consent, such as recording audio after the stop button is pressed, or accessing an address book in the background. Our work centres around a new abstraction of Android applications, called a Permission Event Graph, which we construct with static analysis, and query using model checking. We evaluate application-independent properties on 152 malicious and 117 benign applications, and application-specific properties on 8 benign and 9 malicious applications. In both cases, we can detect, or prove the absence of malicious behaviour beyond the reach of existing techniques.
Edinburgh Research Explorer Protection of the Ovine Fetal Gut against Ureaplasma-Induced Chorioamnionitis

: Chorioamnionitis, sterols reduced intestinal accumulation of proinﬂammatory phospholipids and tended to prevent mucosal myeloperoxidase-positive (MPO) cell inﬂux, indicating an inhibition of gut inﬂammation. IA administration of plant sterols and carrier diminished intestinal mucosal damage, stimulated maturation of the immature epithelium, and partially prevented U. parvum -driven reduction of mucosal bile acids. In conclusion, we show that β -sitosterol and campesterol administration protected the fetus against adverse gut outcomes following UP-driven chorioamnionitis by preventing intestinal and systemic inﬂammation. The e ﬀ ects of plant sterol treatment in the context of UP-induced chorioamnionitis were investigated by studying circulatory cytokine levels, fetal gut inﬂammation, intestinal delivery of the plant sterols, and intestinal injury and maturation. UP-induced changes in the intestinal lipidome were partially normalized by plant sterols and / or the carrier. The carrier (2-hydroxypropyl- β -cyclodextrin), characterized by m / z 1331.52 [M + Na] + , m / z 1389.55, and m / z 1447.59, was detected in the intestinal epithelium of the 6-d UP + sterol + carrier and the 6-d UP + carrier groups. the e ﬀ ect of plant sterols on T-cells is determined by the underlying pathophysiology, intestinal region, timing of the intervention, duration, and nature of the inﬂammatory stimulus and developmental stage.
On the Existence of Optimal Policies for a Class of Static and Sequential Dynamic Teams

In this paper, we identify sufficient conditions under which static teams and a class of sequential dynamic teams admit team-optimal solutions. We first investigate the existence of optimal solutions in static teams where the observations of the decision makers are conditionally independent given the state and satisfy certain regularity conditions. Building on these findings and the static reduction method of Witsenhausen, we then extend the analysis to sequential dynamic teams. In particular, we show that a large class of dynamic linear-quadratic-Gaussian (LQG) teams, including the vector version of the well-known Witsenhausen's counterexample and the Gaussian relay channel problem viewed as a dynamic team, admit team-optimal solutions. Results in this paper substantially broaden the class of stochastic control problems with nonclassical information known to have optimal solutions.
Multiobjective Binary Biogeography Based Optimization for Feature Selection Using Gene Expression Data

Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.
HyperANF: approximating the neighbourhood function of very large graphs on a budget

The neighbourhood function NG(t) of a graph G gives, for each t ∈ N, the number of pairs of nodes x, y such that y is reachable from x in less that t hops. The neighbourhood function provides a wealth of information about the graph [10] (e.g., it easily allows one to compute its diameter), but it is very expensive to compute it exactly. Recently, the ANF algorithm [10] (approximate neighbourhood function) has been proposed with the purpose of approximating NG(t) on large graphs. We describe a breakthrough improvement over ANF in terms of speed and scalability. Our algorithm, called HyperANF, uses the new HyperLogLog counters [5] and combines them efficiently through broadword programming [8]; our implementation uses talk decomposition to exploit multi-core parallelism. With HyperANF, for the first time we can compute in a few hours the neighbourhood function of graphs with billions of nodes with a small error and good confidence using a standard workstation. Then, we turn to the study of the distribution of the distances between reachable nodes (that can be efficiently approximated by means of HyperANF), and discover the surprising fact that its index of dispersion provides a clear-cut characterisation of proper social networks vs. web graphs. We thus propose the spid (Shortest-Paths Index of Dispersion) of a graph as a new, informative statistics that is able to discriminate between the above two types of graphs. We believe this is the first proposal of a significant new non-local structural index for complex networks whose computation is highly scalable.
Phonological and syntactic competition effects in spoken word recognition: evidence from corpus-based statistics

ABSTRACT As spoken language unfolds over time the speech input transiently activates multiple candidates at different levels of the system – phonological, lexical, and syntactic – which in turn leads to short-lived between-candidate competition. In an fMRI study, we investigated how different kinds of linguistic competition may be modulated by the presence or absence of a prior context (Tyler 1984; Tyler et al. 2008). We found significant effects of lexico-phonological competition for isolated words, but not for words in short phrases, with high competition yielding greater activation in left inferior frontal gyrus (LIFG) and posterior temporal regions. This suggests that phrasal contexts reduce lexico-phonological competition by eliminating form-class inconsistent cohort candidates. A corpus-derived measure of lexico-syntactic competition was associated with greater activation in LIFG for verbs in phrases, but not for isolated verbs, indicating that lexico-syntactic information is boosted by the phrasal context. Together, these findings indicate that LIFG plays a general role in resolving different kinds of linguistic competition.
Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees

Genomic regions participating in recombination events may support distinct topologies, and phylogenetic analyses should incorporate this heterogeneity. Existing phylogenetic methods for recombination detection are challenged by the enormous number of possible topologies, even for a moderate number of taxa. If, however, the detection analysis is conducted independently between each putative recombinant sequence and a set of reference parentals, potential recombinations between the recombinants are neglected. In this context, a recombination hotspot can be inferred in phylogenetic analyses if we observe several consecutive breakpoints. We developed a distance measure between unrooted topologies that closely resembles the number of recombinations. By introducing a prior distribution on these recombination distances, a Bayesian hierarchical model was devised to detect phylogenetic inconsistencies occurring due to recombinations. This model relaxes the assumption of known parental sequences, still common in HIV analysis, allowing the entire dataset to be analyzed at once. On simulated datasets with up to 16 taxa, our method correctly detected recombination breakpoints and the number of recombination events for each breakpoint. The procedure is robust to rate and transition∶transversion heterogeneities for simulations with and without recombination. This recombination distance is related to recombination hotspots. Applying this procedure to a genomic HIV-1 dataset, we found evidence for hotspots and de novo recombination.