Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale visual concept detection with explicit kernel maps and power mean SVM

Many emerging application areas in video and image processing require large-scale visual concept detection. Examples include content-based indexing of online user-generated videos and 24/7 archival of TV broadcasts. The current state of the art in concept detection uses bag-of-visual-words features with computationally heavy exponential kernel classifiers. We argue that this classifier approach is not feasible for large-scale real-time applications, and propose instead to use combinations of approximate additive kernel classifiers. By using explicit kernel maps and the power mean SVM, followed by fusion of classifiers trained on different features, we achieve high retrieval precision while retaining real-time performance for large sets of concepts. This paper presents a series of experiments with the large-scale TRECVID 2012 video database and the commonly used Fifteen Scene Categories image database. We show significantly improved retrieval performance over standard linear classifiers, and by late fusion over several visual features, the approximative additive kernels outperform any single exponential kernel in only a fraction of the detection time.
Applications for a hybrid operating room in thoracic surgery: from multidisciplinary procedures to image-guided video-assisted thoracoscopic surgery

ABSTRACT The concept of a hybrid operating room represents the union of a high-complexity surgical apparatus with state-of-the-art radiological tools (ultrasound, CT, fluoroscopy, or magnetic resonance imaging), in order to perform highly effective, minimally invasive procedures. Although the use of a hybrid operating room is well established in specialties such as neurosurgery and cardiovascular surgery, it has rarely been explored in thoracic surgery. Our objective was to discuss the possible applications of this technology in thoracic surgery, through the reporting of three cases.
Lecture Notes on Parameterized Complexity

Parameterized complexity is a framework developed mainly by Downey and Fellows [6] to deal with the complexity of a problem with respect to its input size |I| and a chosen parameter k. For instance, we can take the size k of a vertex cover in the classical Vertex Cover problem as a parameter to form the parameterized problem k-Vertex Cover. The key issue in parameterized complexity is to confine the exponential runtime of algorithms for a parameterized problem to its parameter k, and therefore solve the the problem efficiently when k is “small”, which can be very useful in practice. For instance, the k-Vertex Cover problem can be solved in O(1.2738k + kn) time [5], which makes the problem solvable in practice for n ≤ 1015 and k ≤ 150.
The research of portable ECG monitoring system with USB host Interface

This paper introduces a portable ECG monitor with USB host Interface. This system uses Microprocessor C8051F021 as its controlling core, and SL811HS as USB host Interface chip. Based on this hardware, the paper presents USB host specification stack, specification frame about USB Mass Storage and FAT file system so that the collected ECG signals can be stored in U-Disk without delay. In addition, the ECG signal collection circuit of this system embodies several characteristics including high input impedance, free switch of lead, full filter function and low circuit loss, all of which are quite suitable for portable ECG monitoring system.
A Study of the Cognition-Action Gap in Knowledge Management

We investigated three types of volitional control mechanisms that may impact people’s knowledge management (KM) practices. Our results show that, when employing KM, people do not always perform in a manner consistent with their beliefs concerning attitudes and intentions. This cognition-behavior inconsistency can be explained by volitional control mechanisms. Specifically, both perceived self-efficacy (Bandura 1997) and action control (Kuhl and Bechmann 1985) play a role in motivating individuals to share and use knowledge, while perceived behavioral control does not. In addition, action/state orientation moderates a person’s enactment of subjective norm and self-efficacy beliefs into intentions just as it moderates enactment of perceived behavioral control belief into behaviors. These results have important theoretical and managerial implication.
Industrial application of acousto-ultrasonic signal quality and robust time-of-flight estimation for anisotropic materials

Algorithm development work is described for the initial phase of a two-part task, where these algorithm upgrades improved existing material characterization-related data collection by culling ultrasonic signals for quality and designing a more noise-resistant time-of-flight estimate. This prepares the existing factory-installed ultrasonic signal analysis system for future data collection and algorithm training, yielding a more comprehensive, real-time material characterization capability within this manufacturing setting.
Edge detection for Very High Resolution satellite imagery based on Cellular Neural Network

In the context of Very High Resolution (VHR) satellite imagery, edge detection is one of the most important and difficult steps in image processing and pattern recognition. This paper presents the use of Cellular Neural Network (CNN) in edge detection and shows its capacity to locate and identify discontinuities in the gray levels of the objects that appear in VHR im4 age. The results show that the characteristics of the CNN, in terms of its local connectivity, are those that allow greater extraction of continuous edges. The multi4layer processing structure design of the CNN allows to identify a defini4 tive edge in urban environment. To evaluate the results, a metric of peak signal to noise ratio (PSNR) has been introduced as a manner to rank the accuracy of the resultant edge determined by the assessed methods. The extracted VHR fea4 tures with the CNN edge detector include accuracy of edge location and better linking of edge segments.
Control of the error signals in negative correlation learning

Negative correlation learning has been proposed to create a set of negatively correlated artificial neural networks (ANNs) in a committee machine. In negative correlation learning, the error signals for each ANN on a given data are not only decided by the error differences between the output of ANN and the targets. Two terms are optimized at the same time. The first one is to minimize the error between the output of each ANN and the target output on the given data. The other one is to maximize the difference between the output of the ensemble and the output of each ANN on the given data. From the point of view on the bias-variance-covariance trade-off, the minimization of the first term would decrease the bias while the maximization of the second term would reduce the sum of bias and variance. In order to balance well among bias, variance and covariance, error signals in learning should be well adjusted. On one hand, when the learning would force itself to be closer to the ensemble, an individual ANN would choose to learn less so that the learning on that direction would be disencouraged. On the other hand, when the learning would help itself to be more different to the ensemble, an individual ANN would let itself to learn more so that the learning on that direction would be encouraged. A new version of negative correlation learning based on such error signal adjustment have been implemented in this paper. Experimental results were carried out to show how the error signal adjustment would help to achieve the better generalization.
Influence of Carbon Nanotube Coatings on Carbon Fiber by Ultrasonically Assisted Electrophoretic Deposition on Its Composite Interfacial Property

Carbon nanotube (CNT) coatings were utilized to enhance the interfacial properties of carbon fiber (CF)/epoxy(EP) composites by ultrasonically assisted electrophoretic deposition (EPD). A characterization of the CF surface properties was done before and after coating (surface chemistry, surface morphologies, and surface energy). The result shows that oxygenated groups concentrations of the CF surfaces experienced significant increases from 12.11% to 24.78%. Moreover, the uniform and homogeneous CNT films were tightly attached on the surface of CF, and the surface wettability of CF is significant improved by enhanced surface free energy when introduced ultrasonic during the EPD process. In addition, the interlaminar shear strength (ILSS) and water absorption of CF/EP composite were measured. Scanning electron microscopy (SEM) revealed that the fracture mechanisms of the new interface layer formed by depositing CNTs on the CF surface contributed to the enhancement of the mechanical performance of the epoxy. This means that the efficient method to improve interfacial performance of composites has shown great commercial application potential.
The saturation function of complete partite graphs

A graph G is called F-saturated if it is F-free but the addition of any missing edge to G creates a copy of F. Let the saturation function sat(n, F) be the minimum number of edges that an F-saturated graph on n vertices can have. We determine this function asymptotically for every ﬁxed complete partite graph F as n tends to inﬁnity and give some structural information about almost extremal F-saturated graphs. If the two largest parts of F have diﬀerent sizes, then we can reduce the error term to an additive constant.
Processing laser range image for the investigation on the long-term performance of ductile iron pipe

Prediction of service life of ductile iron pipe is carried out using the information on pipe condition, backfill/soil properties, and corrosion rates, as well as historical failure records. Among the factors that affect pipe condition, external pitting corrosion is a prominent variable contributing to the disintegrity of the pipe. A pipe scanner equipped with a laser displacement sensor was used to measure pitting corrosion. The pipe scanner can generate an accurate topographic mapping of the outer surface of the pipe. In order to accelerate the scanning process, a scan with reduced resolution is preferred. An image of higher resolution can be obtained with interpolation methods afterwards. Different interpolation methods are compared in term of the accuracy. To establish the statistical model, we need to identify and characterize each corroded area from the scanned laser range images. Quantitative description of the corroded areas, such as size, percentage of material loss, and location, needs to be generated and kept in a database for modeling and prediction purpose. This paper describes the procedure to carry out the process of the laser scan of ductile iron pipe.
Player Guild Dynamics and Evolution in Massively Multiplayer Online Games

In the latest versions of massively multiplayer online games (MMOGs), developers have purposefully made guilds part of game environments. Guilds represent a powerful method for giving players a sense of online community, but there is little quantitative data on guild dynamics. To address this topic, we took advantage of a feature found in one of today's most popular MMOGs (World of Warcraft) to collect in-game data: user interfaces that players can modify and refine. In addition to collecting data on in-game player activities, we used this feature to observe and investigate how players join and leave guilds. Data were analyzed for the purpose of identifying factors that propel game-world guild dynamics and evolution. After collecting data for 641,805 avatars on 62 Taiwanese World of Warcraft game servers between February 10 and April 10, 2006, we created five guild type categories (small, large, elite, newbie, and unstable) that have different meanings in terms of in-game group dynamics. By viewing players as the most important resource affecting guild life cycles, it is possible to analyze game worlds as ecosystems consisting of evolving guilds and to study how guild life cycles reflect game world characteristics.
A token strengthened encryption packer to prevent reverse engineering PE files

Before software is released, developers often pack it with a software packer in order to protect the internal design. Many existing packers have been unpacked by crackers. To address this problem, we propose an encryption packer that strengthens confidentiality of the protected PE file with the security token. The packer also incorporates techniques of anti-debugging, anti-dumping, and anti-tracking to prevent reverse engineering PE files. We have designed and implemented the system. Our experimental results show that it effectively resists common cracking methods with a little time and space cost.
Analysis of collision probability in unsaturated situation

A large number of the MAC protocols proposed for establishing wireless sensor networks are based on the 802.11 standard. The trade-off in these protocols is the control packet overhead and the retransmission cost due to collision without it. In this paper we evaluate the collision probability of a CSMA/CA MAC protocol in an unsaturated situation as a function of nodes' sampling and transmission rates. We provide an accurate and comprehensive analytical model in which a finite number of nodes exist. We assume an ideal channel condition, independent collision probability of packets as well as infrequent communication between sensor nodes. We will demonstrate that the collision probability changes from 0 to 0.22 as the sampling rate changes from 0 to 0.94Mbps. Moreover, we will demonstrate that collision only begins after the sampling rate reaches 0.31Mbps, which implies that for a sampling rate below this threshold, the control overhead can be avoided by altogether avoiding the collision avoidance mechanism.
Single- and multi-objective evolutionary algorithms for graph bisectioning

Evolutionary algorithms have been analyzed for many optimization problems during the last decade in order to understand their behavior. Recently, the application of multi-objective evolutionary algorithms for several single-objective problems has been investigated. The graph bisection problem (GBP), which is to partition the nodes of a given graph into two equal-sized subsets such that the number of edges crossing between these subsets is minimal, belongs to the group of single-objective NP-hard problems. Heuristics like Simulated Annealing have been proven to perform well on certain graph models. For evolutionary algorithms in contrast mostly experimental results exist. This paper proposes two evolutionary algorithms for GBP, a (1+1) EA and a multi-objective approach. The proposed algorithms are analyzed on two classes of instances in order to provide a first theoretical analysis of evolutionary algorithms for GBP as well as to point out their advantages and disadvantages. Each class causes one algorithm to struggle with a local optimum leading to expected exponential optimization time, while the other one finds the optimal solution within polynomial time. Experimental results are used to support the theoretical analysis.
Metrics for Evaluation of Student Models

Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student models. In the discussion we mention the relation of metrics to parameter fitting, the impact of student models on student practice (over-practice, under-practice), and point out connections to related work on evaluation of probability forecasters in other domains. We also provide an empirical comparison of metrics. One of the conclusion of the paper is that some commonly used metrics should not be used (MAE) or should be used more critically (AUC).
Automated Detection of Healthcare Associated Infections: External Validation and Updating of a Model for Surveillance of Drain-Related Meningitis

Objective Automated surveillance of healthcare-associated infections can improve efficiency and reliability of surveillance. The aim was to validate and update a previously developed multivariable prediction model for the detection of drain-related meningitis (DRM). Design Retrospective cohort study using traditional surveillance by infection control professionals as reference standard. Patients Patients receiving an external cerebrospinal fluid drain, either ventricular (EVD) or lumbar (ELD) in a tertiary medical care center. Children, patients with simultaneous drains, <1 day of follow-up or pre-existing meningitis were excluded leaving 105 patients in validation set (2010–2011) and 653 in updating set (2004–2011). Methods For validation, the original model was applied. Discrimination, classification and calibration were assessed. For updating, data from all available years was used to optimally re-estimate coefficients and determine whether extension with new predictors is necessary. The updated model was validated and adjusted for optimism (overfitting) using bootstrapping techniques. Results In model validation, the rate of DRM was 17.4/1000 days at risk. All cases were detected by the model. The area under the ROC curve was 0.951. The positive predictive value was 58.8% (95% CI 40.7–75.4) and calibration was good. The revised model also includes Gram stain results. Area under the ROC curve after correction for optimism was 0.963 (95% CI 0.953– 0.974). Group-level prediction was adequate. Conclusions The previously developed multivariable prediction model maintains discriminatory power and calibration in an independent patient population. The updated model incorporates all available data and performs well, also after elaborate adjustment for optimism.
Accretion Disks around Black Holes: Dynamical Evolution, Meridional Circulations, and Gamma-Ray Bursts

We study the hydrodynamic evolution of massive accretion disks around black holes, formed when a neutron star is disrupted by a black hole in a binary system. The initial conditions are taken from three-dimensional calculations of coalescing binaries. By assuming azimuthal symmetry we are able to follow the time dependence of the disk structure for 0.2 s in cylindrical coordinates (r, z). We use an ideal gas equation of state and assume that all the dissipated energy is radiated away. The disks evolve because of viscous stresses, modeled with an α law. We study the disk structure and, in particular, the strong meridional circulations that are established and persist throughout our calculations. These consist of strong outflows along the equatorial plane that reverse direction close to the surface of the disk and converge on the accretor. In the context of gamma-ray bursts (GRBs), we estimate the energy released from the system in neutrinos and through magnetic-dominated mechanisms and find it can be as high as Eν ≈ 1052 ergs and EBZ ≈ 1051 ergs, respectively, during an estimated accretion timescale of 0.1-0.2 s. The ν annihilation is likely to produce bursts from only a short, impulsive energy input L ∝ t-5/2 and so would be unable to account for a large fraction of bursts that show complicated light curves. On the other hand, a gas mass ≈0.1-0.25 M☉ survives in the orbiting debris, which enables strong magnetic fields ≈1016 G to be anchored in the dense matter long enough to power short duration GRBs. We highlight the effects that the initial disk and black holes masses, viscosity, and binary mass ratio have on the evolution of the disk structure. Finally, we investigate the continuous energy injection that arises as the black hole slowly swallows the rest of the disk and discuss its consequences on the GRB afterglow emission.
Affordance Prediction via Learned Object Attributes

—We present a novel method for learning and predicting the affordances of an object based on its physical and visual attributes. Affordance prediction is a key task in autonomous robot learning, as it allows a robot to reason about the actions it can perform in order to accomplish its goals. Previous approaches to affordance prediction have either learned direct mappings from visual features to affordances, or have introduced object categories as an intermediate representation. In this paper, we argue that physical and visual attributes provide a more appropriate mid-level representation for affordance prediction, because they support information-sharing between affordances and objects, resulting in superior generalization performance. In particular, affordances are more likely to be correlated with the attributes of an object than they are with its visual appearance or a linguistically-derived object category. We provide preliminary validation of our method experimentally, and present empirical comparisons to both the direct and category-based approaches of affordance prediction. Our encouraging results suggest the promise of the attribute-based approach to affordance prediction.
Response to Levine

In this short reply to Levine's critique, I defend the enterprise of 'fusion philosophy. ' I agree that the sort of careful scholarly examination of Asian philosophical traditions that is often done under the banner of 'comparative philosophy' is of great importance. But it is a separate question whether those traditions have resources that would help us solve philosophical problems of current interest. This is the question fusion philosophy tries to answer.
Strategies for Covert Web Search

Web search engines provide easy access to a wealth of open access information but depend for their optimal use upon careful selection of search terms in order to return a high proportion of relevant information. Generally, this implies a clear association between the meaning of the chosen search expression and the information needs of the searcher. In contrast, this paper considers a scenario in which the searcher may wish to retrieve information without making their information requirements transparent. We argue that careful choice of search strategy can also facilitate retrieval of relevant information in a covert fashion that conceals the searcher's intention by ensuring return of a majority of irrelevant search hits.
New directions in exploring the migration industries: introduction to special issue

ABSTRACT This Special Issue explores the directions through which we can take research on the migration industries. In this introduction, we review existing research on migration industries to look at how this explores questions on how migration industries foster, assist and constrain migration. In doing so, we argue that these questions have primarily been approached from three different perspectives: structuralist, labour market and mobilities, but these perspectives often speak past rather than to one another. In highlighting how these approaches can work together, the question that the Special Issue explores becomes how do the migration industries function and when/where/how do they intersect with other domains of migration. In highlighting the contributions that each paper in the special issue makes to answering this question, we show how an understanding of the migration industries is not just a research field in itself, but can strengthen our understanding of migration.