Miracle-dz/UniFAR-FTUs huggingface dataset:
ACQ: An Automatic Clustering and Querying Approach for Large Image Databases

Large image collections such as web-based image databases are being built in various locations. Because of the diversity of such image data collections, clustering images becomes an important and non-trivial problem. Such clustering tries to find the densely populated regions in the feature space to be used for eficient image retrieval. In this paper, we present an automatic clustering and querying (ACQ) approach for large image databases. Our approach can eficiently detect clusters of arbitrary shape. It does not require the number of clusters to be known a priori and is insensitive to the noise (outliers) and the order of input data. Based on this clustering approach, eficient image querying is supported. Experiments demonstrate the effectiveness and eficiency of
Metabolic Limitations of Performance and Fatigue in Football

Football participation requires considerable utilization of both aerobic and anaerobic energy systems to match the high energetic demands of the sport. The consequent stresses imposed on the physiological and metabolic systems carries players to the threshold of exhaustion during match-play, from which they are required to recover in preparation for the subsequent game. A high number of players experience fatigue during the high-intensity bouts and a consequent decline in their performance towards the end of the game is a likely outcome during match-play. The current review aims to establish the current understanding that relates to metabolic limitations of performance and the associated mechanisms for the onset of fatigue that may be instrumental in further development of evidence-based nutritional and training interventions in this event.
Deformable kernels for early vision

A technique is presented that allows (1) computing the best approximation of a given family using linear combinations of a small number of basis functions; and (2) describing all finite-dimensional families, i.e. the families of filters for which a finite-dimensional representation is possible with no error. The technique is general and can be applied to generating filters in arbitrary dimensions. Experimental results that demonstrate the applicability of the technique to generating multi-orientation multiscale 2-D edge-detection kernels are presented. The implementation issues are also discussed. <<ETX>>
Increasing the Value of Process Modelling and Models

This paper presents an effort to increase the value gained from process modelling activities in an organisation, both on a project and an organisational level. The main result of this effort is a framework to facilitate awareness of, communication about, and coordination of modelling initiatives between stakeholders and within and across projects, over time. The current version of the framework as a normative process model, is presented and discussed in the context of case projects and activities, and we conclude that although work remains both on sophistication of the approach and on validation of its general applicability and value, our results so far show that it addresses recognised challenges in a useful way. Introduction Enterprises have a long history as functional organisations. The introduction of machinery in the 18 century lead to the principle of work specialisation and the division of labour, and on to the need of capturing, structuring, storing and distributing information and knowledge on both the product and the work or business process. Business process models have always provided a means to structure the enormous amount of information needed in many business processes (Hammer, 1990). The availability of computers provided more flexibility in information handling, and lead to the adoption of modelling languages originally developed for systems modelling like IDEF0. (IDEF-0, 1993). The modelling of work processes, organisational structures and infrastructure as an approach to organisational and software development and documentation is becoming an established practice in many companies. Process modelling is not done for one specific objective only, which partly explains the great diversity of approaches found in literature and practice. Five main categories for process
The deadlock problem in the control of Flexible Manufacturing Systems: An overview of the Petri net approach

The disparity of Petri net models in the literature for the study of resource allocation problems in Flexible Manufacturing Systems (FMSs) is overwhelming. Paradoxically, those models often strongly overlap in terms of their modeling capability. Such clustering seems to be impelled by a mature knowledge of results which capsize liveness at the topological level of the FMS for families of models which comply with some properties of good behaviour. Not rarely, such properties are embraced at the expense of severe or inconsistent syntax restrictions paying slight regard to actual modelling requirements in the application domain of FMSs. In this paper, we promote a distillation of the current amalgam of subclasses and tangent results, proposing taxonomic categories that allow capturing the different capabilities of each kind of model and establishing a reference framework for past and future advances.
Routing attacks prevention mechanism for RPL based on micropayment scheme

RPL (Routing Protocol for Low-power and lossy networks) is a special routing protocol designed for optimizing 6LoWPAN (IPv6 over Lower power Wireless Personal Area Network) operations. RPL suffers from resource constraints on battery, processor, memory and bandwidth, which make RPL more vulnerable to several threats than normal routing protocol. It is necessary to design a lightweight method to protect RPL from attacks. This paper analyzed security threats for RPL and proposed an efficient protect mechanism based on micropayment scheme(MPS). In this method, the credible root node generates payword chain for each data node's routers using hash algorithm, and establish payments relation of trust between data nodes and routers. In addition, anonymous interaction is essential in some conditions. An pseudonym mechanism was designed to implement anonymity between leaf nodes and routers. Theoretical analysis shows that the scheme can effectively resist to the security threats in RPL network without bringing more additional performance lost. Finally, the simulation proved the method is effective and light-weight.
Pseudo-collaboration as a method to perform selective algorithmic mediation in collaborative IR systems

Traditional recommendation systems suggest results based on data collected from users’ actions. Many of the newer information retrieval (IR) systems incorporate social search or collective search signals as an extension to standard term-based retrieval algorithms. Systems based on social or collaborative search methods, however, do not consider when, how, and to what extent such support could help or hurt their users’ search performance. In this poster we propose a novel approach of selective algorithmic mediation capable of identifying when a user should be aided by a collaborator and to what extent such help could enhance search success. We demonstrate the applicability and benefits of our approach through simulations using a pseudocollaboration method on the log data of individual users and pairs of users gathered during a laboratory study with 131 participants. The results show that our approach can improve the search performance of both individual searchers and others collaborating intentionally by identifying and recommending regions in search processes with best chance of improvements, thus increasing the likelihood that users find more useful information with less effort.
Clustering Of Big Data Using a Novel Method in K-Means and K-Medoids Clustering Algorithms

Large amounts of structured and unstructured data are being collected from various sources for several years. These huge amounts of data called Big Data which are difficult to handle by a single machine require the work to be distributed across large number of computers. Hadoop is a distributed framework which uses MapReduce programming model to process the data in a distributed manner. Clustering analysis is one of the most important research areas in the field of data mining. Clustering is the most commonly used data processing algorithms. Clustering is a division of data into different groups. Data are grouped in such a way that data of the same group are similar and the data in other groups are dissimilar. Clustering aims in minimizing intra-class similarity and in maximizing interclass dissimilarity. k-Means is the popular clustering algorithm because of its simplicity. Nowadays, as the volume of data increases, researchers started to use MapReduce which is a parallel processing framework to get high performance. But, MapReduce is unsuitable for iterated algorithms owing to repeated times of restarting jobs, big data reading and shuffling. To overcome this problem, a novel processing model in MapReduce called optimized kmeans clustering method which uses the methods of probability sampling and clustering, merging using two algorithms called weight based merge clustering and distribution based merge clustering is introduced to eliminate the iteration dependence and obtain high performance. Also the algorithms are compared with the kmedoids clustering algorithms
Diversity of Group I and II Clostridium botulinum Strains from France Including Recently Identified Subtypes

In France, human botulism is mainly food-borne intoxication, whereas infant botulism is rare. A total of 99 group I and II Clostridium botulinum strains including 59 type A (12 historical isolates [1947–1961], 43 from France [1986–2013], 3 from other countries, and 1 collection strain), 31 type B (3 historical, 23 recent isolates, 4 from other countries, and 1 collection strain), and 9 type E (5 historical, 3 isolates, and 1 collection strain) were investigated by botulinum locus gene sequencing and multilocus sequence typing analysis. Historical C. botulinum A strains mainly belonged to subtype A1 and sequence type (ST) 1, whereas recent strains exhibited a wide genetic diversity: subtype A1 in orfX or ha locus, A1(B), A1(F), A2, A2b2, A5(B2′) A5(B3′), as well as the recently identified A7 and A8 subtypes, and were distributed into 25 STs. Clostridium botulinum A1(B) was the most frequent subtype from food-borne botulism and food. Group I C. botulinum type B in France were mainly subtype B2 (14 out of 20 historical and recent strains) and were divided into 19 STs. Food-borne botulism resulting from ham consumption during the recent period was due to group II C. botulinum B4. Type E botulism is rare in France, 5 historical and 1 recent strains were subtype E3. A subtype E12 was recently identified from an unusual ham contamination. Clostridium botulinum strains from human botulism in France showed a wide genetic diversity and seems to result not from a single evolutionary lineage but from multiple and independent genetic rearrangements.
A Fast Motion Estimation Algorithm for SAD Optimization in Sub-pixel

The motion estimation is the most important technique in the image compression of the video standards. In the case of next generation standards in the video codec as H.264, a high compression-efficiency can be also obtained by using a motion compensation. To obtain the accurate motion search, a motion estimation should be achieved up to 1/2 pixel and 1/4 pixel units. To do this, the computational complexity is increased although the image compression rate is increased. Therefore, in this paper, we propose the advanced sub-pixel block matching algorithm to reduce the computational complexity by using statistical characteristics of SAD (sum of absolute difference). Generally, the probability of the minimum SAD values is high when searching point is in the distance 1 from the reference point. Thus, we reduced the searching area and then we can overcome the computational complexity problem. The main concept of proposed algorithm, which based on TSS (three step search) method, first we find three minimum SAD points which is in integer distance unit, and then, in second step, the optimal point is in 1/2 pixel unit either between the most minimum SAD value point and the second minimum SAD point or between the most minimum SAD value point and the third minimum SAD point In third step, after finding the smallest SAD value between two SAD values on 1/2 pixel unit, the final optimized point is between the most minimum SAD value and the result value of the third step, in 1/2 pixel unit i.e., 1/4 pixel unit in totally. The conventional TSS method needs an eight search points in the sub-pixel steps in 1/2 pixel unit and also an eight search points in 1/4 pixel, to detect the optimal point. However, in proposed algorithm, only total five search points are needed. In the result, 22% improvement of processing speed is obtained.
Uniform spanning forests

We study uniform spanning forest measures on infinite graphs, which are weak limits of uniform spanning tree measures from finite subgraphs. These limits can be taken with free (FSF) or wired (WSF) boundary conditions. Pemantle proved that the free and wired spanning forests coincide in Z d and that they give a single tree iff d ≤ 4. In the present work, we extend Pemantle's alternative to general graphs and exhibit further connections of uniform spanning forests to random walks, potential theory, invariant percolation and amenability. The uniform spanning forest model is related to random cluster models in statistical physics, but, because of the preceding connections, its analysis can be carried further. Among our results are the following: ○ The FSF and WSF in a graph G coincide iff all harmonic Dirichlet functions on G are constant. ○ The tail σ-fields of the WSF and the FSF are trivial on any graph. ○ On any Cayley graph that is not a finite extension of Z, all component trees of the WSF have one end; this is new in Z d for d > 5. On any tree, as well as on any graph with spectral radius less than 1, a.s. all components of the WSF are recurrent. ○ The basic topology of the free and the wired uniform spanning forest measures on lattices in hyperbolic space H d is analyzed. A Cayley graph is amenable iff for all e > 0, the union of the WSF and Bernoulli percolation with parameter e is connected. ○ Harmonic measure from infinity is shown to exist on any recurrent proper planar graph with finite codegrees. We also present numerous open problems and conjectures.
Motif Simplification : Improving Network Visualization Readability with Fan and Parallel Glyphs

Network data structures have been used extensively in recent years for modeling entities and their ties, across diverse disciplines. Analyzing networks involves understanding the complex relationships between entities, as well as any attributes, statistics, or groupings associated with them. A widely used class of visualizations called sociograms excel at showing the network topology, attributes, and groupings simultaneously. However, many sociograms are not easily readable or difficult to extract meaning from because of the inherent complexity of the relationships and the number of items designers try to render in limited space. This paper introduces a technique called motif simplification that leverages the repeating motifs in networks to reduce visualization complexity and increase readability. We propose replacing motifs in the network with easily understandable glyphs that (1) require less screen space, (2) are easier to understand in the context of the network, (3) can reveal otherwise hidden relationships, and (4) result in minimal loss of fidelity. We tackle two frequently occurring and high-payoff motifs: a fan motif consisting of a fan of nodes with only a single neighbor connecting them to the network, and a parallel motif of functionally equivalent nodes that span two or more other nodes together. We contribute the design of representative glyphs for these motifs, algorithms for detecting them, a publicly available reference implementation, and initial case studies and user feedback that support the motif simplification approach.
The Complexity of AND-Decomposition of Boolean Formulas

Decomposition of boolean functions is an important research topic having a long history and a wide range of applications in the logic circuit synthesis, (hyper)graph/game theory, and combinatorial optimization. AND-decomposition means representing a boolean function as a conjunction of two (or several) functions sharing a given subset of variables. If the shared subset is empty, the decomposition is called disjoint. Though numerous heuristics and approaches to computing more general forms of decompositions have been developed, the algorithmic complexity of ANDdecomposition remained unknown. The renewed interest to this question has been motivated by the recent research on decomposition of theories in expressive logics and modern developments in the analysis of complex systems, combinatorial optimization, and circuit design. In this paper, we study the complexity of AND-decomposition for various representations of boolean functions (CNF/DNF/Full DNF/ANF), prove a number of complexity bounds, and identify tractable cases. The positive complexity results of this paper follow from tractability of multilinear polynomial factorization over the finite field of order 2, for which we provide a polytime factorization algorithm based on identity testing for partial derivatives of multilinear polynomials.
Pros and Cons of truncated Gaussian EP in the context of Approximate Inference Control

Approximate Inference Control (AICO, [1]) is a method for solving Stochastic Optimal Control (SOC) problems. A core step in this method is to approximate the belief in each time step, that is, compute a distribution over the system state given the coupling to the previous state, the next state, and the current constraints or goals. Since constraints and goals may be arbitrarily non-linear (specified in arbitrary task spaces) exact inference is infeasible. The core motivation for introducing AICO was to leverage the variety of existing approximate inference methods to suggest new efficient approximations in the context of SOC. In [1] we used Gaussian message approximations which can be computed from local LQG approximations of the system dynamics and costs. We also mentioned the possibility to use Expectation Propagation and truncated Gaussians for inference under hard constraints and limits. With this abstract we discuss in more detail the approach, its promises and limitations, and report on experiments with AICO and truncated Gaussians for robotics problems.
Asymptotically Stable Walking of a Simple Underactuated 3D Bipedal Robot

This paper presents a feedback controller that achieves an asymptotically stable, periodic, and fast walking gait for a 3D bipedal robot consisting of 3-links and passive (unactuated) point-feet. The robot has 6 DOF in the single support phase and four actuators. In addition to the reduced number of actuators, the interest of studying robots with point feet is that the feedback control solution must exploit the robot's natural dynamics in order to achieve balance while walking. We use an extension of the method of virtual constraints, a very successful method for planar bipeds, in order to simultaneously compute a periodic orbit and an autonomous feedback controller that realizes the orbit, for a 3D (spatial) bipedal walking robot. This method allows the computations for the controller design and the periodic orbit to be carried out on a 2-DOF subsystem of the 6-DOF robot model. The linearization of the Poincare map of the closed-loop system proves that the achieved periodic walking motion, at a speed of approximately one and a half body lengths per second, is exponentially stable.
Research on Modeling Method of Virtual Enterprise in Uncertain Environments

Virtual Enterprise is a kind of temporary organization which is made up of many enterprises. The internal and external environments of virtual enterprises are changing incessantly. Nowadays, it is short of the normalized methods of describing this kind of organizations in uncertain environments. Role theory is used to design role view which associates with task view, workflow view, organization view, agent view and interaction view. Finally, gains the modeling method of virtual enterprise based on extended agent UML.
Post-influenza Pneumonia Caused by the USA300 Community-Associated Methicillin-Resistant Staphylococcus aureus in Korea

Panton-Valentine leukocidin (PVL)-positive USA300 clone has been the most successful community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) clone spreading in North America. In contrast, PVL-negative ST72-CA-MRSA has been predominant in Korea, and there has been no report of infections by the USA300 strain except only one case report of perianal infection. Here, we describe the first case of pneumonia caused by the USA300 strain following pandemic influenza A (H1N1) in Korea. A 50-year-old man was admitted with fever and cough and chest radiograph showed pneumonic consolidation at the right lower lung zone. He received a ventilator support because of respiratory failure. PCR for pandemic influenza A (H1N1) in nasopharyngeal swab was positive, and culture of sputum and endotracheal aspirate grew MRSA. Typing of the isolate revealed that it was PVL-positive, ST 8-MRSA-SCCmec type IV. The analysis of the PFGE patterns showed that this isolate was the same pulsotype as the USA300 strain.
Surfaceome of Leptospira spp

ABSTRACT The identification of the subset of outer membrane proteins exposed on the surface of a bacterial cell (the surfaceome) is critical to understanding the interactions of bacteria with their environments and greatly narrows the search for protective antigens of extracellular pathogens. The surfaceome of Leptospira was investigated by biotin labeling of viable leptospires, affinity capture of the biotinylated proteins, two-dimensional gel electrophoresis, and mass spectrometry (MS). The leptospiral surfaceome was found to be predominantly made up of a small number of already characterized proteins, being in order of relative abundance on the cell surface: LipL32 > LipL21 > LipL41. Of these proteins, only LipL32 had not been previously identified as surface exposed. LipL32 surface exposure was subsequently verified by three independent approaches: surface immunofluorescence, whole-cell enzyme-linked immunosorbent assay (ELISA), and immunoelectron microscopy. Three other proteins, Q8F8Q0 (a putative transmembrane outer membrane protein) and two proteins of 20 kDa and 55 kDa that could not be identified by MS, one of which demonstrated a high degree of labeling potentially representing an additional, as-yet-uncharacterized, surface-exposed protein. Minor labeling of p31LipL45, GroEL, and FlaB1 was also observed. Expression of the surfaceome constituents remained unchanged under a range of conditions investigated, including temperature and the presence of serum or urine. Immunization of mice with affinity-captured surface components stimulated the production of antibodies that bound surface proteins from heterologous leptospiral strains. The surfaceomics approach is particularly amenable to protein expression profiling using small amounts of sample (<107 cells) offering the potential to analyze bacterial surface expression during infection.