Miracle-dz/UniFAR-FTUs huggingface dataset:
Web Scale Information Extraction with LODIE

Information Extraction (IE) is the technique for transforming unstructured textual data into structured representation that can be understood by machines. The exponential growth of the Web generates an exceptional quantity of data for which automatic knowledge capture is essential. This work describes the methodology for Web scale Information Extraction adopted by the LODIE project (Linked Open Data Information Extraction). LODIE aims to develop Information Extraction techniques able to (i) scale at web level and (ii) adapt to user information need. The core idea behind LODIE is the usage of Linked Open Data, a very large-scale information resource, as a ground-breaking solution for IE, which provides invaluable annotated data on a growing number of domains.
A Novel Access Control Scheme for Multimedia Content with Modified Hash Chain

With the continuing growth in network technology, the exchange of digital images and audio as well as text has become very common regardless of whether the digital content is used for commercial purpose or not. Since such digital content is easily duplicated and re-distributed, protecting copyrights and privacy is an important issue. For the protection of digital content, access control based on naive encryption (encrypting the whole content) (1) or media-aware encryption (2–6) has been studied widely.
The use of fatty acid methyl ester analysis (FAME) for the identification of heterotrophic bacteria present on three mural paintings showing severe damage by microorganisms.

Mural paintings in Carmona (Spain), Herberstein (Austria) and Greene (Germany), showing visible deterioration by microorganisms, were sampled to investigate the biodiversity of the heterotrophic bacteria present. Four hundred twenty-eight bacterial strains were isolated from which 385 were characterized by fatty acid methyl ester analysis (FAME). The isolates were grouped into 41 clusters on the basis of their FAME profiles, 20 isolates remained ungrouped. The majority (94%) of the isolates comprised the gram-positive bacteria and the main clusters were identified as Bacillus sp. , Paenibacillus sp. , Micrococcus sp. , Arthrobacter sp. and Staphylococcus sp. Other clusters contain nocardioform actinomycetes and gram-negative bacteria, respectively. A cluster of the latter contained extreme halotolerant bacteria isolated in Herberstein. The FAME profiles of this cluster showed a high similarity with Halomonas.
MODELING PEER-TO-PEER NETWORK TOPOLOGIES THROUGH "SMALL-WORLD" MODELS AND POWER LAWS

I INTRODUCTION The recent emergence of novel network applications such as Gnutella, Freenet, and Napster has reincarnated the familiar peer-to-peer (P2P) architecture model of the original Internet in new and innovative ways in an effort to facilitate worldwide sharing of information [1]. As a result, there is an ever-increasing need for distributed protocols that would allow peer-to-peer applications to scale to a large community of users. The main difficulty in designing such protocols is that currently, very little is known about the nature of networks on which they would be operating. Current protocols were designed without any knowledge about the underlying network topology. The end result is that even simple protocols, as in the case of Gnutella, result in complex interactions that can adversely affect system performance. To study these interactions, we first need an accurate model of the network topology. We model the topology of P2P networks by undirected graph G, where nodes represent hosts and edges represent connections between those hosts. In this paper we point out several important structural characteristics of the P2P network topology graph, namely the " small-world " properties and several power law distributions of various graph metrics. We report measurements through experimental studies of a large P2P network application known as Gnutella. To obtain the Gnutella topology data, a network crawler that allows topology discovery to be performed in parallel was developed. Upon analysing the obtained topology data, we discovered it exhibits strong "small-world" properties. More specifically, the properties of small diameter and clustering were observed. In addition, we report evidence of four different power laws previously observed in other technological networks, such as the Internet and the WWW. Recent research results show that the " small-world " and power-law properties of the underlying network topology can significantly impact the performance of algorithms such as those for routing and searching [2, 3]. Therefore the existence of these properties in P2P networks presents an important issue to consider when designing new, more-scalable application-level protocols. II " SMALL-WORLD " PROPERTIES The term " small-world " originated with a famous social experiment conducted by Stanley Milgram in the late 1960s. Watts and Strogatz in [4] showed that other networks, such as those occurring in nature and technology, also exhibit "small-world" behavior. Throughout this work we use the term " small-world " loosely to mean that the network possesses both small diameter and is also highly clustered. …
Incorporating load dependent servers in approximate mean value analysis

Queueing network performance modelling technology has made tremendous strides in recent years. Two of the most important developments in facilitating the modelling of large and complex systems are hierarchical modelling, in which a single load dependent server is used as a surrogate for a subsystem, and approximate mean value analysis, in which reliable approximate solutions of separable models are efficiently obtained. Unfortunately, there has been no successful marriage of these two developments; that is, existing algorithms for approximate mean value analysis do not accommodate load dependent servers reliably. This paper presents a successful technique for incorporating load dependent servers in approximate mean value analysis. We consider multiple class models in which the service rate of each load dependent server is a function of the queue length at that server. In other words, load dependent center k delivers “service units” at a total rate of f@@@@ (n@@@@) when n@@@@ customers are present. We present extensive experimental validation which indicates that our algorithm contributes an average error in response times of less than 1% compared to the (much more expensive) exact solution. In addition to the practical value of our algorithm, several of the techniques that it employs are of independent interest.
Polarization and SEDs from Microlensing of Circumstellar Envelopes

Microlensing surveys have proven to be tremendously fruitful in providing valuable data products for many fields of astrophysics, from eclipse lightcurves for substellar candidates to limb darkening in stellar atmospheres. We report on a program of modeling observables from microlensing of circumstellar envelopes, particularly those of red giant stars that are the most likely to show finite source effects. We will summarize work for how polarization light curves can be used to infer envelope properties and will describe recent modeling of the time dependent spectral energy distributions (SEDs) for microlensing of dusty winds. One of the most exciting developments is the possibility of measuring variable polarization from microlensing in a suitable source using the RINGO polarimeter at La Palma. Also quite interesting is the possibility of probing a dusty wind using IRAC data for a suitable source in the event that Spitzer has a ``warm'' cycle.
A Perception of Teachers of the Potentialities and Fragilities in the Teaching Process Learning through Self-Assessment

This research aims to analyze from the perception of teachers of a higher education institution of Tocantins the quality of education offered by this institution, through the analysis of the results of the institutional self-assessment applied by means of a questionnaire composed of common perceptions of the surveyed subject. Among the findings of the research, it is noted that teachers consider the methodology of lessons and the integration of disciplines as positive points, as well as the reformulations of the pedagogical political project of the course, but there is a need for improvements in the practical teaching activities and the use of new technologies. It is noted that this study brought, through the teaching segment, a perspective of the actual situation of higher courses in respect of teaching and it was verified that the Institute is well evaluated, with a good concept, as statistical data 
Approximation error of shifted signals in spline spaces

Spline signal spaces offer several advantages for the representation of signals compared with the more traditional signal spaces of bandlimited signals. Among them are the finite support of B-splines, simple manipulations like differentiation and integration, etc. A major disadvantage, however, is that spline signal spaces are not closed under signal shifts. In order to assess the approximation error introduced by shifting a spline signal, the approximation error norm and its average are evaluated. Furthermore, an upper bound on the expected normalized approximation error is derived using Reid's inequality.
Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein–protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.
Tiled Auxetic Cylinders for Soft Robots

Compliant structures allow robots to overcome environmental challenges by deforming and conforming their bodies. In this paper, we investigate auxetic structures as a means of achieving this compliance for soft robots. Taking a tiling based approach, we fabricate 3D printed cylindrical auxetic structures to create tiled auxetic cylinders (TACs). We characterise the relative stiffness of the structures and show that variation in behaviour can be achieved by modifying the geometry within the same tiling family. In addition, we analysed the equivalent Poisson's ratio and found the range between the investigated designs to span from −0.33 to −2. Furthermore, we demonstrate a conceptual application in the design of a soft robot using the auxetic cylinders. We show that these structures can reactively change in shape, thereby reducing the complexity of control, with potential applications in confined spaces such as the human body, or for exploration through unpredictable terrain.
Finding Acceptable Pareto-Optimal Solutions using Multiobjective Genetic Algorithms

This paper investigates the problem of using a genetic algorithm to converge on a small, user-defined subset of acceptable solutions to multiobjective problems, in the Pareto-optimal (P-O) range. The paper initially explores exactly why separate objectives can cause problems in a genetic algorithm (GA). A technique to guide the GA to converge on the subset of acceptable solutions is then introduced. The paper then describes the application of six multiobjective techniques (three established methods and three new, or less commonly used methods) to four test functions. The previously unknown distribution of solutions produced in the P-O range(s) by each method is described. The distribution of solutions and the ability of each method to guide the GA to converge on a small, user-defined subset of P-O solutions is then assessed, with the conclusion that two of the new multiobjective ranking methods are most useful.
Immigration attitudes amongst European Muslims: social identity, economic threat and familiar experiences

ABSTRACT Studies of immigration attitudes have typically taken the majority perspective, focusing on views of majority rather than minority groups. However, as modern societies have grown in diversity and more people within society have migration histories, this approach has become more partial. This article uses European Social Survey Round 7 to explore attitudes towards immigration from the perspective of Muslim Europeans. Muslim attitudes are compared to Christian and secular majority populations in eight European countries with large/moderate Muslim populations. We show that income has a weaker effect on Muslim attitudes compared to the secular majority and that Muslims hold more favourable attitudes towards migrants from poorer countries. We argue that European Muslim attitudes towards immigration fit better with Social Identity Theory and are less a consequence of competition. We propose an additional model for understanding immigration attitudes, whereby people’s personal histories and experiences create empathy with others across national borders.
A NEW PARADIGM FOR GAMMA-RAY BURSTS: LONG-TERM ACCRETION RATE MODULATION BY AN EXTERNAL ACCRETION DISK

We present a new way of looking at the very long-term evolution of gamma-ray bursts (GRBs) in which the disk of material surrounding the putative black hole powering the GRB jet modulates the mass flow, and hence the efficacy of the process that extracts rotational energy from the black hole and inner accretion disk. The pre-Swift paradigm of achromatic, shallow-to-steep “breaks” in the long-term GRB light curves has not been borne out by detailed Swift data amassed in the past several years. We argue that, given the initial existence of a fall-back disk near the progenitor, an unavoidable consequence will be the formation of an “external disk” whose outer edge continually moves to larger radii due to angular momentum transport and lack of a confining torque. The mass reservoir at large radii moves outward with time and gives a natural power-law decay to the GRB light curves. In this model, the different canonical power-law decay segments in the GRB identified by Zhang et al. and Nousek et al. represent different physical states of the accretion disk. We identify a physical disk state with each power-law segment.
A surface coordination network based on copper adatom trimers.

Surface coordination networks formed by co-adsorption of metal atoms and organic ligands have interesting properties, for example regarding catalysis and data storage. Surface coordination networks studied to date have typically been based on single metal atom centers. The formation of a novel surface coordination network is now demonstrated that is based on network nodes in the form of clusters consisting of three Cu adatoms. The network forms by deposition of tetrahydroxybenzene (THB) on Cu(111) under UHV conditions. As shown from a combination of scanning tunneling microscopy, X-ray photoelectron spectroscopy, and density functional theory calculations, all four hydroxy groups of THB dehydrogenate upon thermal activation at 440 K. This highly reactive ligand binds to Cu adatom trimers, which are resolved by high-resolution STM. The network creates an ordered array of mono-dispersed metal clusters constituting a two-dimensional analogue of metal-organic frameworks.
Sonar Image Denoising via Adaptive Overcomplete Dictionary Based on K-SVD Algorithm

In order to remove the noise of sonar image more effectively, the adaptive over complete dictionary based on K-SVD algorithm is carried out in this paper. Given a set of training signals from noisy image, the predefined dictionary is trained so that the new dictionary leads to the best sparse representation for sonar image, but not for the noise. Experiments are provided to demonstrate the performance of the proposed method, as compared with other denoising methods. Results show that this method, which has the capability of adaptation, is particularly appealing in terms of both denoising effect and keeping details, and has improved performance over traditional methods.
Antiplatelet drugs in cardiological practice: Established strategies and new developments

A common pathophysiological course in vascular diseases is an overwhelming activation and aggregation of blood platelets, which results in atherothrombosis. By causing the last decisive step of cerebral, coronary, or peripheral arterial ischemia thrombotic complications of atherosclerotic disease represent a major player in death cause statistics of most western countries. The development of novel therapies against platelet-dependent thrombosis and the concurrent improvement of existing therapeutic strategies thus is a paramount focus of pharmaceutical research. Currently, efficiency, dosing and indications of established antiplatelet substances are being re-evaluated, whilst new, so far unrecognized molecular targets for inhibition of platelet activity come up front. This not only allows for interesting new therapeutical options, but also widens our insight into the role platelets play in atherosclerosis in general. This article summarizes the relevant pathophysiology of platelet activation, presents current concepts in antiplatelet drug therapy, and highlights the role of platelets in vascular diseases apart from atherothrombosis.
Conservative extraction of over-represented extensible motifs

MOTIVATION The discovery of motifs in biosequences is frequently torn between the rigidity of the model on the one hand and the abundance of candidates on the other. In particular, the variety of motifs described by strings that include 'don't care' (dot) patterns escalates exponentially with the length of the motif, and this gets only worse if a dot is allowed to stretch up to some prescribed maximum length. This circumstance tends to generate daunting computational burdens, and often gives rise to tables that are impossible to visualize and digest. This is unfortunate, as it seems to preclude precisely those massive analyses that have become conceivable with the increasing availability of massive genomic and protein data. Although a part of the problem is endemic, another part of it seems rooted in the various characterizations offered for the notion of a motif, that are typically based either on syntax or on statistics alone. It seems worthwhile to consider alternatives that result from a prudent combination of these two aspects in the model. RESULTS We introduce and study a notion of extensible motif in a sequence which tightly combines the structure of the motif pattern, as described by its syntactic specification, with the statistical measure of its occurrence count. We show that a combination of appropriate saturation conditions (expressed in terms of minimum number of dots compatible with a given list of occurrences) and the monotonicity of probabilistic scores over regions of constant frequency afford us significant parsimony in the generation and testing of candidate over-represented motifs. The merits of the method are documented by the results obtained in implementation, which specifically targeted protein sequence families. In all cases tested, the motif reported in PROSITE as the most important in terms of functional/structural relevance emerges among the top 30 extensible motifs returned by our algorithm, often right at the top. Of equal importance seems the fact that the sets of all surprising motifs returned in each experiment are extracted faster and come in much more manageable sizes than would be obtained in the absence of saturation constrains. AVAILABILITY This software will be available for use with the suite of tools at www.research. ibm.com/bioinformatics.
Creating socially acceptable robots: Leaning grasp configurations for object handovers from demonstrations

Object handover is a basic task that is found in many human-robot cooperation scenarios. If we are to build socially acceptable robots, we need to enable robots to perform handovers properly. In this paper, we discuss some of the social implications of proper robot-human handovers, and we focus on the challenge of determining a proper grasp configuration when handing over an object. We propose a framework that enables a robot to learn proper grasp configurations for handovers through observations. Our aim is to eliminate the need for manually specifying grasp configurations information to the robot, and allow generalization of handover grasp configurations for known objects to unknown objects. We are currently implementing our proposed framework onto an HRP4R robot, and we discuss about our plans for conducting user studies to evaluate our system upon its completion.
Performance evaluation of data distributions with load-balancing for sparse arrays

In our previous work, we have studied three data distribution schemes, Send Followed Compress (SFC), Compress Followed Send (CFS), and Encoding- Decoding (ED), for sparse arrays based on the traditional matrix representation (TMR) scheme. Since multidimensional arrays can also be represented by the extended Karnaugh map representation (EKMR) scheme, in this paper, we first apply the SFC/CFS/ED schemes based on the EKMR scheme. Then, we compare the performance of these three schemes with those based on the TMR scheme. Both theoretical analysis and experimental test were conducted. In theoretical analysis, we analyze the SFC/CFS/ED schemes based on the TMR/EKMR schemes in terms of the data distribution time and the data compression time. In experimental test, we implement these three schemes on an IBM SP2 parallel machine. The theoretical analysis and experimental results first show that the ED scheme is superior to the CFS scheme that is superior to the SFC scheme. Second, these three schemes based on the EKMR scheme outperform those based on the TMR scheme.
Conditional gradient algorithms for machine learning

We consider penalized formulations of machine learning problems with regularization penalty having conic structure. For several important learning problems, state-of-the-art optimization approaches such as proximal gradient algorithms are difficult to apply and computationally expensive, preventing from using them for large-scale learning purpose. We present a conditional gradient algorithm, with theoretical guarantees, and show promising experimental results on two largescale real-world datasets.