ppxscal/embedding_network huggingface dataset:
Efficiently learning the accuracy of labeling sources for selective sampling
Authors: Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources ('oracles' or 'experts') with different but unknown reliabilities? With the recent advent of inexpensive and scalable online annotation tools, such as Amazon's Mechanical Turk, the labeling process has become more vulnerable to noise - and without prior knowledge of the accuracy of each individual labeler. This paper addresses exactly such a challenge: how to jointly learn the accuracy of labeling sources and obtain the most informative labels for the active learning task at hand minimizing total labeling effort. More specifically, we present IEThresh (Interval Estimate Threshold) as a strategy to intelligently select the expert(s) with the highest estimated labeling accuracy. IEThresh estimates a confidence interval for the reliability of each expert and filters out the one(s) whose estimated upper-bound confidence interval is below a threshold - which jointly optimizes expected accuracy (mean) and need to better estimate the expert's accuracy (variance). Our framework is flexible enough to work with a wide range of different noise levels and outperforms baselines such as asking all available experts and random expert selection. In particular, IEThresh achieves a given level of accuracy with less than half the queries issued by all-experts labeling and less than a third the queries required by random expert selection on datasets such as the UCI mushroom one. The results show that our method naturally balances exploration and exploitation as it gains knowledge of which experts to rely upon, and selects them with increasing frequency.
Tuning the light by nanostructures
Authors: Aurelie Lando, Branko Kolaric
Venue: Proceedings of the 1st WSEAS international conference on Nanotechnology (2009)

Here we present a comparative study of the tuning a fluorescence emission by different nanostructures, photonic crystal and nanofractals. Both systems are shown to be capable of molding the fluorescence emission of embedded emitters, through very different mechanisms in the case of photonic crystal by the presence of a band gap i.e the local density of optical states (LDOSs) and by presence of "hot spot" in the case of nanofractals. Finally, we highlighted a new promising field of research in which combining the properties of both fractals and photonic crystals can lead to new optical materials and in particular the achievement of a full bandgap in the visible range.
On countermeasures of worm attacks over the internet
Authors: Wei Zhao, Riccardo Bettati
Venue:  (2008)

Worm attacks have always been considered dangerous threats to the Internet since they can infect a large number of computers and consequently cause large-scale service disruptions and damage. Thus, research on modeling worm attacks, and defenses against them, have become vital to the field of computer and network security. This dissertation intends to systematically study two classes of countermeasures against worm attacks, known as traffic-based countermeasure and non-traffic based countermeasure. Traffic-based countermeasures are those whose means are limited to monitoring, collecting, and analyzing the traffic generated by worm attacks. Non-traffic based countermeasures do not have such limitations. For the traffic-based countermeasures, we first consider the worm attack that adopts feedback loop-control mechanisms which make its overall propagation traffic behavior similar to background non-worm traffic and circumvent the detection. We also develop a novel spectrum-based scheme to achieve highly effective detection performance against such attacks. We then consider worm attacks that perform probing traffic in a stealthy manner to obtain the location infrastructure of a defense system and introduce an information-theoretic based framework to obtain the limitations of such attacks and develop corresponding countermeasures. For the non-traffic based countermeasures, we first consider new unseen worm attacks and develop the countermeasure based on mining the dynamic signature of worm programs&rsquo; run-time execution. We then consider a generic worm attack that dynamically changes its propagation patterns and develops integrated countermeasures based on the attacker&rsquo;s contradicted objectives. Lastly, we consider the real-world system setting with multiple incoming worm attacks that collaborate by sharing the history of their interactions with the defender and develop a generic countermeasure based on establishing the defender&rsquo;s reputation of toughness in its repeated interactions with multiple incoming attackers to optimize the long-term defense performance. This dissertation research has broad impacts on Internet worm research since this work is fundamental, practical and extensible. Our developed framework can be used by researchers to understand key features of other forms of new worm attacks and develop countermeasures against them.
Spatial Relations Analysis by Using Fuzzy Operators
Authors: Nadeem Salamat, El-Hadi Zahzah
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Spatial relations play important role in computer vision, scene analysis, geographic information systems (GIS) and content based image retrieval. Analyzing spatial relations by Force histogram was introduced by Miyajima et al [1] and largely developed by Matsakis [2] who used a quantitative representation of relative position between 2D objects. Fuzzy Allen relations are used to define the fuzzy topological relations between different objects and to detect object positions in images. Concept for combined extraction of topological and directional relations by using histogram was developed by J.Malki and E.Zahzah [3], and further improved by Matsakis [4]. This algorithm has high computational and temporal complexity due to its limitations of object approximations. In this paper fuzzy aggregation operators are used for information integration along with polygonal approximation of objects. This approach gives anew, with low temporal and computational complexity of algorithm for the extraction of topological and directional relations.
New simple and efficient heuristics for the uncapacitated single allocation hub location problem
Authors: Marcos Roberto Silva, Claudio B. Cunha
Venue: Computers and Operations Research (2009)

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs.
Facial recognition system as a maritime security tool
Authors: Jyri Rajamäki, Tuomas Turunen, Aki Harju, Miia Heikkilä, Maarit Hilakivi, Sami Rusanen
Venue: Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science (2009)

The facial recognition system (FRS) requires camera(s) and a control device; a computer with special software. The computer processes the material, face images, collected by the cameras. FRS has been used as monitoring and controlling tool in major events and border crossings. The aim of FRS is to maintain and improve safety and security in a cost efficient way by saving manpower. However, FRS is an additional security tool and therefore not to be trusted only. FRS is being used mainly as a verification method where the human face functions as an access or pin code. Optimal operational environment for FRS is a dry environment with stable illumination; most likely indoor environment is needed to guarantee the operational ability. Images of the faces should be collected in close distance and the persons, who are to be identified, should cooperate. FRS is composition of technical elements and applications which are commonly used in everyday life. Profiling the environment and setting reasonable aims, FRS could be used in various places. Hence FRS is challenging the traditional methods as a sophisticated security tool for the sophisticated situations. So far, the only operational FRS in Finland started in summer 2008 at Helsinki-Vantaa airport. This paper examines and collects experiences from the airport pilot project, from literature and by interviewing researchers of the facial recognition field. The aim of the paper is to specify the desired goal state, how FRS could be applied as a new maritime security tool.
Ontology learning from Italian legal texts
Authors: Alessandro Lenci, Simonetta Montemagni, Vito Pirrelli, Giulia Venturi
Venue: Proceeding of the 2009 conference on Law, Ontologies and the Semantic Web: Channelling the Legal Information Flood (2009)

The paper reports on the methodology and preliminary results of a case study in automatically extracting ontological knowledge from Italian legislative texts. We use a fully--implemented ontology learning system (T2K) that includes a battery of tools for Natural Language Processing (NLP), statistical text analysis and machine language learning. Tools are dynamically integrated to provide an incremental representation of the content of vast repositories of unstructured documents. Evaluated results, however preliminary, show the great potential of NLP--powered incremental systems like T2K for accurate large--scale semi--automatic extraction of legal ontologies.
Beyond the user: A review of socially aware computing
Authors: Jordi Vitrià
Venue: Proceeding of the 2007 conference on Artificial Intelligence Research and Development (2007)

Social cognition is the main channel through which human beings access the social world, very much like vision and hearing are channels through which people access the physical world. For a long time, researchers have addressed the computational implementation of vision and hearing in domains like computer vision and speech recognition, but only early attempts have been made to do the same with social cognition. In other words: machines are beginning to be effective in handling perceptual aspects of the physical world, but up to now are devoid of social context.
A Novel Framework for Case-Based Decision Analysis
Authors: Ning Xiong, Peter Funk
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

Case-based reasoning (CBR) and decision analysis have been two separate research areas aiming to solve problems from different perspectives. CBR is powerful to offer solutions to problems by reusing previous experiences, while decision theory exhibits its strength in dealing with uncertain, nondeterministic situations subject to likelihoods, risks, and probable consequences. In this paper, we present a novel framework of integrating CBR and decision analysis for the purpose of case-based decision analysis. CBR is employed as a methodology to reason from previous cases for building a decision model given the current situation, while decision theory is applied to the decision model learnt from previous cases to identify the most promising, secured, and rational choices. In such a way, we take advantage of both the ability of CBR to learn without domain knowledge and the strength of decision theory to analyze under uncertainty.
Attacks on public WLAN-based positioning systems
Authors: Nils Ole Tippenhauer, Kasper Bonne Rasmussen, Christina Pöpper, Srdjan Čapkun
Venue: Proceedings of the 7th international conference on Mobile systems, applications, and services (2009)

In this work, we study the security of public WLAN-based positioning systems. Specifically, we investigate the Skyhook positioning system, available on PCs and used on a number of mobile platforms, including Apple's iPod touch and iPhone. By implementing and analyzing several kinds of attacks, we demonstrate that this system is vulnerable to location spoofing and location database manipulation. In both, the attacker can arbitrarily change the result of the localization at the victim device, by either impersonating remote infrastructure or by tampering with the service database. Our attacks can easily be replicated and we conjecture that--without appropriate countermeasures--public WLAN-based positioning should therefore be used with caution in safety-critical contexts. We further discuss several approaches for securing WLAN-based positioning systems.
Measures of self-contradiction on Atanassov's intuitionistic fuzzy sets: An axiomatic model
Authors: Elena E. Castiñeira, Susana Cubillo
Venue: International Journal of Intelligent Systems (2009)

Trillas et al. (Soft Comput 1999;3(4):197&ndash;199 and In: Proc 18th Int Conf of the North American Fuzzy Information Processing Society (NAFIPS), New York;1999; pp 28&ndash;32) introduced the concepts of self-contradictory fuzzy set and contradictory fuzzy sets in an attempt to mark out when an inference process is not coherent. Later, contradiction was studied along the same lines in Cubillo and Casti&ntilde;eira (In: Proc X Conf of Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Perugia (Italy); (2004). 2180&ndash;2186) within the framework of Atanassov's intuitionistic fuzzy sets (AIFSs). The aim of this paper is to axiomatically model self-contradiction measures on AIFSs. After introducing some functions to measure negation-dependent or -independent degrees of self-contradiction of an AIFS in Casti&ntilde;eira, Cubillo, and Torres (Mathware Soft-Comput 2006;13:139&ndash;156), a preliminary axiomatic model for measuring the self-contradiction of AIFSs was presented in Casti&ntilde;eira et al. (In: Proc XI Conf of Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2006), Paris (France); 2006. pp 2391&ndash;2398). Being a very early model, it turned out to be incomplete. For this reason, this paper takes up the study started in Casti&ntilde;eira et al. (2006) again with a view to filling up its gaps. Here, we present a more complete model that envisages the continuity of self-contradiction measures from a broader perspective. The concepts of semicontinuous and completely semicontinuous, from both below and above, are now introduced, and a classification result is shown. &copy; 2009 Wiley Periodicals, Inc.
Highlight on a Feature Extracted at Fine Scales: The Pointwise Lipschitz Regularity
Authors: Christophe Damerval, Sylvain Meignen
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

The aim of this paper is to study the robustness of the pointwise Lipschitz regularity in 2D, which is a measure of the local regularity of the intensity function associated to an image. This regularity can be efficiently computed by an approach based on fine scales. We assess its robustness when the image undergoes various transformations, especially geometric ones. The results we obtain show that the pointwise Lipschitz regularity is a suitable feature for applications in computer vision.
A Formal Representation for Interactivity in Asynchronous Communication
Authors: Gyo Sik Moon
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

Computer-Mediated Communication (CMC) based on electronic bulletin board system (e-BBS) has been widely used for various educational purposes since the advent of the Internet. Instructors can achieve pedagogical goals by analyzing the contents of postings. They can be benefited from analytic information on discussion activities. To do that, we develop a formal model to represent and analyze communication activities formally. The model consists of interactivity vector providing a formal representation of discussion activities and interactivity chart providing a graphical representation of interactivity vector. The formal approach can be used as a technical assistance to analyze and compare discussion groups.
Parallel identification of the spelling variants in corpora
Authors: Martin Reynaert
Venue: Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data (2009)

We present a new approach based on anagram hashing to globally handle the typographical variation in large and possibly noisy text collections. Typographical variation is typically handled in a local fashion: given one particular text string some system of retrieving near-neighbours is applied, where near-neighbours are other text strings that differ from the particular string by a given number of characters. The difference in characters between the original string and one of its retrieved near-neighbours we call a particular character confusion. We present a global way of performing this action: given a possible particular character confusion, we identify - in parallel, i.e. in one single operation on anagram-hash derived bit vectors - all the pairs of text strings in the text collection to which the particular confusion applies. The algorithm proposed here is evaluated on about 23,000 English attested typos from the Reuters rcv1 text collection. We further explore its usefulness for unsupervised linking of a historical Dutch word list to its contemporary counterpart.
Improving performance of neural classifiers via selective reduction of target levels
Authors: I. Mora-Jiménez, A. R. Figueiras-Vidal
Venue: Neurocomputing (2009)

Reducing the level of the targets corresponding to training samples for a machine classifier using the outputs of an auxiliary classifier is interesting because it allows to save expressive power unnecessarily dedicated to increase the output level of well-classified samples. In this paper we propose an iterative form of this selective reduction of target levels with a simple linear reduction schedule. Extensive simulations show that the proposed method has not only a performance better than or equal to conventional training or using static versions of the reduction, but also with respect to support vector machines (SVM). This potential advantage is accompanied by a smaller size and a design effort not much higher than the corresponding SVM, thus making the proposed method very attractive for practical applications.
Sparsity-certifying Graph Decompositions
Authors: Ileana Streinu, Louis Theran
Venue: Graphs and Combinatorics (2009)

We describe a new algorithm, the (k, &#x2113;)-pebble game with colors, and use it to obtain a characterization of the family of (k, &#x2113;)-sparse graphs and algorithmic solutions to a family of problems concerning tree decompositions of graphs. Special instances of sparse graphs appear in rigidity theory and have received increased attention in recent years. In particular, our colored pebbles generalize and strengthen the previous results of Lee and Streinu [12] and give a new proof of the Tutte-Nash-Williams characterization of arboricity. We also present a new decomposition that certifies sparsity based on the (k, &#x2113;)-pebble game with colors. Our work also exposes connections between pebble game algorithms and previous sparse graph algorithms by Gabow [5], Gabow and Westermann [6] and Hendrickson [9].
An Empirical Analysis of the Complexity of Model-Based Diagnosis
Authors: Gregory Provan
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

We empirically study the computational complexity of diagnosing systems with real-world structure. We adopt the structure specified by a small-world network, which is a graphical structure that is common to a wide variety of naturally-occurring systems, ranging from biological systems, the WWW, to human-designed mechanical systems. We randomly generate a suite of digital circuit models with small-world network structure, and show that diagnosing these models is computationally hard.
Understanding Online Instructional Modeling: Theories and Practices
Authors: Robert Zheng, Robert Zheng, Sharmila Pixy Ferris
Venue:  (2007)

Higher education is currently undergoing significant changes, and conditions in higher education reflect changing financial, social, and political conditions, which affect both faculty and students. Both the rising costs of education and changes from brick-and-mortar to technologically-driven programs often lead to a change from the traditional space-and-time bound institution to ones that offer cost-effective technologically enhanced programs. Online learning has become an integral and expansive factor in higher education both in distance learning and as an adjunct to the traditional classroom. Understanding Online Instructional Modeling: Theories and Practices focuses on both theoretical and practical aspects of online learning by introducing a variety of online instructional models as well as best practices that help educators and professional trainers to better understand the dynamics of online learning.
Intraoperative Visualization of Anatomical Targets in Retinal Surgery
Authors: Ioana N. Fleming, Sandrine Voros, Balazs Vagvolgyi, Zach Pezzementi, Jim Handa, Russell Taylor, Gregory D. Hager
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

Certain surgical procedures require a high degree of precise manual control within a very restricted area. Retinal surgeries are part of this group of procedures. During vitreoretinal surgery, the surgeon must visualize, using a microscope, an area spanning a few hundreds of microns in diameter and manually correct the potential pathology using direct contact, free hand techniques. In addition, the surgeon must find an effective compromise between magnification, depth perception, field of view, and clarity of view. Pre-operative images are used to locate interventional targets, and also to assess and plan the surgical procedure. This paper proposes a method of fusing information contained in pre-operative imagery, such as fundus and OCT images, with intra-operative video to increase accuracy in finding the target areas. We describe methods for maintaining, in real-time, registration with anatomical features and target areas using image processing. This registration allows us to produce information enhanced displays that ensure that the retinal surgeon is always in visual contact with his/her area of interest.