pinecone/core-2020-05-10-deduplication huggingface dataset:
A proactive DOS filter mechanism for delay tolerant networks

Denial of Service (DOS) attacks are a major threat faced by all types of networks. The effect of DOS in a delay tolerant network (DTN) is even more aggravated due to the scarcity of resources. Perpetrators of DOS attacks in DTN-like environments look beyond the objective of rendering a target node useless. The aim of an attacker is to cause a network-wide degradation of resources, service and performance. This can easily be achieved by exhausting node or link resources and partitioning the network. In this paper we seek to provide a proactive approach in making the DTN authentication process robust against DOS. Our aim is to make security protocols which provide mandatory DTN security services resilient to DOS attacks. The overall objective is to make it hard to launch a DOS attack and ensure the availability of DTN services. A DTN-cookie mechanism has been proposed to quickly identify and filter out illegitimate traffic
Developing a Method of Recommending E-Learning Courses Based on Students’ Learning Preferences

In designing e-learning, it is desirable that individual learner’s learning style is considered. This study proposes a way to present the information about the expected adaptability of the course, in which a student wishes to enroll, based on the student’s responses to the learning preference questionnaire administered at the beginning of the course. As the result of applying the real data to the model derived, it was confirmed that it would be possible to estimate the course adaptability before taking the course and to provide the information for the student to improve his/her course adaptability based on the student’s responses to the learning preference questionnaire.15th International Conference, KES 2011, September 12-14, 2011, Kaiserslautern, German
Subsumption architecture for enabling strategic coordination of robot swarms in a gaming scenario

The field of swarm robotics breaks away from traditional research by maximizing the performance of a group - swarm - of limited robots instead of optimizing the intelligence of a single robot. Similar to current-generation strategy video games, the player controls groups of units - squads - instead of the individual participants. These individuals are rather unintelligent robots, capable of little more than navigating and using their weapons. However, clever control of the squads of autonomous robots by the game players can make for intense, strategic matches. 
The gaming framework presented in this article provides players with strategic coordination of robot squads. The developed swarm intelligence techniques break up complex squad commands into several commands for each robot using robot formations and path finding while avoiding obstacles. These algorithms are validated through a 'Capture the Flag' gaming scenario where a complex squad command is split up into several robot commands in a matter of milliseconds
Portfolio Optimization Using SPEA2 with Resampling

Proceeding of: Intelligent Data Engineering and
Automated Learning – IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011The subject of financial portfolio optimization under real-world constraints is a difficult problem that can be tackled using multiobjective evolutionary algorithms. One of the most problematic issues is the dependence of the results on the estimates for a set of parameters, that is, the robustness of solutions. These estimates are often inaccurate and this may result on solutions that, in theory, offered an appropriate risk/return balance and, in practice, resulted being very poor. In this paper we suggest that using a resampling mechanism may filter out the most unstable. We test this idea on real data using SPEA2 as optimization algorithm and the results show that the use of resampling increases significantly the reliability of the resulting portfolios.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10- UC3M/TIC-5029).Publicad
Decoding Phase-based Information from SSVEP Recordings with Use of Complex-Valued Neural Network

In this paper, we report on decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings with use of complex-valued neural network. The networks of this kind has inputs and output well fitted for the considered task. The dependency of the decoding accuracy from the number of targets and the decoding window size is discussed. Comparing the existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for bigger amount of target classes and sufficient length of time window used in the decoding procedure. The necessity of the proper frequency selection for each subject is discussed.status: publishe
Using the clustering coefficient to guide a genetic-based communities finding algorithm

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23878-9_20Proceedings of 12th International Conference IDEAL, Norwich, UK, September 7-9, 2011.We describe an approach taken for automatically associating entries from an on-line encyclopedia with concepts in an ontology or a lexical semantic network. It has been tested with the Simple English Wikipedia and WordNet, although it can be used with other resources. The accuracy in disambiguating the sense of the encyclopedia entries reaches 91.11% (83.89% for polysemous words). It will be applied to enriching ontologies with encyclopedic knowledge
A two-armed bandit collective for examplar based mining of frequent itemsets with applications to intrusion detection

Chapter from the book: Computational Collective Intelligence. Technologies and Applications. Also available from the publisher at SpringerLink: http://dx.doi.org/10.1007/978-3-642-23935-9_7Over the last decades, frequent itemset mining has become a major area of research, with applications including indexing and similarity search, as well as mining of data streams, web, and software bugs. Although several efficient techniques for generating frequent itemsets with a minimum support (frequency) have been proposed, the number of itemsets produced is in many cases too large for effective usage in real-life applications. Indeed, the problem of deriving frequent itemsets that are both compact and of high quality, remains to a large degree open. In this paper we address the above problem by posing frequent itemset mining as a collection of interrelated two-armed bandit problems. In brief, we seek to find itemsets that frequently appear as subsets in a stream of itemsets, with the frequency being constrained to support granularity requirements. Starting from a randomly or manually selected examplar itemset, a collective of Tsetlin automata based two-armed bandit players aims to learn which items should be included in the frequent itemset. A novel reinforcement scheme allows the bandit players to learn this in a decentralized and on-line manner by observing one itemset at a time. Since each bandit player learns simply by updating the state of a finite automaton, and since the reinforcement feedback is calculated purely from the present itemset and the corresponding decisions of the bandit players, the resulting memory footprint is minimal. Furthermore, computational complexity grows merely linearly with the cardinality of the examplar itemset. The proposed scheme is extensively evaluated using both artificial data as well as data from a real-world network intrusion detection application. The results are conclusive, demonstrating an excellent ability to find frequent itemsets at various level of support. Furthermore, the sets of frequent itemsets produced for network instrusion detection are compact, yet accurately describe the different types of network traffic present
FPGA-based true random number generation using circuit metastability with adaptive feedback control

The paper presents a novel and efficient method to generate true random numbers on FPGAs by inducing metastability in bi-stable circuit elements, e.g. flip-flops. Metastability is achieved by using precise programmable delay lines (PDL) that accurately equalize the signal arrival times to flip-flops. The PDLs are capable of adjusting signal propagation delays with resolutions higher than fractions of a pico second. In addition, a real time monitoring system is utilized to assure a high degree of randomness in the generated output bits, resilience against fluctuations in environmental conditions, as well as robustness against active adversarial attacks. The monitoring system employs a feedback loop that actively monitors the probability of output bits; as soon as any bias is observed in probabilities, it adjusts the delay through PDLs to return to the metastable operation region. Implementation on Xilinx Virtex 5 FPGAs and results of NIST randomness tests show the effectiveness of our approach.13th International Workshop, Nara, Japan, September 28 – October 1, 2011. Proceeding
Lightweight and secure PUF key storage using limits of machine learning

A lightweight and secure key storage scheme using silicon Physical Unclonable Functions (PUFs) is described. To derive stable PUF bits from chip manufacturing variations, a lightweight error correction code (ECC) encoder / decoder is used. With a register count of 69, this codec core does not use any traditional error correction techniques and is 75% smaller than a previous provably secure implementation, and yet achieves robust environmental performance in 65nm FPGA and 0.13μ ASIC implementations. The security of the syndrome bits uses a new security argument that relies on what cannot be learned from a machine learning perspective. The number of Leaked Bits is determined for each Syndrome Word, reducible using Syndrome Distribution Shaping. The design is secure from a min-entropy standpoint against a machine-learning-equipped adversary that, given a ceiling of leaked bits, has a classification error bounded by ε. Numerical examples are given using latest machine learning results.13th International Workshop, Nara, Japan, September 28 – October 1, 2011. Proceeding
Improved Collision-Correlation Power Analysis on First Order Protected AES

International audienceThe recent results presented by Moradi et al. on AES at CHES 2010 and Witteman et al. on square-and-multiply always RSA exponentiation at CT-RSA 2011 have shown that collision-correlation power analysis is able to recover the secret keys on embedded implementations. However, we noticed that the attack published last year by Moradi et al. is not eﬃcient on correctly ﬁrst-order protected implementations. We propose in this paper improvements on collision-correlation attacks which require less power traces than classical second-order power analysis techniques. We present here two new methods and show in practice their real eﬃciency on two ﬁrst-order protected AES implementations. We also mention that other symmetric embedded algorithms can be targeted by our new techniques
Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals
Generalized Bayesian pursuit: A novel scheme for multi-armed Bernoulli bandit problems

Published version of a chapter in the book: IFIP Advances in Information and Communication Technology. Also available from the publisher at: http;//dx.doi.org/10.1007/978-3-642-23960-1_16In the last decades, a myriad of approaches to the multi-armed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, while UCB-Tuned and the ε -greedy class of algorithms can be seen as state-of-the-art regret minimizing algorithms. Recently, however, the Bayesian Learning Automaton (BLA) outperformed all of these, and other schemes, in a wide range of experiments. Although seemingly incompatible, in this paper we integrate the foundational learning principles motivating the design of the BLA, with the principles of the so-called Generalized Pursuit algorithm (GPST), leading to the Generalized Bayesian Pursuit algorithm (GBPST). As in the BLA, the estimates are truly Bayesian in nature, however, instead of basing exploration upon direct sampling from the estimates, GBPST explores by means of the arm selection probability vector of GPST. Further, as in the GPST, in the interest of higher rates of learning, a set of arms that are currently perceived as being optimal is pursued to minimize the probability of pursuing a wrong arm. It turns out that GBPST is superior to GPST and that it even performs better than the BLA by controlling the learning speed of GBPST. We thus believe that GBPST constitutes a new avenue of research, in which the performance benefits of the GPST and the BLA are mutually augmented, opening up for improved performance in a number of applications, currently being tested
The impact of sampling and rule set size on generated fuzzy inference system predictive accuracy: analysis of a software engineering data set

Abstract. Software project management makes extensive use of predictive modeling to estimate product size, defect proneness and development effort. Although uncertainty is acknowledged in these tasks, fuzzy inference systems, designed to cope well with uncertainty, have received only limited attention in the software engineering domain. In this study we empirically investigate the impact of two choices on the predictive accuracy of generated fuzzy inference systems when applied to a software engineering data set: sampling of observations for training and testing; and the size of the rule set generated using fuzzy c-means clustering. Over ten samples we found no consistent pattern of predictive performance given certain rule set size. We did find, however, that a rule set compiled from multiple samples generally resulted in more accurate predictions than single sample rule sets. More generally, the results provide further evidence of the sensitivity of empirical analysis outcomes to specific model-building decisions
A software platform for evolutionary computation with pluggable parallelism and quality assurance

This paper proposes the Java Evolutionary Computation Library
(JECoLi), an adaptable, flexible, extensible and reliable software
framework implementing metaheuristic optimization algorithms, using
the Java programming language. JECoLi aims to offer a solution suited
for the integration of Evolutionary Computation (EC)-based approaches
in larger applications, and for the rapid and efficient benchmarking of
EC algorithms in specific problems. Its main contributions are (i) the implementation
of pluggable parallelization modules, independent from the
EC algorithms, allowing the programs to adapt to the available hardware
resources in a transparent way, without changing the base code; (ii) a
flexible platform for software quality assurance that allows creating tests
for the implemented features and for user-defined extensions. The library
is freely available as an open-source project.Fundação para a Ciência e a Tecnologia (FCT) -  PTDC/EIA-EIA/115176/2009, Programa COMPET
Uncertainty handling in quantitative BDD-based fault-tree analysis by interval computation

In fault-tree analysis probabilities of failure of components are often assumed to be precise. However this assumption is seldom verified in practice. There is a large literature on the computation of the probability of the top (dreadful) event of the fault-tree, based on the representation of logical formulas in the form of a binary decision diagram (BDD). When probabilities of atomic propositions are ill-known and modelled by intervals, BDD-based algorithms no longer apply to the computation of the top probability interval. This paper investigates this question, and proposes an approach based on interval methods, relying on the analysis of the structure of the Boolean formula representing the fault-tree. The considered application deals with the reliability of aircraft operations
Weak and strong disjunction in possibilistic ASP

Possibilistic answer set programming (PASP) unites answer set programming (ASP) and possibilistic logic (PL) by associating cer- tainty values with rules. The resulting framework allows to combine both non-monotonic reasoning and reasoning under uncertainty in a single framework. While PASP has been well-studied for possibilistic definite and possibilistic normal programs, we argue that the current semantics of possibilistic disjunctive programs are not entirely satisfactory. The problem is twofold. First, the treatment of negation-as-failure in existing approaches follows an all-or-nothing scheme that is hard to match with the graded notion of proof underlying PASP. Second, we advocate that the notion of disjunction can be interpreted in several ways. In particu- lar, in addition to the view of ordinary ASP where disjunctions are used to induce a non-deterministic choice, the possibilistic setting naturally leads to a more epistemic view of disjunction. In this paper, we propose a semantics for possibilistic disjunctive programs, discussing both views on disjunction. Extending our earlier work, we interpret such programs as sets of constraints on possibility distributions, whose least specific solutions correspond to answer sets
Get Involved in an Interactive Virtual Tour of Brest Harbour: Follow the Guide and Participate

International audienceRecent cultural heritage applications have been based on rich-content virtual environment (VE), in which virtual humans can communicate with visitors and other agents using natural language (NL). The conceptualisation of these dialogues are dependent on the contents of the application. Hence, we propose to use the semantic modelling of the VE and the agents' activities for the conceptualisation of the dialogue. Meta-level semantic information are used as arguments in NLU/NLG rules. The advantage of this approach is that the dialogue rules are independent from the contents of the application and have clear semantics. We applied these principles to develop Brest'Coz, an interactive virtual tour for the learning of shipbuilding techniques used in France in early 18 th century
Sharing emotions and space - empathy as a basis for cooperative spatial interaction

Empathy is believed to play a major role as a basis for humans’ cooperative behavior. Recent research shows that humans empathize with each other to different degrees depending on several modulation factors including, among others, their social relationships, their mood, and the situational context. In human spatial interaction, partners share and sustain a space that is equally and exclusively reachable to them, the so-called interaction space. In a cooperative interaction scenario of relocating objects in interaction space, we introduce an approach for triggering and modulating a virtual humans cooperative spatial behavior by its degree of empathy with its interaction partner. That is, spatial distances like object distances as well as distances of arm and body movements while relocating objects in interaction space are modulated by the virtual human’s degree of empathy. In this scenario, the virtual human’s empathic emotion is generated as a hypothesis about the partner’s emotional state as related to the physical effort needed to perform a goal directed spatial behavior.
Towards Responsive Open Learning Environments: the ROLE Interoperability framework

In recent years, research on mash-up technologies for learning environments has gained interest. The overall goal is to enrich or replace traditional learning management systems (LMS) with mash-ups of widgets and services that can be easily combined and configured to fit the learner needs. This paper presents the implemented prototype of the ROLE interoperability framework and a business and an educational case study. The framework provides a common technical infrastructure to assemble widgets and services in Personal Learning Environments (PLEs). Evaluation results indicate that the perceived usefulness and usability is high for one case study in which a mature LMS was enriched with ROLE technology. In the second case study, an early mash-up prototype was deployed. The usefulness and usability of this early prototype were rated low, but the case study provides interesting insights for further research and development.status: publishe
Framework for contextualized learning ecosystems

Proceedings of: 6th European Conference of Technology Enhanced Learning, EC-TEL 2011, Palermo, Italy, September 20-23, 2011Using mobile personal devices to interact with pervasive smart learning objects and services that create contextualized learning ecosystems can
enhance both the learning outcomes and the motivational states of students.
This paper defines and analyzes several pervasive learning ecosystems in which
students at the Carlos III University of Madrid interact with contextualized
learning objects and services. The technology defining the contextualized
learning environments is first introduced and later used in two user
experiments. These experiments provide data both about the learning outcomes
for students after interacting with smart learning objects and services and about
the motivational impact that the use of these technologies have on themThe research leading to these results has been partially funded
by the ARTEMISA project TIN2009-14378-C02-02 within the Spanish "Plan
Nacional de I+D+I", the Madrid regional community projects S2009/TIC-1650 and
CCG10-UC3M/TIC-4992 and the SOLITE CYTED Program 508AC0341. Thanks to
INNOVISION for providing the NFC tags for this experiment. Gustavo Ramirez-
Gonzalez is funded by the EU Programme Alban, scholarship number
E06D101768CO and by the Universidad del Cauca
On the use of learning object metadata: the GLOBE experience

Since IEEE LTSC LOM was published in 2002, it is one of the widest adopted standard for the description of educational resources. The GLOBE (Global Learning Objects Brokered Exchange) alliance enables share and reuse between several Learning Object Repositories worldwide. Being the largest and more diverse collection of Learning Object Metadata, it is an ideal place to perform an analysis of the actual use of the LOM standard in the real world. This paper presents an in-depth analysis of the use and quality of 630.317 metadata instances.status: publishe