ppxscal/embedding_network huggingface dataset:
Sensor Network Applications Implemented by Industry and Their Security Challenges
Authors: Erdal Cayirci
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

Wireless sensor networks (WSN) have many security and safety applications. BODAS, TADAS and TEDAS are three examples for WSN security applications implemented and deployed recently.BODAS detects threats against the security and safety of pipelines. TADAS is a tactical sensing system to detect and classify the intruders. It is developed for surveillance along borders, through approach routes and around critical facilities. Finally TEDAS detects the intruders passing over, through or under a perimeter fence. All three applications are based on the deployment of a large number of unattended nodes for extended time periods. Therefore, scalability and power awareness are critical design parameters for them. They are also susceptible to security threats different from typical military and commercial systems.
A Monitoring Module for a Streaming Server Transmission Architecture
Authors: Sadick Jorge Nahuz, Mario Meireles Teixeira, Zair Abdelouahab
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

The Internet has experienced a considerable increase in the use of audio and video applications, which provoke a large consumption of the resources available in the network and servers. Therefore, the monitoring and analysis of those resources has become an essential task in order to enhance the service delivered to users. This work depicts a monitoring module implemented in a video server architecture, which is used to track the transmission of some popular video formats. Our experiments have demonstrated that one of the formats delivers a performance considerably better than the other, regarding the bandwidth allocated to each user session, what reassures the importance of having such a monitoring module available in a server's architecture.
Data privacy protection in multi-party clustering
Authors: Weijia Yang, Shangteng Huang
Venue: Data Knowledge Engineering (2008)

Privacy concerns over sensitive data have become important in knowledge discovery. Usually, data owners have different levels of concerns over different data attributes, which adds complexity to data privacy. Moreover, collusion among malicious adversaries poses a severe threat to data security. In this paper, we present an efficient clustering method for distributed multi-party data sets using the orthogonal transformation and perturbation techniques. Our method allows data owners to apply different levels of privacy to different attributes. The miner, while receiving the perturbed data, can still obtain accurate clustering results. This method protects data privacy, not only in the semi-honest situation, but also in the presence of collusion. The accuracy of the mining results and the privacy levels, and their relations to the parameters in the method are analyzed. Moreover, we propose an improved version of the method to alleviate the problem with a large number of participants. Experimental results demonstrate the effectiveness of our method as compared to existing methods.
Mining Impact-Targeted Activity Patterns in Imbalanced Data
Authors: Longbing Cao, Yanchang Zhao, Chengqi Zhang
Venue: IEEE Transactions on Knowledge and Data Engineering (2008)

Impact-targeted activities are rare but lead to significant impact on the society, e.g., isolated terrorism activities may lead to a disastrous event threatening national security. Similar issues can also be seen in many other areas. Therefore, it is important to identify such particular activities before they lead to significant impact to the world. However, it is challenging to mine impact-targeted activity patterns due to its imbalanced structure. This paper develops techniques for discovering such activity patterns. First, the complexities of mining imbalanced impact-targeted activities are analyzed.We then discuss strategies for constructing impact-targeted activity sequences. Algorithms are developed to mine frequent positive-impact (P → T) and negative-impact (P → $(\bar{T})$) oriented activity patterns, sequential impact-contrasted activity patterns (P is frequently associated with both pattern P → T and P → $(\bar{T})$) in separated data sets), and sequential impact-reversed activity patterns (both P → T and PQ → $(\bar{T})$) are frequent). Activity impact modelling is also studied to quantify pattern impact on business outcomes. Social security debt-related activity data is used to test the proposed approaches. The outcomes show that they are promising for ISI applications to identify impact-targeted activity patterns in imbalanced data.
Moving from syntactic to semantic organizations using JXML2OWL
Authors: Toni Rodrigues, Pedro Rosa, Jorge Cardoso
Venue: Computers in Industry (2008)

Today's enterprises face critical needs in integrating disparate information spread over several data sources inside and even outside the organization. Most organizations already rely on XML standard to define their data models. Unfortunately, even when using XML to represent data, problems arise when it is necessary to integrate different data sources. Emerging Semantic Web technologies, such as ontologies, RDF, RDFS, and OWL, can play an important role in the semantic definition and integration of data. The purpose of our study is to present a framework to assist organizations to move from a syntactic data infrastructure defined in XML to a semantic data infrastructure using OWL. The framework supports mappings and fully automated instance transformation from syntactic data sources in XML format to a common shared global model defined by an ontology using Semantic Web technologies. The presented framework, JXML2OWL, allows organizations to automatically convert their XML data sources to a semantic model defined in OWL.
Epoch-Incremental Queue-Dyna Algorithm
Authors: Roman Zajdel
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

The basic reinforcement learning algorithm, as Q-learning, is characterized by short time-consuming single learning step, however, the number of epochs necessary to achieve the optimal policy is not satisfactory. There are many methods that reduce the number of necessary epochs, like TD(¿> 0), Dyna or prioritized sweeping, but their learning time is considerable. This paper proposes a combination of Q-learning algorithm performed in incremental mode with executed in epoch mode method of acceleration based on environment model and distance to terminal state. This approach ensures the maintenance of short time of a single learning step and high efficiency comparable with Dyna or prioritized sweeping. Proposed algorithm is compared with Q(¿)-learning, Dyna-Q and prioritized sweeping in the experiments on three maze tasks. The time-consuming learning process and number of epochs necessary to reach the terminal state is used to evaluate the efficiency of compared algorithms.
An Improved V-MDAV Algorithm for l-Diversity
Authors: Han Jian-min, Cen Ting-ting, Yu Hui-qun
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

V-MDAV algorithm is a high efficient multivariate microaggregation algorithm and the anonymity table generated by the algorithm has high data quality. But it does not consider the sensitive attribute diversity, so the anonymity table generated by the algorithm cannot resist homogeneity attack and background knowledge attack. To solve the problem, the paper proposes an improved V-MDAV algorithm, which first generates groups satisfying l-diversity, then extends these groups to the size between l and 2l-1 to achieve optimal k-partition. Experimental results indicate that the algorithm can generate anonymity table satisfying sensitive attribute diversity efficiently.
Preparing Future IT Managers for the Services Economy
Authors: Piers R. J. Campbell, Beverley G. Hope, Ahmad D. Jaffar
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

This paper presents a new postgraduate program in IT Management which contains a core component designed to equip IT professionals in the United Arab Emirates with the necessary skills to transition to the emerging services driven economy. Candidates enrolled in this program will be graduates from an IT related field who wish to develop their career path into a management role. The program is aimed to provide a solid platform for management of a diverse range of specialist IT management while exposing candidates to the changes required in shifting to the design, delivery and support of services.
Broadcast Authentication in Sensor Networks Using Compressed Bloom Filters
Authors: Yu-Shian Chen, I-Lun Lin, Chin-Laung Lei, Yen-Hua Liao
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

We propose a light-weight and scalable broadcast authentication scheme, Curtain, for sensor network. Instead of using Merkel tree to combine multiple μTESLA instance, we apply compressed Bloom filters to multiple μTESLA. Our scheme can support longer duration and prolong the self-healing property. We greatly reduce the communication overhead at the cost of allocating a moderate space in each receiver. Combing with PKC computation like ECC, our scheme can guarantee the long-term security and also mitigate energy consumption. Moreover, our methods can be extend to the situation of multiple senders, offering efficient user addition and revocation.
A New Schema Survival and Construction Theory for One-Point Crossover
Authors: Liang Ming, Yuping Wang
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

For one-point crossover, only the survival action to schema is mainly discussed in the existing schema theory. There are few works tackling the construction action to schema. Furthermore, there exist some limitations in these research results on schema construction theory. For example, the effects of the schema survival and the schema construction by crossover can not be distinguished. In order to analyze the effects of the survival and construction of crossover, respectively, a ternary representation for schema is proposed in this paper, through which the effects of the survival and construction of a schema can be easily distinguished. The effects of the schema survival and the schema construction by one-point crossover is analyzed separately. Subsequently, their united action is discussed.
Potential for collaborative caching and prefetching in largely-disconnected villages
Authors: Sibren Isaacman, Margaret Martonosi
Venue: Proceedings of the 2008 ACM workshop on Wireless networks and systems for developing regions (2008)

In a world becoming ever more reliant on the power of information, bringing data connectivity into developing regions is becoming an important way to lift these regions out of poverty by educating and informing the population. Although many of these regions are not likely to receive the infrastructure to support fully wired (or even wireless) networks, existing cellular and delay tolerant technologies allow limited connectivity. In this paper we show that usability of highly disconnected networks can be increased through collaborative caching and data prefetching techniques. We focus on decreasing the miss rate of pages fetched in both general web access as well as more specialized education applications. We evaluate our schemes by running trace-driven simulations of internet traces from Cambodia and logs from Princeton University's Blackboard courseware web servers. Our caching and prefetching strategies in these environments show improvements in miss rate of up to 90% over more traditional approaches.
A Brief Review and Comparison of Feedforward Morphological Neural Networks with Applications to Classification
Authors: Alexandre Monteiro Da Silva, Peter Sussner
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

The mathematical background of MNNs can be found in mathematical morphology (MM). Since MM can be conducted very generally in the complete lattice setting, MNNs are closely related to other lattice-based neurocomputing models.This paper reviews some important types of feedforward morphological neural networks including their mathematical background. In addition, we analyze and compare the performance of feedforward morphological models and conventional multi-layer perceptrons in some classification problems.
Optimal Accurate Minkowski Sum Approximation of Polyhedral Models
Authors: Xijuan Guo, Lei Xie, Yongliang Gao
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

This paper presents an innovational unified algorithmic framework to compute the 3D Minkowski sum of polyhedral models. Our algorithm decomposes the non-convex polyhedral objects into convex pieces, generates pairwise convex Minkowski sum, and compute their union. The framework incorporates different decomposition policies for different polyhedral models that have different attributes. We also present a more efficient and exact algorithm to compute Minkowski sum of convex polyhedra, which can handle degenerate input, and produces exact results. When incorporating the resulting Minkowski sum of polyhedra, our algorithm improves the fast swept volume methods to obtain approximate the uniting of the isosurface. We compare our framework with another available method, and the result shows that our method outperforms the former method.
Kansei evaluation based on prioritized multi-attribute fuzzy target-oriented decision analysis
Authors: Hong-Bin Yan, Van-Nam Huynh, Tetsuya Murai, Yoshiteru Nakamori
Venue: Information Sciences: an International Journal (2008)

This paper deals with Kansei evaluation focusing on consumers' psychological needs and personal taste. To do so, a preparatory study is conducted beforehand to obtain Kansei data of the products to be evaluated, in which products are assessed according to Kansei attributes by means of the semantic differential method and linguistic variables. These Kansei data are then used to generate Kansei profiles for evaluated products by means of the voting statistics. As consumers' preferences on Kansei attributes of products vary from person to person and target-oriented decision analysis provides a good description of individual preference, the target-oriented decision analysis has been used and extended to quantify how well a product meets consumers' preferences. Due to the vagueness and uncertainty of consumers' preferences, three types of fuzzy targets are defined to represent the consumers' preferences. Considering the priority order of Kansei attributes specified by consumers, a so-called prioritized scoring aggregation operator is utilized to aggregate the partial degrees of satisfaction for the evaluated products. As the aesthetic aspect plays a crucial role in human choice of traditional crafts, an application to evaluate Kanazawa gold leaf, a traditional craft in Ishikawa, Japan, has also been provided to illustrate how the proposed model works in practice.
Prediction of Enzyme Class by Using Reactive Motifs Generated from Binding and Catalytic Sites
Authors: Peera Liewlom, Thanawin Rakthanmanon, Kitsana Waiyamai
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

The purpose of this research is to search for motifs directly at binding and catalytic sites called reactive motifs, and then to predict enzyme functions from the discovered reactive motifs. The main challenge is that the data of binding, or catalytic sites is only available in the range 3.34% of all enzymes, and many of each data provides only one sequence record. The other challenge is the complexity of motif combinations to predict enzyme functions.In this paper, we introduce a unique process which combines statistics with bio-chemistry background to determine reactive motifs. It is consisting of block scan filter, mutation control, and reactive site-group define procedures. The purpose of block scan filter is to alter each 1-sequence record of binding or catalytic site, using similarity score, to produce quality blocks. These blocks are input to mutation control, where in each position of the sequences, amino acids are analyzed an extended to determine complete substitution group. Output of the mutation control step is a set of motifs for each 1-sequence record input. These motifs are then grouped using the reactive site-group define procedure to produce reactive motifs. Those reactive motifs together with known enzyme sequence dataset are used as the input to C4.5 learning algorithm, to obtain an enzyme prediction model. The accuracy of this model is checked against testing dataset. At 235 enzyme function class, the reactive motifs yield the best prediction result with C4.5 at 72.58%, better than PROSITE motifs.
On Datalog vs. LFP
Authors: Anuj Dawar, Stephan Kreutzer
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II (2008)

We show that the homomorphism preservation theorem fails for LFP, both in general and in restriction to finite structures. That is, there is a formula of LFP that is preserved under homomorphisms (in the finite) but is not equivalent (in the finite) to a Datalog program. This resolves a question posed by Atserias. The results are established by two different methods: (1) a method of diagonalisation that works only in the presence of infinite structures, but establishes a stronger result showing a hierarchy of homomorphism-preserved problems in LFP; and (2) a method based on a pumping lemma for Datalog due to Afrati, Cosmadakis and Yannakakis which establishes the result in restriction to finite structures. We refine the pumping lemma of Afrati et al. and relate it to the power of Monadic Second-Order Logic on tree decompositions of structures.
Formal Verification of Generalised State Machines
Authors: George Eleftherakis, Petros Kefalas
Venue: Proceedings of the 2008 Panhellenic Conference on Informatics (2008)

The demand for more complex software is constantly increasing while at the same time the need for reliability leads modern software engineering to use more formally based development techniques. One of the most successfully employed formalisms to address the reliability issue were Finite State Machines (FSM) but they are too simple to capture the modelling needs of modern software that normally require manipulation of non-trivial data structures. X-machines is a formal method that provides such a data structure in form of a memory. On the other hand, FSM models are suitable for verification through model checking, i.e. to prove that certain properties are satisfied by a system model. However, with existing logics, it is obscure how one can describe properties that refer to the memory structure of an X-machine. This paper describes how a new logic, namely XmCTL, which extends temporal logic with memory quantifiers, facilitates model checking of X-machine models. XmCTL is defined and its use is demonstrated through the verification of a steam-boiler system which acts as a case study for our contribution.
Brief paper: Canonical transformations used to derive robot control laws from a port-controlled Hamiltonian system perspective
Authors: Juan Ignacio Mulero-Martínez
Venue: Automatica (Journal of IFAC) (2008)

This paper addresses the problem of deriving control laws for robot manipulators in the framework of port-controlled Hamiltonian systems via canonical transformations and passivity-based control. The control design is focused on the presentation of a new energy-shaping methodology for tracking control based on the introduction of virtual non-homogeneous fields where a desired energy is defined to compensate for the actual energy of the robot manipulator while a virtual field forces the system to track a general reference trajectory. This requires use of the Legendre-Fenchel transformation and allows for the derivation standard control laws in the robotics field such as PD control with gravity compensation or PD with precompensation. Finally, the passivity of the input-output mapping of the non-autonomous Hamiltonian system is analyzed in detail, resulting in new Lyapunov candidate functions having their roots in physics.
A Combined Hardware/Software Optimization Framework for Signal Representation and Recognition
Authors: Melina Demertzi, Pedro Diniz, Mary W. Hall, Anna C. Gilbert, Yi Wang
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

This paper describes a signal recognition system that is jointly optimized from mathematical representation, algorithm dand final implementation. The goal is to exploit signal properties to jointly optimize a computation, beginning with first principles (mathematical representation) and completed with implementation. We use a BestBasis algorithm to search a large collection of orthogonal transforms derived from the Walsh-Hadamard transform to find a series of transforms which best discriminate among signal classes.The implementation exploits the structure of these matrices to compress the matrix representation, and in the process of multiplying the signal by the transform, reuse the results of prior computation and parallelize the implementation in hardware. Through this joint optimization, this dynamic, data-driven system is able to yield much more highly optimized results than if the optimizations were performed statically and in isolation. We provide results taken from applying this system to real input signals of spoken digits, and perform the initial analyses to demonstrate the properties of the transform matrices lead to optimized solutions.
Numerical solution of nonlinear Volterra-Fredholm integro-differential equations
Authors: P. Darania, K. Ivaz
Venue: Computers Mathematics with Applications (2008)

The aim of this paper is to present an efficient analytical and numerical procedure for solving the high-order nonlinear Volterra-Fredholm integro-differential equations. Our method depends mainly on a Taylor expansion approach. This method transforms the integro-differential equation and the given conditions into the matrix equation. The reliability and efficiency of the proposed scheme are demonstrated by some numerical experiments.
Optimistic-Pessimistic Q-Learning Algorithm for Multi-Agent Systems
Authors: Natalia Akchurina
Venue: Proceedings of the 6th German conference on Multiagent System Technologies (2008)

A reinforcement learning algorithm OP-Q for multi-agent systems based on Hurwicz's optimistic-pessimistic criterion which allows to embed preliminary knowledge on the degree of environment friendliness is proposed. The proof of its convergence to stationary policy is given. Thorough testing of the developed algorithm against well-known reinforcement learning algorithms has shown that OP-Q can function on the level of its opponents.
Twofold Extensions of Fuzzy Datalog
Authors: Ágnes Achs
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

In this work we present several possible extensions of fuzzy Datalog. At first the concept of fuzzy Datalog will be summarized, then its extension for intuitionistic- and interval-valued fuzzy logic is given and the concept of bipolar fuzzy Datalog is introduced.