ppxscal/embedding_network huggingface dataset:
A Model for the Study of Privacy Issues in Secure Shell Connections
Authors: Maurizio Dusi, Francesco Gringoli, Luca Salgarelli
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

The Secure Shell protocol strives to protect the privacy of its users in several ways. On one hand, the strong encryption and authentication algorithms that it adopts provide guarantees that the data exchanged between two SSH endpoints remain private to third parties. On the other hand, the type of traffic that each SSH channel transports, such as e-mail, remote shell activity, etc., is also supposed to be hidden from any observer that does not possess the necessary keys. This paper introduces a simple but accurate model of the SSH channel which can be used to study the level of privacy that SSH-protected traffic can achieve with respect to the users' activities. We think that the model can facilitate several types of projects. For example, network managers can detect traffic anomalies hidden by SSH connections more easily by relying on the output of our model. Another example, which we present in this paper, is the use of this model to derive accurate fingerprints of the type of applications run through an SSH channel by simply starting from the statistics of captured clear-text traffic. Such fingerprints can then be used to detect what type of activity, i.e., what type of traffic, is going on within an SSH channel, thereby breaking user privacy.
Improving Anomaly Detection Event Analysis Using the EventRank Algorithm
Authors: Kyrre Begnum, Mark Burgess
Venue: Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management (2007)

We discuss an approach to reducing the number of events accepted by anomaly detection systems, based on alternative schemes for interest-ranking. The basic assumption is that regular and periodic usage of a system will yield patterns of events that can be learned by data-mining. Events that deviate from this pattern can then be filtered out and receive special attention. Our approach compares the anomaly detection framework from Cfengine and the EventRank algorithm for the analysis of the event logs. We show that the EventRank algorithm can be used to successfully prune periodic events from real-life data.
Climate and Biological Sensor Network
Authors: Perfecto Mariño, Fernando Pérez-Fontán, Miguel Ángel Domínguez, Santiago Otero
Venue: Proceedings of the 8th international workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation (2008)

Biological research in agriculture needs a lot of specialised electronic sensors in order to fulfil different goals, like as: climate monitoring, soil and fruit assessment, control of insects and diseases, chemical pollutants, identification and control of weeds, crop tracking, and so on. That research must be supported by consistent biological models able to simulate diverse environmental conditions, in order to predict the right human actions before risky biological damage could be irreversible. In this paper an experimental distributed network based on climatic and biological wireless sensors is described , for providing real measurements in order to validate different biological models used for viticulture applications. First, the experimental network for field automatic data acquisition is introduced , as a system based in a distributed process. Following, the design of the wireless network is explained in detail, with a previous discussion about the state-of-the-art, and some measurements for viticulture research are pointed out. Finally future developments are stated.
Dimensionality Reduction and Similarity Computation by Inner-Product Approximations
Authors: Omer Egecioglu Hakan Ferhatosmanoglu, Umit Ogras
Venue: IEEE Transactions on Knowledge and Data Engineering (2004)

As databases increasingly integrate different types of information such as multimedia, spatial, time-series, and scientific data, it becomes necessary to support efficient retrieval of multidimensional data. Both the dimensionality and the amount of data that needs to be processed are increasing rapidly. Reducing the dimension of the feature vectors to enhance the performance of the underlying technique is a popular solution to the infamous curse of dimensionality. We expect the techniques to have good quality of distance measures when the similarity distance between two feature vectors is approximated by some notion of distance between two lower-dimensional transformed vectors. Thus, it is desirable to develop techniques resulting in accurate approximations to the original similarity distance. In this paper, we investigate dimensionality reduction techniques that directly target minimizing the errors made in the approximations. In particular, we develop dynamic techniques for efficient and accurate approximation of similarity evaluations between high-dimensional vectors based on inner-product approximations. Inner-product, by itself, is used as a distance measure in a wide area of applications such as document databases. A first order approximation to the inner-product is obtained from the Cauchy-Schwarz inequality. We extend this idea to higher order power symmetric functions of the multidimensional points. We show how to compute fixed coefficients that work as universal weights based on the moments of the probability density function of the data set. We also develop a dynamic model to compute the universal coefficients for data sets whose distribution is not known. Our experiments on synthetic and real data sets show that the similarity between two objects in high-dimensional space can be accurately approximated by a significantly lower-dimensional representation.
Finding Largest Well-Predicted Subset of Protein Structure Models
Authors: Shuai Cheng Li, Dongbo Bu, Jinbo Xu, Ming Li
Venue: Proceedings of the 19th annual symposium on Combinatorial Pattern Matching (2008)

How to evaluate the quality of models is a basic problem for the field of protein structure prediction. Numerous evaluation criteria have been proposed, and one of the most intuitive criteria requires us to find a largest well-predicted subset -- a maximum subset of the model which matches the native structure [12]. The problem is solvable in O(n 7) time, albeit too slow for practical usage. We present a (1 + ¿)d distance approximation algorithm that runs in time O(n 3logn/¿ 5) for general protein structures. In the case of globular proteins, this result can be enhanced to a randomized O(nlog2 n) time algorithm with probability at least 1 ¿ O(1/n). In addition, we propose a (1 + ¿)-approximation algorithm to compute the minimum distance to fit all the points of a model to its native structure in time O(n(loglogn + log1/¿)/¿ 5). We have implemented our algorithms and results indicate our program finds much more matched pairs with less running time than TMScore, which is one of the most popular tools to assess the quality of predicted models.
Exact Pair Theorem for the ω-Enumeration Degrees
Authors: Hristo Ganchev
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

In the paper the exact pair theorem for the &omega;-enumeration degrees is proved. As a corollary an exact pair theorem involving the jump operation for the enumeration degrees is obtained. Mathematics subject classification: 03D30.
The Banknote Anti-forgery System Based on Digital Signature Algorithms
Authors: Shenghui Su, Yongquan Cai, Changxiang Shen
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

Through the combination of digital signature algorithms and in-tegrated circuit chips, the paper puts forwards a new banknote anti-forgery method which may be regarded as an information system. The paper conceives the preliminary design of the system which includes the banknote print-data editing, key management, signing-code injection, and signing-code verification four modules, and describes the function of every module. The paper analyzes the security and effectiveness of the new method, discusses curtly the digital signature algorithms with a small modulus, and points out that the thought of the banknote anti-forgery system may be extended to passport anti-forgery for antiterrorism.
Graph Matching Recombination for Evolving Neural Networks
Authors: Ashique Mahmood, Sadia Sharmin, Debjanee Barua, Md. Monirul Islam
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper presents a new evolutionary system using genetic algorithm for evolving artificial neural networks (ANNs). Existing genetic algorithms (GAs) for evolving ANNs suffer from the permutation problem. Frequent and abrupt recombination in GAs also have very detrimental effect on the quality of offspring. On the other hand, Evolutionary Programming (EP) does not use recombination operator entirely. Proposed algorithm introduces a recombination operator using graph matching technique to adapt structure of ANNs dynamically and to avoid permutation problem. The complete algorithm is designed to avoid frequent recombination and reduce behavioral disruption between parents and offspring. The evolutionary system is implemented and applied to three medical diagnosis problems - breast cancer, diabetes and thyroid. The experimental results show that the system can dynamically evolve compact structures of ANNs, showing competitiveness in performance.
A Very Compact Hardware Implementation of the MISTY1 Block Cipher
Authors: Dai Yamamoto, Jun Yajima, Kouichi Itoh
Venue: Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems (2008)

This paper proposes compact hardware (H/W) implementation for the MISTY1 block cipher, which is an ISO/IEC18033 standard encryption algorithm. In designing the compact H/W, we focused on optimizing the implementation of FO/FI functions, which are the main components of MISTY1. For this optimization, we propose two new methods; reducing temporary registers for the FO function, and shortening the critical path for the FI function. According to our logic synthesis on a 0.18-μm CMOS standard cell library based on our proposed method, the gate size is 3.95 Kgates, which is the smallest as far as we know.
A Framework for Music Content Description and Retrieval
Authors: Alberto Pinto, Goffredo Haus
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

The recently approved format for music content description IEEE PAR1599 (MX) defines a standard for retrieval models representation within music and audio/video formats that makes use of XML documents as content descriptors. We show how music/audio semantics can be represented within the Structural layer of MX through the introduction of novel Music Information Retrieval (MIR) objects in order to embed metadata relative to specific retrieval models.
Integration of Semantically Annotated Data by the KnoFuss Architecture
Authors: Andriy Nikolov, Victoria Uren, Enrico Motta, Anne Roeck
Venue: Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns (2008)

Most of the existing work on information integration in the Semantic Web concentrates on resolving schema-level problems. Specific issues of data-level integration (instance coreferencing, conflict resolution, handling uncertainty) are usually tackled by applying the same techniques as for ontology schema matching or by reusing the solutions produced in the database domain. However, data structured according to OWL ontologies has its specific features: e.g., the classes are organized into a hierarchy, the properties are inherited, data constraints differ from those defined by database schema. This paper describes how these features are exploited in our architecture KnoFuss, designed to support data-level integration of semantic annotations.
Projection-Field-Type VLSI Convolutional Neural Networks Using Merged/Mixed Analog-Digital Approach
Authors: Osamu Nomura, Takashi Morie
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

The hierarchical convolutional neural network models are considered promising for robust object detection/recognition. These models require huge computational power for performing a large number of multiply-and-accumulation (MAC) operations. In this paper, first we discuss efficient calculation schemes suitable for 2D MAC operations. Then we review the related algorithms and LSI architecture proposed in our previous work, in which we use a projection-field-type network architecture with sorting of neuron outputs by magnitude. For the LSI implementation, we adopt a merged/mixed analog-digital circuit approach using a large number of analog or pulse modulation circuits. We demonstrate the validity of our LSI architecture by testing proof-of-concept LSIs. It is essential to develop efficient and parallel A/D and D/A conversion circuits in order to connect a lot of on-chip analog circuits with the external digital system. In this paper, we also propose such an A/D conversion circuit scheme.
Clustering Likelihood Curves: Finding Deviations from Single Clusters
Authors: Claudia Hundertmark, Frank Klawonn
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

For systematic analyses of quantitative mass spectrometry data a method was developed in order to reveal peptides within a protein, that show differences in comparison with the remaining peptides of the protein concerning their regulatory characteristics. Regulatory information is calculated and visualised by a probabilistic approach resulting in likelihood curves. On the other hand the algorithm for the detection of one or more clusters is based on fuzzy clustering, so that our hybrid approach combines probabilistic concepts as well as principles from soft computing. The test is able to decide whether peptides belonging to the same protein, cluster into one or more group. In this way obtained information is very valuable for the detection of single peptides or peptide groups which can be regarded as regulatory outliers.
Self-configuring object-to-relational mapping queries
Authors: Pietu Pohjalainen, Juha Taina
Venue: Proceedings of the 6th international symposium on Principles and practice of programming in Java (2008)

Object-to-relational maps are nowadays routinely utilized in providing a persistency mechanism for object-oriented programs. We present how an object-to-relational mapper, such as Hibernate, provides transparent persistency to object-oriented programs. We then show how the existing approaches of eager and lazy fetching of associations are problematic under the face of program evolution. As an improvement, we present self-configuring components, which reflectively configure the persistency layer usage sites, thus leading to improved maintainability of software. A self-configuring component analyses the actual persistency layer usage pattern. Based on this information, the actual queries are configured. A general algorithm for retrieving the actual usage pattern is given. As an implementation mechanism, we discuss different approaches for the self-configuring components. Practical choices for implementation can vary between analysis of abstract syntax trees and Java byte-code analysis. Suitability of two byte-code analysis frameworks, namely BCEL and Soot are evaluated.
Learning User Profile with Genetic Algorithm in AmI Applications
Authors: Verónica Venturini, Javier Carbó, José M. Molina
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

In this paper, the algorithm for prediction of user preferences is described through making use of the Heuristic Genetic Algorithm. A Multi-Agent System was used to evaluate our algorithm with the goal of obtaining the user private information to determine what the user requests, in order to offer appropriate services. Additionally, a list of prioritized tasks is generated accordingly, in order to assist the user in making decisions.
People Helping Computers Helping People: Navigation for People with Mobility Problems by Sharing Accessibility Annotations
Authors: Harald Holone, Gunnar Misund
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

Accessibility maps are valuable tools for people with mobility problems navigating in the urban landscape, in particular for first time visitors. However, the costs of establishing and maintaining such maps prohibit widespread use. Moreover, smartphones, GPS positioning and a growing number of open geospatial tools and technology are becoming commodities. Letting users create and augment geospatial data is opening up for a host of novel user generated geospatial services. Maps, or more precisely, the geospatial data they depict, might for instance be used for route planning. For pedestrians, not to say people with mobility problems, such tools are scarce. In this paper, we explore the combination of accessibility maps and route planning for people with mobility problems. To overcome the cost problems of accessibility surveys, we propose a novel concept, OurWay, which allows the users to annotate the accessibility of their surroundings. This user generated content provides a basis for computing satisfactory routes, from one location to another, matching the user's preferences and needs. We present findings from two experiments, which bear evidence of the validity of the concept, and discuss its potential as a tool for surveying and route planning.
SIETTE: A Web-Based Tool for Adaptive Testing
Authors: Ricardo Conejo, Eduardo Guzmán, Eva Millán, Mónica Trella, José Luis Pérez-De-La-Cruz, Antonia Ríos
Venue: International Journal of Artificial Intelligence in Education (2004)

Student assessment is a very important issue in educational settings. The goal of this work is to develop a web-based tool to assist teachers and instructors in the assessment process. Our system is called SIETTE, and its theoretical bases are Computer Adaptive Testing and Item Response Theory. With SIETTE, teachers worldwide can define their tests, and their students can take these tests on-line. The tests are generated according to teachers' specifications and are adaptive, that is, the questions are selected intelligently to fit the student's level of knowledge. In this way, we obtain more accurate estimations of student's knowledge with significantly shorter tests. By using the computer, larger question databases can be stored, selection algorithms can be performed efficiently, and questions can include multimedia content. The use of Java applets allows the inclusion of executable content in question stem and/or answers, so the student can interact with the system by means of this applet. In this way, new possibilities are added to Computer Adaptive Tests, such as using traditional multiple-choice questions together with questions whose answer is evaluated by the applet itself.
Workshop on Algorithms and Models for the Web Graph
Authors: William Aiello, Andrei Broder, Jeannette Janssen, Evangelos Milios
Venue: Algorithms and Models for the Web-Graph: Fourth International Workshop, WAW 2006, Banff, Canada, November 30 - December 1, 2006. Revised Papers (2007)

For barely a decade now the Web graph (the network formed by Web pages and their hyperlinks) has been the focus of scientific study. In that short a time, this study has made a significant impact on research in physics, computer science and mathematics. It has focussed the attention of the scientific community on all the different kinds of networks that have arisen through technology and human activity; some speak of a "new science of networks". It has brought the computational and deductive power of computer science to the study of the complex social networks formed by inter-human relationships. And, it has given birth to new branches of research in different areas of mathematics, most notably graph theory and probability.
SCARE of an Unknown Hardware Feistel Implementation
Authors: Denis Réal, Vivien Dubois, Anne-Marie Guilloux, Frédéric Valette, Mhamed Drissi
Venue: Proceedings of the 8th IFIP WG 8.8/11.2 international conference on Smart Card Research and Advanced Applications (2008)

Physical attacks based on Side Channel Analysis (SCA) or on Fault Analysis (FA) target a secret usually manipulated by a public algorithm. SCA can also be used for Reverse Engineering (SCARE) against the software implementation of a private algorithm. In this paper, we claim that an unknown Feistel scheme with an hardware design can be recovered with a chosen plaintexts SCA attack. First, we show that whatever is the input of the unknown Feistel function, its one-round output can be guessed by SCA. Using this relation, two attacks for recovering the algorithm are proposed : an expensive interpolation attack on a generic Feistel scheme and an improved attack on a specific but commonly used scheme. Then, a countermeasure is proposed.
Supporting Learners' Organization in Collective Challenges
Authors: Patrice Moguel, Pierre Tchounikine, André Tricot
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

This article presents research that aims to construct a computer-based system that (1) supports learners in organizing themselves in the context of a mediated pedagogic collective challenge and (2) supports human tutors in monitoring the learners' process, and supporting it. We present the result of an exploratory experiment that helps in understanding the organizational dimensions of this type of learning situation, a theoretical background originating from CSCW, a prototype designed according to this theoretical background and preliminary results.
A New Super-Resolution Algorithm Based on Areas Pixels and the Sampling Theorem of Papoulis
Authors: Alain Horé, François Deschênes, Djemel Ziou
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In several application areas such as art, medicine, broadcasting and e-commerce, high-resolution images are needed. Super-resolution is the algorithmic process of increasing the resolution of an image given a set of displaced low-resolution, noisy and degraded images. In this paper, we present a new super-resolution algorithm based on the generalized sampling theorem of Papoulis and wavelet decomposition. Our algorithm uses an area-pixel model rather than a point-pixel model. The sampling theorem is used for merging a set of low-resolution images into a high-resolution image, and the wavelet decomposition is used for enhancing the image through efficient noise removing and high-frequency enhancement. The proposed algorithm is non-iterative and not time-consuming. We have tested our algorithm on multiple images and used the peak-to-noise ratio, the structural similarity index and the relative error as quality measures. The results show that our algorithm gives images of good quality.