ppxscal/embedding_network huggingface dataset:
Spectropolarimetric Imaging for Skin Characteristics Analysis
Authors: Yongqiang Zhao, Tieheng Yang, Peifeng Wei, Quan Pan
Venue: Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers (2008)

Light scattering spectra and polarization states can provide important information about skin. To analyse the mechanisms of interaction between skin and light, and the relationship between the changes of light's characteristics and the variations of skin's states, a spectropolarimetric imaging system is proposed to acquire the spectral, polarimetric and spatial properties of the skin. After acquiring the spectropolarimetric imagery, an empirical line correction method is used to analyse the polarimetric spectra differences between normal skin and skin with a chicken pox scar. To enhance the visual difference between normal skin and skin with a chicken pox scar, and the polarimetric and spectral characteristics of the skin in different states, a false colour mapping based spectropolarimetric image fusion method is proposed that combines the intensity image, the degree of linearity of the polarization image, and the phase of the polarization image, with spectral imagery. We demonstrate experimentally that this imaging system can be used to discriminate between the different skin pathological conditions efficiently.
Color Reduction Using the Combination of the Kohonen Self-Organized Feature Map and the Gustafson-Kessel Fuzzy Algorithm
Authors: Konstantinos Zagoris, Nikos Papamarkos, Ioannis Koustoudis
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

The color of the digital images is one of the most important components of the image processing research area. In many applications such as image segmentation, analysis, compression and transition, it is preferable to reduce the colors as much as possible. In this paper, a color clustering technique which is the combination of a neural network and a fuzzy algorithm is proposed. Initially, the Kohonen Self Organized Featured Map (KSOFM) is applied to the original image. Then, the KSOFM results are fed to the Gustafson-Kessel (GK) fuzzy clustering algorithm as starting values. Finally, the output classes of GK algorithm define the numbers of colors of which the image will be reduced.
An Elastoplastic Model Considering Sand Crushing
Authors: Yangping Yao, Naidong Wang, Haruyuki Yamamoto, Huilin Xing
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

The behavior of sand crushing will appear when the confining pressure is up to a certain value, which results in disappearing of the positive dilatancy of sand. Adopting a new hardening parameter with the crushing stress, an elastoplastic constitutive model considering sand crushing is proposed. Comparing the conventional triaxial compression test results with the model prediction, it shows that the proposed model can reasonably describe the dilatancy of sand from positive to negative.
Fuzzy Tree Mining: Go Soft on Your Nodes
Authors: Federico Razo Lopez, Anne Laurent, Pascal Poncelet, Maguelonne Teisseire
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

Tree mining consists in discovering the frequent subtrees from a forest of trees. This problem has many application areas. For instance, a huge volume of data available from the Internet is now described by trees (e.g. XML). Still, for several documents dealing with the same topic, this description is not always the same. It is thus necessary to mine a common structure in order to query these documents. Biology is another field where data may be described by means of trees. The problem of mining trees has now been addressed for several years, leading to well-known algorithms. However, these algorithms can hardly deal with real data in a soft manner. Indeed, they consider a subtree as fully included in the super-tree. This means that all the nodes must appear. In this paper, we extend this definition to fuzzy inclusion based on the idea that a tree is included to a certain degree within another one, this fuzzy degree being correlated to the number of matching nodes.
Imitation Learning of an Intelligent Navigation System for Mobile Robots Using Reservoir Computing
Authors: Eric A. Antonelo, Benjamin Schrauwen, Dirk Stroobandt
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome.Reservoir Computing (RC) uses a randomly created recurrent neural network (the reservoir) which functions as a temporal kernel of rich dynamics that projects the input to a high dimensional space. This projection is mapped into the desired output (only this mapping must be learned with standard linear regression methods).In this work, RC is used for imitation learning of navigation behaviors generated by an intelligent navigation system in the literature. Obstacle avoidance, exploration and target seeking behaviors are reproduced with an increase in stability and robustness over the original controller. Experiments also show that the system generalizes the behaviors for new environments.
Aspect-Based Tagging for Collaborative Media Organization
Authors: Oliver Flasch, Andreas Kaspari, Katharina Morik, Michael Wurst
Venue: From Web to Social Web: Discovering and Deploying User and Content Profiles: Workshop on Web Mining, WebMine 2006, Berlin, Germany, September 18, 2006. Revised Selected and Invited Papers (2007)

Organizing multimedia data is very challenging. One of the most important approaches to support users in searching and navigating media collections is collaborative filtering. Recently, systems as flickr or last.fm have become popular. They allow users to not only rate but also tag items with arbitrary labels. Such systems replace the concept of a global common ontology, as envisioned by the Semantic Web, with a paradigm of heterogeneous, local "folksonomies". The problem of such tagging systems is, however, that resulting taggings carry only little semantics. In this paper, we present an extension to the tagging approach. We allow tags to be grouped into aspects. We show that introducing aspects does not only help the user to manage large numbers of tags, but also facilitates data mining in various ways. We exemplify our approach on Nemoz, a distributed media organizer based on tagging and distributed data mining.
Collusion-Resistant Mechanisms with Verification Yielding Optimal Solutions
Authors: Paolo Penna, Carmine Ventre
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

A truthful mechanism consists of an algorithm augmented with a suitable payment function which guarantees that "players" cannot improve their utilities by "cheating". Mechanism design approaches are particularly appealing for designing "protocols" that cannot be manipulated by rational players.We present new constructions of so called mechanisms with verification introduced by Nisan and Ronen [STOC 1999]. We first show how to obtain mechanisms that, for single-parameter domains, are resistant to coalitions of colluding agents even in the case in which compensation among members of the coalition is allowed (i.e., n-truthful mechanisms). Based on this technique we derive a class of exact truthful mechanisms with verification for arbitrary bounded domains. This class of problems includes most of the problems studied in the algorithmic mechanism design literature and for which exact solutions cannot be obtained with truthful mechanisms without verification. This result improves over all known previous constructions of exact mechanisms with verification.
Rewriting Systems with Data
Authors: Ahmed Bouajjani, Peter Habermehl, Yan Jurski, Mihaela Sighireanu
Venue: Proceedings of the 16th international symposium on Fundamentals of Computation Theory (2007)

We introduce a uniform framework for reasoning about infinite-state systems with unbounded control structures and unbounded data domains. Our framework is based on constrained rewriting systems on words over an infinite alphabet. We consider several rewriting semantics: factor, prefix, and multiset rewriting. Constraints are expressed in a logic on such words which is parametrized by a first-order theory on the considered data domain. We show that our framework is suitable for reasoning about various classes of systems such as recursive sequential programs, multithreaded programs, parametrized and dynamic networks of processes, etc. Then, we provide generic results (1) for the decidability of the satisfiability problem of the fragment ¿ * ¿ * of this logic provided that the underlying logic on data is decidable, and (2) for proving inductive invariance and for carrying out Hoare style reasoning within this fragment. We also show that the reachability problem is decidable for a class of prefix rewriting systems with integer data.
Augmenting the Zachman Enterprise Architecture Framework with a Systemic Conceptualization
Authors: Alain Wegmann, Anders Kotsalainen, Lionel Matthey, Gil Regev, Alain Giannattasio
Venue: Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference (2008)

The Zachman Framework offers a classification of the models created in an enterprise architecture project. These models form a holistic representation of the organization. Despite the prominent position of the Framework, there is little information publicly available to help designers create exact models that fit each other. In this paper, we propose a conceptualization based on General Systems Thinking. Our conceptualization provides concrete guidelines for creating the models required by the Framework. The proposed conceptualization establishes a better understanding of the models and of their relationships. This facilitates the creation and interpretation of the models. It also improves the traceability between them. We illustrate our approach with the results of a case study.
A Fuzzy Logic-Based Approach to Two-Dimensional Warranty System
Authors: Sanghyun Lee, Dongsu Lee, Chulsu Park, Jaehee Lee, Seungbeom Park, Kyungil Moon, Byungki Kim
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Much of the literature on warranty analysis considers failure models which are indexed by a single variable, such as age or usage. However, there are situations where several characteristics are used together as criteria for judging the eligibility of a failed product. The warranty analysis characterized by a region in a two-dimensional plane with one axis representing age and the other axis representing usage is known as a "two-dimensional" warranty plan. A classical warranty plan requires crisp data obtained from strictly controlled reliability tests. However, in a real situation these requirements might not be fulfilled. In an extreme case, the warranty claims data come from users whose reports are expressed in a vague way. It might be caused by subjective and imprecise perception of failures by a user, by imprecise records of warranty data, or by imprecise records of the rate of usage. This paper suggests different tools appropriate for modeling a two-dimensional warranty plan, and a suitable fuzzy method to handle vague data.
TOPN Based Temporal Performance Evaluation Method of Neural Network Based Robot Controller
Authors: Hua Xu, Peifa Jia
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

These years, for some neural network (NN) controller based time critical systems, temporal performance is always required to be evaluated. In order to model complex time critical systems, timed hierarchical object-oriented Petri net (TOPN) has been proposed. On the base of TOPN method, this paper has proposed worst case execution time (WCET) calculation method called time accumulation effect (TAE) calculation, whose goal is to evaluate the performance of TOPN models such as NN based robot controller systems etc al. TAE method can be used to calculate the worst case execution time interval directly on the base of integral linear programming (ILP) method. The use and benefits of TAE calculation for TOPN models have also been illustrated by analyzing one robot controller system model.
An Efficient and Secure RFID Security Method with Ownership Transfer
Authors: Kyosuke Osaka, Tsuyoshi Takagi, Kenichi Yamazaki, Osamu Takahashi
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Radio Frequency Identification (RFID) has come under the spotlight as technology supporting ubiquitous society. But now, we face several security problems and challenges in RFID systems. Recent papers have reported that RFID systems have to achieve the following requirements: (1) Indistinguishability, (2) Forward Security, (3) Replay Attack resistance, (4) Tag Killing resistance, and (5) Ownership Transferability. We have to design RFID system that achieves the above-mentioned requirements. The previous security methods achieve only some of them individually, and no RFID system has been constructed that achieves all requirements. In this paper, we propose an RFID security method that achieves all requirements based on a hash function and a symmetric key cryptosystem. In addition, our proposed method provides not only high-security but also high-efficiency.
Continual collaborative planning for mixed-initiative action and interaction
Authors: Michael Brenner
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Multiagent environments are often highly dynamic and only partially observable which makes deliberative action planning computationally hard. In many such environments, however, agents can take a more proactive approach and suspend planning for partial plan execution, especially for active information gathering and interaction with others. This paper presents a new algorithm for Continual Collaborative Planning (CCP) that enables agents to deliberately interleave planning, acting, perception and communication. Our implementation of CCP has been evaluated with MAPSIM, a tool that automatically generates multiagent simulations from formal multiagent planning (MAP) domains. For different such simulations, we show how CCP leads to collaborative planning and acting and, despite minimal linguistic capabilities, to fairly natural dialogues between agents.
Fast Stereo Matching Algorithm Using Adaptive Window
Authors: Guie Luo, Xinrong Yang, Qi Xu
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Due to fixed window area-based stereo algorithms having given rise to relatively large matching errors around depth discontinuities, a adaptive window for stereo matching algorithm using integral image was proposed in this paper. A new similarity function and Four-direction line mask were employed in initial parallax gain aspect, not only to reduce noise effects but also to improve matching accuracy. Meanwhile, the window cost function that consists of average errors, variance of error and bias to larger windows was used to determine the size of matching and improve the matching accuracy. Simultaneously this algorithm using the integer image to enhance the efficiency conveniently, and the computation load having nothing to do without the window size, may reduce the timing operand is a constant. At last, the experiment result shows advantages of this algorithm, and it can be used in real-time system.
The Power of Preemption in Economic Online Markets
Authors: Lior Amar, Ahuva Mu'Alem, Jochen Stößer
Venue: Proceedings of the 5th international workshop on Grid Economics and Business Models (2008)

In distributed computer networks where resources are under decentralized control, selfish users will generally not work towards one common goal, such as maximizing the overall value provided by the system, but will instead try to strategically maximize their individual benefit. This shifts the scheduling policy in such systems --- the decision about which user may access what resource --- from being a purely algorithmic challenge to the domain of mechanism design.In this paper we will showcase the benefit of allowing preemption in such economic online settings regarding the performance of market mechanisms by extending the Decentralized Local Greedy Mechanism of Heydenreich et al. [11]. This mechanism was shown to be 3.281-competitive with respect to total weighted completion time if the players act rationally. We show that the preemptive version of this mechanism is 2-competitive. As a by-product, preemption allows to relax the assumptions on jobs upon which this competitiveness relies. In addition to this worst case analysis, we provide an in-depth empirical analysis of the average case performance of the original mechanism and its preemptive extension based on real workload traces. Our empirical findings indicate that introducing preemption improves both the utility and the slowdown of the jobs. Furthermore, this improvement does not come at the expense of low-priority jobs.
Statistical Model for Rough Set Approach to Multicriteria Classification
Authors: Krzysztof Dembczyński, Salvatore Greco, Wojciech Kotłowski, Roman Słowiński
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, Dominance-based Rough Set Approach (DRSA) has been introduced to deal with the problem of multicriteria classification. However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive, which led to the proposal of the Variable Consistency variant of DRSA. In this paper, we introduce a new approach to variable consistency that is based on maximum likelihood estimation. For two-class (binary) problems, it leads to the isotonic regression problem. The approach is easily generalized for the multi-class case. Finally, we show the equivalence of the variable consistency rough sets to the specific risk-minimizing decision rule in statistical decision theory.
A Constant-Approximate Feasibility Test for Multiprocessor Real-Time Scheduling
Authors: Vincenzo Bonifaci, Alberto Marchetti-Spaccamela, Sebastian Stiller
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

We devise the first constant-approximate feasibility test for sporadic multiprocessor real-time scheduling. We give an algorithm that, given a task system and ¿> 0, correctly decides either that the task system can be scheduled using the earliest deadline first algorithm on m speed-(2 ¿ 1/m + ¿) machines, or that the system is infeasible for m speed-1 machines. The running time of the algorithm is polynomial in the size of the task system and 1/¿. We also provide an improved bound trading off speed for additional machines.Our analysis relies on a new concept for counting the workload of an interval, that might also turn useful for analyzing other types of task systems.
Development and evaluation of an arterial adaptive traffic signal control system using reinforcement learning
Authors: Yunlong Zhang
Venue:  (2007)

This dissertation develops and evaluates a new adaptive traffic signal control system for arterials. This control system is based on reinforcement learning, which is an important research area in distributed artificial intelligence and has been extensively used in many applications including real-time control. In this dissertation, a systematic comparison between the reinforcement learning control methods and existing adaptive traffic control methods is first presented from the theoretical perspective. This comparison shows both the connections between them and the benefits of using reinforcement learning. A Neural-Fuzzy Actor-Critic Reinforcement Learning (NFACRL) method is then introduced for traffic signal control. NFACRL integrates fuzzy logic and neural networks into reinforcement learning and can better handle the curse of dimensionality and generalization problems associated with ordinary reinforcement learning methods. This NFACRL method is first applied to isolated intersection control. Two different implementation schemes are considered. The first scheme uses a fixed phase sequence and variable cycle length, while the second one optimizes phase sequence in real time and is not constrained to the concept of cycle. Both schemes are further extended for arterial control, with each intersection being controlled by one NFACRL controller. Different strategies used for coordinating reinforcement learning controllers are reviewed, and a simple but robust method is adopted for coordinating traffic signals along the arterial. The proposed NFACRL control system is tested at both isolated intersection and arterial levels based on VISSIM simulation. The testing is conducted under different traffic volume scenarios using real-world traffic data collected during morning, noon, and afternoon peak periods. The performance of the NFACRL control system is compared with that of the optimized pre-timed and actuated control. Testing results based on VISSIM simulation show that the proposed NFACRL control has very promising performance. It outperforms optimized pre-timed and actuated control in most cases for both isolated intersection and arterial control. At the end of this dissertation, issues on how to further improve the NFACRL method and implement it in real world are discussed.
Learning Node Label Controlled Graph Grammars (Extended Abstract)
Authors: Christophe Costa Florêncio
Venue: Proceedings of the 9th international colloquium on Grammatical Inference: Algorithms and Applications (2008)

Within the data mining community there has been a lot of interest in mining and learning from graphs (see [1] for a recent overview). Most work in this area has has focussed on finding algorithms that help solve real-world problems. Although useful and interesting results have been obtained, more fundamental issues like learnability properties have hardly been adressed yet. This kind of work also tends not to be grounded in graph grammar theory, even though some approaches aim at inducing grammars from collections of graphs. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
FORTRAN Anecdotes
Venue: IEEE Annals of the History of Computing (1984)

Anecdotes are an integral part of the historical record. They tell us much that cannot be conveyed in the formal documentation. Through these stories, we come as close as we ever will to re-creating the excitement of the project and to learning some vital things about the most important component of any historical event: the people who made it happen. The FORTRAN gathering was no exception. To the world, FORTRAN is a programming language. To the people who made it happen, it was an event. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player