ppxscal/embedding_network huggingface dataset:
A Learning Algorithm for the Optimum-Path Forest Classifier
Authors: João Paulo Papa, Alexandre Xavier Falcão
Venue: Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition (2009)

Graph-based approaches for pattern recognition techniques are commonly designed for unsupervised and semi-supervised ones. Recently, a novel collection of supervised pattern recognition techniques based on an optimum-path forest (OPF) computation in a feature space induced by graphs were presented: the OPF-based classifiers. They have some advantages with respect to the widely used supervised classifiers: they do not make assumption of shape/separability of the classes and run training phase faster. Actually, there exists two versions of OPF-based classifiers: OPF cpl (the first one) and OPF knn . Here, we introduce a learning algorithm for the last one and we show that a classifier can learns with its own errors without increasing its training set.
Intelligent Parking Negotiation Based on Agent Technology
Authors: Li Yang, Ma Rongguo, Wang Longfei
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

In order to solve the parking problems in the metropolises, a new approach integrating active pattern for parking guidance information system composed of parks, cars and parking information service center and trafficmanagement center is proposed. A multi-phase navigation method based on a two-layer traffic map is employed for parking routes negotiation and guidance. The system is implemented based on an extended FIPA architecture through using mobile agent, message queue, agent container and agent execution environment, making full use of the interoperability of FIPA standards and the flexibility of mobile agent. The whole service environment can automatically execute park searching, parking price negotiation, parking space booking and parking routes negotiation and guidance using the intelligence, adaptability and collaboration of agents.
Influence Analysis of Jitter on Image Quality of Remote Sensing Camera
Authors: Fan Chao, Liang Yi-tao, Guan Ai-hong
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01 (2009)

In order to analyze the influence of the satellite jitter on the image quality of the remote sensing camera, the image motion introduced by the low frequency sine jitter was given. The influence of the image motion along the roll, pitch and yaw direction on the image quality was studied chiefly. Finally, the degradation of the image quality caused by the jitter was computed concretely according to the datum come from two kinds of the emulation on the satellite kinetic jitter. The results showed that, with the increase of TDICCD integration number, the requirement of remote sensing camera for jitter amplitude is more stringent, and must be limited.
Security extensions for integrity and confidentiality in embedded processors
Authors: Austin Rogers, Aleksandar Milenković
Venue: Microprocessors Microsystems (2009)

With current trends toward embedded computer systems' ubiquitous accessibility, connectivity, diversification, and proliferation, security becomes a critical issue in embedded computer systems design and operation. Embedded computer systems are subjected to both software and physical attacks aimed at subverting system operation, extracting key secrets, or intellectual property theft. We propose several cost-effective architectural extensions suitable for mid-range to high-end embedded processors. These extensions ensure the integrity and confidentiality of both instructions and data, introducing low performance overhead (1.86% for instructions and 14.9% for data).
Energy Consumption of Fair-Access in Sensor Networks with Linear and Selected Grid Topologies
Authors: Miao Peng, Yang Xiao
Venue: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (2009)

In this paper, we investigate energy consumption of medium access control (MAC) for multi-hop sensor networks. In a wireless sensor network, one of the primary tasks of sensor nodes is to gather data and give an accurate measurement of the sensing environment. Thus, obtaining a fair amount of data from each sensor node plays a key role to achieve this objective. In this scenario, we adopt the model of a fair-access criterion which requires that sensor nodes have an equal rate of frame delivery to the base station. Then we derive tight lower bounds on network energy consumption in a cycle during which the base station successfully receives at least one original data frame from each sensor node. The unique aspect of this study is that the lower bounds on energy consumption hold for any MAC protocol. Furthermore, we prove that these bounds are tight.
Face Recognition Using ALLE and SIFT for Human Robot Interaction
Authors: Yaozhang Pan, Shuzhi Sam Ge, Hongsheng He
Venue: Proceedings of the FIRA RoboWorld Congress 2009 on Advances in Robotics (2009)

Face recognition is a very important aspect in developing human-robot interaction (HRI) for social robots. In this paper, an efficient face recognition algorithm is introduced for building intelligent robot vision system to recognize human faces. Dimension deduction algorithms locally linear embedding (LLE) and adaptive locally linear embedding (ALLE) and feature extraction algorithm scale-invariant feature transform (SIFT) are combined to form new methods called LLE-SIFT and ALLE-SIFT for finding compact and distinctive descriptors for face images. The new feature descriptors are demonstrated to have better performance in face recognition applications than standard SIFT descriptors, which shows that the proposed method is promising for developing robot vision system of face recognition.
Implementing Reflective Access Control in SQL
Authors: Lars E. Olson, Carl A. Gunter, William R. Cook, Marianne Winslett
Venue: Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII (2009)

Reflective Database Access Control (RDBAC) is a model in which a database privilege is expressed as a database query itself, rather than as a static privilege in an access control matrix. RDBAC aids the management of database access controls by improving the expressiveness of policies. The Transaction Datalog language provides a powerful syntax and semantics for expressing RDBAC policies, however there is no efficient implementation of this language for practical database systems. We demonstrate a strategy for compiling policies in Transaction Datalog into standard SQL views that enforce the policies, including overcoming significant differences in semantics between the languages in handling side-effects and evaluation order. We also report the results of evaluating the performance of these views compared to policies enforced by access control matrices. This implementation demonstrates the practical feasibility of RDBAC, and suggests a rich field of further research.
Predicting Click Rates by Consistent Bipartite Spectral Graph Model
Authors: Wensheng Guo, Guohe Li
Venue: Proceedings of the 5th International Conference on Advanced Data Mining and Applications (2009)

Search advertising click-through rate (CTR) is one of the major contributions to search ads' revenues. Predicting the CTR for new ads put a direct impact on the ads' quality. Traditional predicting methods limited to Vector Space Model fail to sufficiently consider the search ads' characteristics of heterogeneous data, and therefore have limited effect. This paper presents consistent bipartite graph model to describe ads, adopting spectral co-clustering method in data mining. In order to solve the balance partition of the map in clustering, divide-and-merge algorithm is introduced into consistent bipartite graph's co-partition, a more effective heuristic algorithm is established. Experiments on real ads dataset shows that our approach worked effectively and efficiently.
Using Avatars for Improving Speaker Identification in Captioning
Authors: Quoc V. Vy, Deborah I. Fels
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

Captioning is the main method for accessing television and film content by people who are deaf or hard-of-hearing. One major difficulty consistently identified by the community is that of knowing who is speaking particularly for an off screen narrator. A captioning system was created using a participatory design method to improve speaker identification. The final prototype contained avatars and a coloured border for identifying specific speakers. Evaluation results were very positive; however participants also wanted to customize various components such as caption and avatar location.
Efficient MAP Inference for Statistical Relational Models through Hybrid Metaheuristics
Authors: Marenglen Biba, Stefano Ferilli, Floriana Esposito
Venue: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems (2009)

Statistical Relational Models are state-of-the-art representation formalisms at the intersection of logical and statistical machine learning. One of the most promising models is Markov Logic (ML) which combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. MAP inference in ML is the task of finding the most likely state of a set of output variables given the state of the input variables and this problem is NP-hard. In this paper we present an algorithm for this inference task based on the Iterated Local Search (ILS) and Robust Tabu Search (RoTS) metaheuristics. The algorithm performs a biased sampling of the set of local optima by using RoTS as a local search procedure and repetitively jumping in the search space through a perturbation operator, focusing the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for the optimization engine. We show through extensive experiments in real-world domains that it improves over the state-of-the-art algorithm in terms of solution quality and inference time.
Revisiting Satisfiability and Model-Checking for CTLK with Synchrony and Perfect Recall
Authors: Cătălin Dima
Venue: Computational Logic in Multi-Agent Systems: 9th International Workshop, CLIMA IX, Dresden, Germany, September 29-30, 2008. Revised Selected and Invited Papers (2009)

We show that CTL with knowledge modalities but without common knowledge has an undecidable satisfiability problem in the synchronous perfect recall semantics. We also present an adaptation of the classical model-checking algorithm for CTL that handles knowledge operators.
Practical Mitigations for Timing-Based Side-Channel Attacks on Modern x86 Processors
Authors: Bart Coppens, Ingrid Verbauwhede, Koen De Bosschere, Bjorn De Sutter
Venue: Proceedings of the 2009 30th IEEE Symposium on Security and Privacy (2009)

This paper studies and evaluates the extent to which automated compiler techniques can defend against timing-based side-channel attacks onmodern x86 processors. We study how modern x86 processors can leak timinginformation through side-channels that relate to control flow and data flow. Toeliminate key-dependent control flow and key-dependent timing behavior relatedto control flow, we propose the use of if-conversion in a compiler backend, andevaluate a proof-of-concept prototype implementation. Furthermore, we demonstratetwo ways in which programs that lack key-dependent control flow and key- dependent cache behavior can still leak timing information on modern x86implementations such as the Intel Core 2 Duo, and propose defense mechanisms against them.
Competitive self-trained pronoun interpretation
Authors: Andrew Kehler, Douglas Appelt, Lara Taylor, Aleksandr Simma
Venue: Proceedings of HLT-NAACL 2004: Short Papers on XX (2004)

We describe a system for pronoun interpretation that is self-trained from raw data, that is, using no annotated training data. The result outperforms a Hobbsian baseline algorithm and is only marginally inferior to an essentially identical, state-of-the-art supervised model trained from a substantial manually-annotated coreference corpus.
A Memory-Efficient Data Redistribution Algorithm
Authors: Stephen F. Siegel, Andrew R. Siegel
Venue: Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2009)

Many memory-bound distributed applications require frequent redistribution of data. Pinar and Hendrickson investigated two families of memory-limited redistribution algorithms. The first family has many advantages, but fails on certain inputs, and, if not implemented carefully, may lead to an explosion in the number of local data copies. The second family eliminates the possibility of failure at the expense of considerable additional overhead. We carefully analyze these algorithms and develop a modified method that potentially combines advantages of each. The resulting algorithm has been implemented in MADRE and experiments reveal its performance to be superior to that of other MADRE algorithms in most cases.
Color Image Segmentation Method Based on Region Growing and Ant Colony Clustering
Authors: Xinyan Mao, Ying Zhang, Yanxiao Hu, Binjie Sun
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01 (2009)

In this paper, a color image segmentation method is proposed based on region growing and ant colony clustering. By using the intensity similarity among pixels in the HSI color space and the edge information, seeds are first automatically selected. Then seeded region begins to grow based on color and spatial information. After that, ant colony clustering method is used to merge different regions of homogeneity. Finally good results of segmentation can be achieved. Experimental results show that this method is an effective method for image segmentation, which eliminates large quantities of cycles and calculations caused by traditional ant colony algorithm.
Using CSP||B Components: Application to a Platoon of Vehicles
Authors: Samuel Colin, Arnaud Lanoix, Olga Kouchnarenko, Jeanine Souquières
Venue: Formal Methods for Industrial Critical Systems: 13th International Workshop, FMICS 2008, L'Aquila, Italy, September 15-16, 2008, Revised Selected Papers (2009)

This paper presents an experience report on the specification and the validation of a real case study in the context of the industrial CRISTAL project. The case study concerns a platoon of a new type of urban vehicles with new functionalities and services. It is specified using the combination, named CSP||B, of two well-known formal methods, and validated using the corresponding support tools. This large --- both distributed and embedded --- system typically corresponds to a multi-level composition of components that have to cooperate. We identify some lessons learned, showing how to develop and verify the specification and check some properties in a compositional way using theoretical results and support tools to validate this complex system.
Intelligence Analysis Based on Intervenient Optimum Learning Guide System
Authors: Zengtang Qu, Ping He
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

In this paper, we present intervenient optimum learning guide (IOLG), a system for learning non-optimum-lean heuristics under resource constraints. IOLG is an implementation of a genetics-based learning framework we have developed for improving the performance of intelligence in application problem solvers. Besides providing a flexible and modular framework for conducting experiments, IOLG provides (a) a optimum-non-optimum for experimenting with various resource scheduling, generalization, and non-optimum-lean strategies, (b) a intervenient optimum learning guide system (IOLGS) that can be easily interfaced to new applications and can be customized based on user requirements and target environments. This paper describes the application-independent functions provided by IOLGS, and the application dependent functions for interfacing to new problem solvers. By adjusting various global parameters in IOLGS users can control the numerous options and alternatives in IOLGS.
Sensor Modalities for Brain-Computer Interfacing
Authors: Gerwin Schalk
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques (2009)

Many people have neuromuscular conditions or disorders that impair the neural pathways that control muscles. Those most severely affected lose all voluntary muscle control and hence lose the ability to communicate. Brain-computer interfaces (BCIs) might be able to restore some communication or control functions for these people by creating a new communication channel --- directly from the brain to an output device. Many studies over the past two decades have shown that such BCI communication is possible and that it can serve useful functions. This paper reviews the different sensor methodologies that have been explored in these studies.
Using web selectors for the disambiguation of all words
Authors: Hansen A. Schwartz, Fernando Gomez
Venue: Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (2009)

This research examines a word sense disambiguation method using selectors acquired from the Web. Selectors describe words which may take the place of another given word within its local context. Work in using Web selectors for noun sense disambiguation is generalized into the disambiguation of verbs, adverbs, and adjectives as well. Additionally, this work incorporates previously ignored adverb context selectors and explores the effectiveness of each type of context selector according to its part of speech. Overall results for verb, adjective, and adverb disambiguation are well above a random baseline and slightly below the most frequent sense baseline, a point which noun sense disambiguation overcomes. Our experiments find that, for noun and verb sense disambiguation tasks, each type of context selector may assist target selectors in disambiguation. Finally, these experiments also help to draw insights about the future direction of similar research.
Enforcing Constraints on Life Cycles of Business Artifacts
Authors: Xiangpeng Zhao, Jianwen Su, Hongli Yang, Zongyan Qiu
Venue: Proceedings of the 2009 Third IEEE International Symposium on Theoretical Aspects of Software Engineering (2009)

Artifact-centric business process models allow to describe artifacts (data objects) and their life cycles, which allow designers to focus on individual artifact in business processes, thus simplifies the design and analysis of business process model. However, this feature is a double-edged sword. The description of the relationships between artifacts becomes a new and nontrivial problem. It is better that the associations among business artifacts are specified at a high level as logical assertions. We think taking business constraints as complements of artifact-centric business operational model is an useful idea. Based on this consideration,in this paper, we propose an approach which combines both the declarative way and the procedural way in the construction of business processes. This flexibility can help designers to separate the parts of a business process that are more likely to change from those that are less likely to change. We propose a language TiLE to specify business constraints, and give complexity results on the satisfiability of TiLE. Moreover, we discussed how to enforce the constraints at run-time.
Combining Classifier Based on Decision Tree
Authors: Yao Yu, Fu Zhong-liang, Zhao Xiang-hui, Cheng Wen-fang
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 02 (2009)

A new classifier ensemble learning algorithmbased on decision tree is proposed. Ensemble learningalgorithm is one of the algorithms which have bestclassification results in many classification algorithms. Adecision tree algorithm is a kind of greedy algorithm, ituse top-down recursive way to determine the treestructure. The proposed algorithm improved theaccuracy of classification by combining the advantage ofBoosting algorithm with decision tree. The main idea is tomake full use of the advantages of ensemble learningalgorithm and decision tree. We introduce the algorithmprocession in detail. The proposed algorithm proved thatthe property which has the smallest classification errorrate as of decision tree is equivalent to the branchingmethod of traditional decision tree. The algorithm usesthe rapid classification capabilities of decision tree. In themeantime, we take into account the classificationaccuracy of joint classification. Finally, Experiments withUCI machine learning data sets show the effectiveness ofthe proposed algorithm.
A multipath routing and spectrum access (MRSA) framework for cognitive radio systems in multi-radio mesh networks
Authors: Xiaofei Wang, Ted "Taekyoung" Kwon, Yanghee Choi
Venue: Proceedings of the 2009 ACM workshop on Cognitive radio networks (2009)

Cognitive Radio (CR) techniques to dynamically utilize the frequency spectrum is under hot discussion recently. In particular, wireless mesh networks along with CR techniques may play a vital role in spectrum-scarce situations, which are the target environments of this paper. In CR systems, the most crucial issue is how to detect and mitigate the appearance of primary users. To minimize the effect of "onset" of primary users, we propose to use multipath routing assuming that multiple radios are equipped at each station. Using multipath and multiradio in wireless mesh networks shows that the performance (e.g. end-to-end throughput) is substantially more resilient to the dynamic behavior of primary users than other choices. The proposed routing and spectrum access framework seeks to reduce the contention and interference among stations in perspective of traffic load balancing.
A music stream segregation system based on adaptive multi-agents
Authors: Kunio Kashino, Hiroshi Murase
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

A principal problem of auditory scene analysis is stream segregation: decomposing an input acoustic signal into signals of individual sound sources included in the input. While existing signal processing algorithms cannot properly solve this inverse problem, a multiagentbased architecture has been considered to be a promising methodology in its modularity and scalability. However, most attempts made so far depend on subjectively defined rules to deal with variability of sounds. Here we propose a quantitatively principled architecture in agent interaction by formulating the problem as least-squares optimization. In this architecture, adaptation of the agents is the essential idea. We have developed two kinds of processing to realize adaptivity: template filtering and phase tracking. These mechanisms enable each agent to optimally, in the least-squares sense, track the individual sound. As an example application of the proposed architecture, we have built a music recognition system that recognizes instrument names and pitches of the notes included in ensemble music performances. Experimental results show that these adaptive mechanisms significantly improve the recognition accuracy.