ppxscal/embedding_network huggingface dataset:
Face Recognition Based on Polynomial Fuzzy Matching
Authors: Cui-xiang Liu, Yi-cai Sun, Ming Yu, Yan Zhang, Cun-da Yuan
Venue: Proceedings of the 16th International Conference on Artificial Reality and Telexistence--Workshops (2006)

The dimensionality of face image is high very much. It has a lot of difficulty in face recognition. In this paper, first, the concepts of polynomial fuzzy matching based on four rulers (two point rulers and two slope rulers) are introduced. It is not practical because all the slopes m_i must be given at the beginning. It is presented that the fuzzy matching for a nonlinear function between input and output can be realized by using three rulers (two point rulers and one slope ruler). The affinity between two memberships can be used for assessment to the linearity of the matched curve. The image is composed of some curves. So the affinity can be used in face image recognition. Before recognition, the nonlinear dimensionality reduction algorithm Isomap is applied in face images. Then the polynomial fuzzy matching based on three rulers algorithm is applied in face recognition. The experiments show that this method is feasible and has good recognition capability
Bias analysis in continuous adaptation systems for hearing aids
Authors: M. G. Siqueira
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02 (1999)

This paper studies analytically the steady-state convergence behavior of adaptive algorithms that approximate the Wiener solution when operating in continuous adaptation to reduce acoustic feedback in hearing aids. A bias is found in the adaptive filter's estimate of the hearing-aid feedback path when the input signal is not white. Delays in the forward and cancellation paths are shown to reduce the magnitude of the bias. Equations for the bias transfer function are obtained. A discussion about properties of the bias when delays are placed in the forward and cancellation paths follows.
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Authors: Katja Verbeeck, Ann Nowé, Johan Parent, Karl Tuyls
Venue: Autonomous Agents and Multi-Agent Systems (2007)

In this paper we introduce a new multi-agent reinforcement learning algorithm, called exploring selfish reinforcement learning (ESRL). ESRL allows agents to reach optimal solutions in repeated non-zero sum games with stochastic rewards, by using coordinated exploration. First, two ESRL algorithms for respectively common interest and conflicting interest games are presented. Both ESRL algorithms are based on the same idea, i.e. an agent explores by temporarily excluding some of the local actions from its private action space, to give the team of agents the opportunity to look for better solutions in a reduced joint action space. In a latter stage these two algorithms are transformed into one generic algorithm which does not assume that the type of the game is known in advance. ESRL is able to find the Pareto optimal solution in common interest games without communication. In conflicting interest games ESRL only needs limited communication to learn a fair periodical policy, resulting in a good overall policy. Important to know is that ESRL agents are independent in the sense that they only use their own action choices and rewards to base their decisions on, that ESRL agents are flexible in learning different solution concepts and they can handle both stochastic, possible delayed rewards and asynchronous action selection. A real-life experiment, i.e. adaptive load-balancing of parallel applications is added.
AModified O(n) Leader Election Algorithm for Complete Networks
Authors: Maria Castillo, Federico Farina, Alberto Cordoba, Jesus Villadangos
Venue: Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (2007)

This paper presents a modified leader election algorithm for complete networks without sense of direction. The original algorithm, introduced by Villadangos et al. in [11], had the aim of reducing the number of exchanged messages in order to select a leader. However, the original O(n) algorithm fails to choose a leader on several occasions. A modified algorithm, which eliminates the problems that cause the wrong behaviour, is proposed. It is formally proved that the new algorithm verifies the correctness criteria that consist of selecting a unique leader in every case. Additionally, the algorithm maintains the O(n) complexity in both messages and time, where n is the number of nodes in the system.
Exploring Non Uniform Quality of Service for Extending WSN Lifetime
Authors: Anna Egorova-Forster, Amy L. Murphy
Venue: Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops (2007)

Interest in extracting and exploiting information from wireless sensor networks has been increasing for several years. Most scenarios focus on collecting and evaluating large amounts of data at a single sink, and many protocols and algorithms have been designed to accomplish this efficiently. However, not all applications require all data, nor do they have a single collection point. Instead, multiple moving base stations collect and exploit information, concentrating on data gathered in their immediate vicinity and requiring less precise knowledge about the data collected far away. While such a scenario can be supported by traditional approaches that present perfect information, they actually require less data, therefore providing a clear opportunity for increasing network lifetime without sacrificing the data quality requirements of the application. This idea of non uniform quality of service is the focus of this paper, along with two preliminary protocols demonstrating its potential and motivating future work.
A novel evolutionary neural learning algorithm
Authors: B. Verma, R. Ghosh
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

We present a novel genetic algorithm and least square (GALS) based hybrid learning approach for the training of an artificial neural network (ANN). The approach combines evolutionary algorithms with matrix solution methods such as Gram-Schmidt, SVD, etc., to adjust weights for hidden and output layers. Our hybrid method (GALS) incorporates the evolutionary algorithm (EA) in the first layer and the least square method (LS) in the second layer of the ANN. In the proposed approach, a two-layer network is considered, the hidden layer weights are evolved using an evolutionary algorithm and the output layer weights are calculated using a linear least square method. When a certain number of generation or error goals in terms of RMS error is reached, the training is stopped. We start training with a small number of hidden neurons and then the number is increased gradually in an incremental process. The proposed algorithm was implemented and many experiments were conducted on benchmark data sets such as XOR, 10-bit odd parity, handwritten segmented characters recognition, breast cancer diagnosis and heart disease data. The experimental results showed very promising results when compared with other existing evolutionary and error back propagation (EBP) algorithms in classification rate and time complexity.
Time-area efficient multiplier-free recursive filter architectures for FPGA implementation
Authors: M. Shajaan
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

Simultaneous design of multiplier-free recursive filters (IIR filters) and their hardware implementation in Xilinx field programmable gate array (XC4000) is presented. The hardware design methodology leads to high performance recursive filters with sampling frequencies in the interval 15-21 MHz (17 bits internal data representation). It is demonstrated that the time-area efficiency and performance of the architectures are considerably above any known approach.
Exploiting Gossip for Self-Management in Scalable Event Notification Systems
Authors: Ken Birman, Anne-Marie Kermarrec, Krzystof Ostrowski, Marin Bertier, Danny Dolev, Robbert Van Renesse
Venue: Proceedings of the 27th International Conference on Distributed Computing Systems Workshops (2007)

Challenges of scale have limited the development of event notification systems with strong properties, despite the urgent demand for consistency, reliability, security, and other guarantees in applications developed for sensitive tasks in large enterprises. These issues are the focus of Quicksilver, a new multicast platform targeted to largescale deployments. An initial version of the system can support large numbers of overlapping multicast groups, high data rates and groups with large numbers of members. However, Quicksilver still requires manual help when discovering the system configuration and can't easily enforce certain types of application monitoring and integrity constraints. In this paper, we propose to extend Quicksilver by introducing gossip mechanisms, yielding a self-managed event notification platform. The two technologies are presented through a single interface and appear to end users as live distributed objects, side-byside with other kinds of typed components.
Sensitivity of PCA for traffic anomaly detection
Authors: Haakon Ringberg, Augustin Soule, Jennifer Rexford, Christophe Diot
Venue: ACM SIGMETRICS Performance Evaluation Review (2007)

Detecting anomalous traffic is a crucial part of managing IP networks. In recent years, network-wide anomaly detection based on Principal Component Analysis (PCA) has emerged as a powerful method for detecting a wide variety of anomalies. We show that tuning PCA to operate effectively in practice is difficult and requires more robust techniques than have been presented thus far. We analyze a week of network-wide traffic measurements from two IP backbones (Abilene and Geant) across three different traffic aggregations (ingress routers, OD flows, and input links), and conduct a detailed inspection of the feature time series for each suspected anomaly. Our study identifies and evaluates four main challenges of using PCA to detect traffic anomalies: (i) the false positive rate is very sensitive to small differences in the number of principal components in the normal subspace, (ii) the effectiveness of PCA is sensitive to the level of aggregation of the traffic measurements, (iii) a large anomaly may in advertently pollute the normal subspace, (iv) correctly identifying which flow triggered the anomaly detector is an inherently challenging problem.
ARRG: real-world gossiping
Authors: Niels Drost, Elth Ogston, Rob V. van Nieuwpoort, Henri E. Bal
Venue: Proceedings of the 16th international symposium on High performance distributed computing (2007)

Gossiping is an effective way of disseminating information in large dynamic systems. Until now, most gossiping algorithms have been designed and evaluated using simulations. However, these algorithms often cannot cope with several real-world problems that tend to be overlooked in simulations, such as node failures, message loss, non-atomicity ofinformation exchange, and firewalls. We explore the problems in designing and applying gossiping algorithms in real systems. Next to identifying the most prominent real-world problems and their current solutions, we introduce Actualized Robust Random Gossiping (ARRG), an algorithm specifically designed to take all of these real-world problems into account simultaneously. To address network connectivity problems such as firewalls we introduce a novel technique, the Fallback Cache. This cache can be applied to existing gossiping algorithms to improve their resilience against connectivity problems. We introduce a new metric, Perceived Network Size to measure a gossiping algorithm's effectiveness. In contrast to existing metrics, our new metric does not require global knowledge. Evaluation of ARRG and the Fallback Cache in a number of realistic scenarios shows that the proposed techniques significantly improve the performance of gossiping algorithms in networks with limited connectivity. Even in pathological situations, with 50% message loss and with 80% of the nodes behind a NAT, ARRG continues to work well. Existing algorithms fail in these circumstances.
Engineering Drug Design Using a Multi-Input Multi-Output Neuro-Fuzzy System
Authors: Crina Grosan, Ajith Abraham, Stefan Tigan
Venue: Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2006)

This article presents a multi-input multi-output (MIMO) neuro-fuzzy model for a pharmaceutical research problem. Designing drugs is a current problem in the pharmaceutical research domain. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose a neuro-fuzzy model and the performance is compared with artificial neural networks. This research used the experimental data obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj- Napoca, Romania. The idea is to build a multi-input -- multi-output neuro-fuzzy model depicting the dependence between inputs and outputs. A first order Takagi-Sugeno type fuzzy inference system is developed and it is fine tuned using neural network learning techniques. Bootstrap techniques were used to generate more samples of data and the number of experimental data is reduced due to the costs and time durations of experimentations. We obtain in this way a better estimation of some drug parameters. Experiment results indicate that the proposed method is efficient.
Performance analysis of a recursive fractional super-exponential algorithm
Authors: J. Gomes
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05 (1999)

The super-exponential algorithm is a block-based technique for blind channel equalization and system identification. Due to its fast convergence rate, and no a priori parameterization other than the block length, it is a useful tool for linear equalization of moderately distortive channels. This paper presents a recursive implementation of the super-exponential algorithm for fractionally-sampled PAM signals. Although the resulting algorithm is still block-based, recursive propagation of several key variables allows the block length to be significantly reduced without compromising the algorithm's accuracy or speed, thereby enhancing its ability to track channel variations. The convergence rate is only mildly influenced by specific channel responses, and oversampling provides smaller output variance and almost perfect tolerance to sampling errors. Simulation results demonstrate the effectiveness of the proposed technique.
Scalability of TCP Servers, Handling Persistent Connections
Authors: Konstantin Shemyak, Kai Vehmanen
Venue: Proceedings of the Sixth International Conference on Networking (2007)

Network servers often are expected to handle a very large number of simultaneous connections. A claim that .TCP servers scale poorly. was sometimes considered as a reason to use UDP as the transport layer for servers, handling tens of thousands of connections. The article identifies the reason for this statement, and shows that the current Linux kernel has means to handle as many simultaneous TCP connections as it would have been possible with UDP. Test results show drastic performance increase compared to traditional implementations particularly for SIP servers.
Automatic body feature extraction from a marker-less scanned human body
Authors: Iat-Fai Leong, Jing-Jing Fang, Ming-June Tsai
Venue: Computer-Aided Design (2007)

In this paper, we propose a novel method of body feature extraction from a marker-less scanned body. The descriptions of human body features mostly defined in ASTM (1999) and ISO (1989) are interpreted into logical mathematical definitions. Using these significant definitions, we employ image processing and computational geometry techniques to identify, automatically, body features from the torso cloud points. We have currently extracted 21 feature points and 35 feature lines on the human torso; this number may be extended if necessary. Moreover, less than 2 min processing time is taken for body feature extraction starting from the raw point cloud. This algorithm is successfully tested on several Asian female adults who are aged from 18 to 60.
Scalable location-based security in wireless networks
Authors: David R. Cheriton
Venue:  (2007)

This dissertation presents a location-based approach to wireless security. It differs from current solutions in that it uses information about the physical location of clients to leverage physical security measures instead of relying on long-term secrets such as passwords and private keys. Our approach adapts to the wireless scenario an intuitive security model that is effective and already commonplace in wired LANs. We show that it addresses three problems with current solutions. First is the inability of network administrators to define geographical boundaries for wireless coverage. While access to Ethernet ports can be controlled by locking them inside buildings, wireless links extend connectivity beyond physical boundaries, making networks reachable to users across the street or in nearby buildings, therefore more vulnerable to attacks. We show that our services allow networks to provide connectivity to clients located within the intended service area (SA). Moreover, we show that malicious devices located outside the SA either need to get physically close to it---running afoul of physical security measures---or are faced with impractical hardware demands.The second problem is the lack of proper accountability. Without additional mechanisms, wireless networks are unable to accurately locate and securely identify traffic sources because clients are no longer physically connected to network ports. Even with user authentication and cryptographic packet protection, some link-layer services still rely on MAC addresses to identify clients, making networks vulnerable to denial-of-service attacks that are both effective and easy to implement. We show that our services allow wireless devices to be distinguished and located accurately, making misbehaving clients again physically exposed and accountable for their acts.Finally, most solutions that increase security to acceptable levels incur substantial management costs. Unlike these, our approach is to improve security in a cost-effective manner. Our architecture takes advantage of higher numbers of access points not only to improve accuracy but also to configure itself autonomously, with minimal operator participation. We use extensive measurements in a real setting to show that such automatic calibration provides for accurate services while also allowing networks to scale to large numbers of access points.
Estimation of directions-of-arrival of partially polarized signals with electromagnetic vector sensors
Authors: Kwok-Chiang Ho
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05 (1996)

We develop a high-resolution method for estimating the directions-of-arrival of partially polarised signals with electromagnetic (EM) vector sensors, of which each provides measurements of the complete electric and magnetic fields induced by EM signals. The method is computationally efficient since unlike many high-resolution methods, it does not involve searching across a multi-dimensional array manifold. In addition, it does not require a priori information about the array system such as sensor positions.
Dual synchronization based on two different chaotic systems: Lorenz systems and Rössler systems
Authors: Di Ning, Jun-an Lu, Xiuping Han
Venue: Journal of Computational and Applied Mathematics (2007)

In this paper, we improve and extend the works of Liu and Davids [Dual synchronization of chaos, Phys. Rev. E 61 (2000) 2176-2179] which only introduce the dual synchronization of 1-D discrete chaotic systems. The dual synchronization of two different 3-D continuous chaotic systems, Lorenz systems and Rossler systems, is discussed. And a sufficient condition of dual synchronization about the two different chaotic systems is obtained. Theories and numerical simulations show the possibility of dual synchronization and the effectiveness of the method.
Free Information Usage System on the Network Introducing DACS Scheme
Authors: Kazuya Odagiri, Rihito Yaegashi, Masaharu Tadauchi, Naohiro Ishii
Venue: Proceedings of the Second International Conference on Internet and Web Applications and Services (2007)

As a communication mean, Personal Portal, which is an entrance where each user can acquire his/her interested information on the network, is often used. We have problems to realize the Personal Portal described as follows. First, the program to realize the Personal Portal, which is located on a specific Web Server, becomes large-scaled and complicated. Moreover, because Web pages as Personal Portal depend on that program, those pages can't always be useful for each user equally. To solve these problem, we develop two kinds of new Web Service which are realized on the network introducing DACS Scheme, which is the network management scheme by communication control of client computers. By use of these Web Services, regardless of information starage form, each user can create the customized Personal Portal to use information on the network easily and freely for oneself. That is, a free information usage system is realized.
SITIO: A Social Semantic Recommendation Platform
Authors: Juan Miguel Gomez, Giner Alor-Hernandez, Ruben Posada-Gomez, Ma. Antonieta Abud-Figueroa, Angel Garcia-Crespo
Venue: Proceedings of the 17th International Conference on Electronics, Communications and Computers (2007)

The exchange of information on the Web has recently gained momentum with the raise of some socially-oriented collaborative trends. Current phenomena such as blogging, wikis or social software sites such as Digg or Slashdot have emerged as a paradigm shift in which the consumer-producer equation on the Web has reverted. The increasing success of these initiatives in pointing at and recommending resources on the Web is fuelling a new type of social recommendation for the discovery and location of Web resources. Together with current Semantic Web technologies and vocabularies that have gained momentum and proved useful, they can help to overcome the significant shortcomings of information overload and foster sharing and collaboration through semantics. In this paper we present the SITIO approach, discuss its forthcomings and introduce BLISS, its proof-of-concept implementation, a biological literature social ranking system used in the bioinformatics field.