ppxscal/embedding_network huggingface dataset:
Robust Multiple-People Tracking Using Colour-Based Particle Filters
Authors: Daniel Rowe, Ivan Huerta, Jordi Gonzàlez, Juan J. Villanueva
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

Robust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by means of particle filtering, where occlusions are handled considering the target's predicted trajectories. Model drift is tackled by careful updating, based on the history of likelihood measures. A colour-based likelihood, computed from histogram similarity, is used. Experiments are carried out using sequences from the CAVIAR database.
Negative Selection with Antigen Feedback in Intrusion Detection
Authors: Wanli Ma, Dat Tran, Dharmendra Sharma
Venue: Proceedings of the 7th international conference on Artificial Immune Systems (2008)

One of the major challenges for negative selection is to efficiently generate effective detectors. The experiment in the past shows that random generation fails to generate useful detectors within acceptable time duration. In this paper, we propose an antigen feedback mechanism for generating the detectors. For an unmatched antigen, we make a copy of the antigen and treat it the same as a newly randomly generated antibody: it goes through the same maturing process and is subject to elimination due to self matching. If it survives and is then activated by more antigens, it becomes a legitimate detector. Our experiment demonstrates that the antigen feedback mechanism provides an efficient way to generate enough effective detectors within a very short period of time. With the antigen feedback mechanism, we achieved 95.21% detection rate on attack strings, with 4.79% false negative rate, and 99.21% detection rate on normal strings, 0.79% false positive. In this paper, we also introduce Arisytis --- Artificial Immune System Tool K it s --- a project we are undertaking for not only our own experiment, but also the research communities in the same area to avoid the waste on repeatedly developing similar software. Arisytis is available on the public domain. Finally, we also discuss the effectiveness of the r-continuous bits match and its impact on data presentation.
Muscle Loadings While Using Finger as Input Device
Authors: Yung-Hui Lee, Wei-Yow Chen, Yi-Cherng Su
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

Finger-operated input device (FOID) might provide effective pointing and dragging control. However, it was suspected that the repetitive nature of the pointing and dragging tasks while operating the FOID would induce finger muscle fatigue. This study examined the finger strain by collecting electromyographic (EMG) data from 10 subjects while playing "computer solitaire" using the FOID or a trackball mouse for 40 minutes each. Pair-wise comparisons of electrical activity and median frequency showed no significant differences in these fatigue measurements between the new FOID and the trackball mouse. On the other hand, results of JASA indicted less finger muscle loading for the new FOID than that of the trackball. These results are of heuristic value and provide a promising basis for future development of the concept of FOIDs.
Robust Combinatorial Optimization with Exponential Scenarios
Authors: Uriel Feige, Kamal Jain, Mohammad Mahdian, Vahab Mirrokni
Venue: Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization (2007)

Following the well-studied two-stage optimization framework for stochastic optimization [15,8], we study approximation algorithms for robust two-stage optimization problems with an exponential number of scenarios. Prior to this work, Dhamdhere et al. [8] introduced approximation algorithms for two-stage robust optimization problems with explicitly given scenarios. In this paper, we assume the set of possible scenarios is given implicitly, for example by an upper bound on the number of active clients. In two-stage robust optimization, we need to pre-purchase some resources in the first stage before the adversary's action. In the second stage, after the adversary chooses the clients that need to be covered, we need to complement our solution by purchasing additional resources at an inflated price. The goal is to minimize the cost in the worst-case scenario. We give a general approach for solving such problems using LP rounding. Our approach uncovers an interesting connection between robust optimization and online competitive algorithms. We use this approach, together with known online algorithms, to develop approximation algorithms for several robust covering problems, such as set cover, vertex cover, and edge cover. We also study a simple buy-at-once algorithm that either covers all items in the first stage or does nothing in the first stage and waits to build the complete solution in the second stage. We show that this algorithm gives tight approximation factors for unweighted variants of these covering problems, but performs poorly for general weighted problems.
Formalizing Context-Based Behavioural Compatibility and Substitutability for Role Components in MAS
Authors: Nabil Hameurlain
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

In this paper we focus on a new approach for the definition of context-based compatibility and substitutability of roles in MAS, and provide a formal framework for modeling roles together with their composition. First, we introduce the concept of usability of roles, and based on that we define two flexible roles compatibility relations depending on the context (environment). The proposed compatibility relations take into account the property preservation such as the completion and the proper termination of roles. Then, our formal framework is enhanced with the definition of two flexible behavioral subtyping relations related to the principle of substitutability. Finally, we show the existing link between compatibility and substitutability of roles, namely the preservation of the proposed compatibility relations by substitutability.
A Sensorless Initial Rotor Position Sensing Using Neural Network for Direct Torque Controlled Permanent Magnet Synchronous Motor Drive
Authors: Mehmet Zeki Bilgin
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

This paper presents a method to determine the initial rotor position for Direct Torque Controlled (DTC) Permanent Magnet Synchronous Motor (PMSM) using Artificial Neural Network (ANN).The inductance variation is a function of the rotor position and stator current for PMSM. A high frequency and low magnitude voltage is applied to the stator windings and examined the effects the stator currents by using ANN for initial rotor position detection. 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
Differential equations, infinite limits and real recursive functions
Authors: José Félix Costa, Bruno Loff, Jerzy Mycka
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

In this article we present a strong support to real recursive function theory as a branch of computability theory rooted in mathematical analysis. This new paradigm connects computation on reals with differential equations and infinite limits in a robust and smooth way. The results presented here are taken mainly from the article (4) of the same authors.
Folk Arguments, Numerical Taxonomy and Case-Based Reasoning
Authors: Luís A. Silva, John A. Campbell, Bernard F. Buxton
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

Experts who narrate their knowledge in case-like form often express significant parts of it in folk arguments ( considerations for and against alternative recommendations where informal judgment is involved. Such arguments do not fit naturally into common frameworks of case-based reasoning. The knowledge they contain may therefore be overlooked despite its value. The paper indicates a mean of helping knowledge acquisition in such circumstances, proposes numerical taxonomy for structuring case bases where folk arguments are included, and shows how these contributions are used, through an example involving both scientific considerations and subjective expert judgment: allocation of frequencies for shortwave broadcasting.
Offering data confidentiality for multimedia overlay multicast: Design and analysis
Authors: Wai-Pun Ken Yiu, Shueng-Han Gary Chan
Venue: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) (2008)

Application layer multicast (ALM) has been proposed to overcome current limitations in IP multicast for large-group multimedia communication. We address offering data confidentiality tailored for ALM. To achieve confidentiality, a node may need to continuously re-encrypt packets before forwarding them downstream. Furthermore, keys have to be changed whenever there is a membership change, leading to rekey processing overhead at the nodes. For a large and dynamic group, these reencryption and rekeying operations incur high processing overhead at the nodes. We propose and analyze a scalable scheme called Secure Overlay Multicast (SOM) which clusters ALM peers so as to localize rekeying within a cluster and to limit re-encryption at cluster boundaries, thereby minimizing the total nodal processing overhead. We describe the operations of SOM and compare its nodal processing overhead with two other basic approaches, namely, host-to-host encryption and whole group encryption. We also present a simplified analytic model for SOM and show that there exists an optimal cluster size to minimize the total nodal processing overhead. By comparing with a recently proposed ALM scheme (DT protocol), SOM achieves a substantial reduction in nodal processing overhead with similar network performance in terms of network stress and delay.
Mathematical programming applied in the optimal power flow problem DC: case studies of market needs
Authors: Emerson Eustáquio Costa, Luiz Danilo Barbosa Terra, George Leal Jamil
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

This paper seeks to investigate the application of mathematical programming, considering it as a tool for optimal electrical power generation and management. Nowadays, observing signals of crisis in various countries, electrical power emerges not only as a commodity but as a valuable, renewable and sometimes rare resource. Modeling and studying electrical power systems with application of mathematical programming can produce alternatives for optimal management of resources and allow better consumer satisfaction, impacting positively as a typical marketing indicator.
Composing Globally Consistent Pathway Parameter Estimates Through Belief Propagation
Authors: Geoffrey Koh, Lisa Tucker-Kellogg, David Hsu, P. S. Thiagarajan
Venue: Proceedings of the 7th international workshop on Algorithms in Bioinformatics (2007)

Parameter estimation of large bio-pathway models is an important and difficult problem. To reduce the prohibitive computational cost, one approach is to decompose a large model into components and estimate their parameters separately. However, the decomposed components often share common parts that may have conflicting parameter estimates, as they are computed independently within each component. In this paper, we propose to use a probabilistic inference technique called belief propagation to reconcile these independent estimates in a principled manner and compute new estimates that are globally consistent and fit well with data. An important advantage of our approach in practice is that it naturally handles incomplete or noisy data. Preliminary results based on synthetic data show promising performance in terms of both accuracy and efficiency.
Bounded Verification of Voting Software
Authors: Greg Dennis, Kuat Yessenov, Daniel Jackson
Venue: Proceedings of the 2nd international conference on Verified Software: Theories, Tools, Experiments (2008)

We present a case-study in which vote-tallying software is analyzed using a bounded verification technique, whereby all executions of a procedure are exhaustively examined within a finite space given by a bound on the size of the heap and the number of loop unrollings. The technique involves an encoding of the procedure in an intermediate relational programming language, a translation of that language to relational logic, and an analysis of the logic that exploits recent advances in finite model-finding. Our technique yields concrete counterexamples --- traces of the procedure that violate the specification.The vote-tallying software, used for public elections in the Netherlands, had previously been annotated with specifications in the Java Modeling Language and analyzed with ESC/Java2. Our analysis found counterexamples to the JML contracts, indicating bugs in the code and errors in the specifications that evaded prior analysis.
Canonical subsets of image features
Authors: Trip Denton, Ali Shokoufandeh, John Novatnack, Ko Nishino
Venue: Computer Vision and Image Understanding (2008)

Many object recognition and localization techniques utilize multiple levels of local representations. These local feature representations are common, and one way to improve the efficiency of algorithms that use them is to reduce the size of the local representations. There has been previous work on selecting subsets of image features, but the focus here is on a systematic study of the feature selection problem. We have developed a combinatorial characterization of the feature subset selection problem that leads to a general optimization framework. This framework optimizes multiple objectives and allows the encoding of global constraints. The features selected by this algorithm are able to achieve improved performance on the problem of object localization. We present a dataset of synthetic images, along with ground-truth information, which allows us to precisely measure and compare the performance of feature subset algorithms. Our experiments show that subsets of image features produced by our method, stable bounded canonical sets (SBCS), outperform subsets produced by K-Means clustering, GA, and threshold-based methods for the task of object localization under occlusion.
Survey of hardware platforms for an energy efficient implementation of matching pursuits algorithm for shallow water networks
Authors: Bridget Benson, Ali Irturk, Junguk Cho, Ryan Kastner
Venue: Proceedings of the third ACM international workshop on Underwater Networks (2008)

Coral reefs worldwide are in serious decline. Underwater wireless sensor networks may be the answer to providing the persistent monitoring presence needed to obtain the data necessary to better understand how to protect these ecosystems for the future. Many advances have been made in underwater acoustic communication devices for underwater wireless sensor networks, but a major challenge that still remains is obtaining an energy efficient modem design. To begin to address this challenge, we implement the Matching Pursuits algorithm for channel estimation, an energy consuming portion of an existing underwater acoustic modem designed for shallow water networks, on a variety of hardware platforms. We determine that a dedicated field programmable gate array (FPGA) intellectual property core provides the most energy efficient hardware platform for Matching Pursuits which motivates future work to port the entire modem design to an FPGA for an energy efficient modem design.
Visualization of Financial Trends Using Chance Discovery Methods
Authors: Tzu-Fu Chiu, Chao-Fu Hong, Yu-Ting Chiu
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Due to the concern of business performance and the consideration of possible investment, stakeholders inside and outside the business need to understand the financial status of a company. Usually, reading and interpreting the public offering financial statements is a common way to obtain an overall financial situation of a company. Apart from reading them directly, some visualization methods were employed so as to assist the less-professional stakeholders to understand the financial status more intuitively and painlessly. Chance discovery is one of the visualization methods which may be suitable to be applied in the financial trend detection area. In this study, a financial data visualization framework has been proposed to produce the serial KeyGraph for explaining the financial situations of two companies. Subsequently, an integrated map of financial trend was generated for depicting the overview of the accumulative five-year scenario. Finally, the visualization of financial trend was attained using serial KeyGraph and integrated map.
Salient Closed Contour Detection Based on Multiscale Analysis and Minimum-Angle
Authors: Dou Yan, Kong Lingfu
Venue: Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02 (2008)

This paper presents a novel computational approach, named MAMA, to extract perceptually salient boundaries from a set of noisy boundary fragments detected in real images. The approach first acquire a contour map using multiscale analysis. Then constructs an relation matrix between arbitrary the endpoints of each edge. At last the most salient boundary is detected by searching the smallest angle that lines got from two points and the point's grad. The proposed approch guarantees the boundary is a global structure. We collect a varietry of images for testing the proposed approach with enouraging results.
Composable distributed access control and integrity policies for query-based wireless sensor networks
Authors: David Wesley Marsh
Venue:  (2008)

In much the same way as the Global Positioning System (GPS) was initially intended as a navigation system for military use, current Wireless Sensor Networks (WSNs) tend to be dedicated to a particular purpose and for the exclusive use of a particular organization. Thus, WSN security has focused in large part on encryption and authentication schemes to protect data during transmission and ensure only authorized users are granted network access. However, just as GPS has become an indispensable resource for military and civilian use it is expected that WSNs will become an essential sensing resource that must support a vast number of users. This will require security measures mirroring the security concepts of typical secured computer networks in which authenticated users are only authorized to access certain data within the system. WSNs will need to similarly restrict access to data, enforcing security policies to protect data within WSNs, even though WSN nodes face severe power, memory, computational, and communication limitations. To date, WSN security has largely been based on encryption and authentication schemes. The WSN Authorization Specification Language (WASL), a mechanism-independent composable WSN policy language, takes into account the severe resource constraints of WSN nodes. The language is itself specified and implemented with a JavaCC&trade; grammar-parser for security policy input and a policy compiler built using Java&trade; code. WASL is capable of specifying arbitrary and composable security policies that span and integrate multiple WSN policies. The construction, hybridization, and composition of well-known models is demonstrated to preserve security, sustaining confidentiality in Bell-LaPadula's model, integrity in Biba's strict integrity model, and conflict of interest avoidance in the Chinese Wall. Using WASL, a multi-level security policy for a 1000 node network requires only 66 bytes of memory per node using a naive data compression scheme. A policy of this size can reasonably he distributed throughout a WSN periodically. The compilation of a variety of policies and policy compositions are shown to be feasible using a notebook-class computer not unlike that expected to be performing typical WSN management responsibilities. A system implementing WASL is secure as defined by the security model. It is also be more flexible in that a policy file update is all that is required to modify the accesses permitted any given user. The policy can be additionally modified to permit inter-network accesses with no more impact on the WSN nodes than any other policy update.
An Agent-Based Model for Hierarchical Organizations
Authors: Luis Erasmo Montealegre Vázquez, Fabiola López Y López
Venue: Coordination, Organizations, Institutions, and Norms in Agent Systems II: AAMAS 2006 and ECAI 2006 International Workshops, COIN 2006 Hakodate, Japan, May 9, 2006 Riva del Garda, Italy, August 28, 2006. Revised Selected Papers (2007)

Hierarchical structures have been widely used by human organizations because they provide the natural means to delegate tasks, to reduce communication lines and to control the activities performed into them. This has motivated the development of different approaches to automate many of the activities that take place in hierarchical organizations. Recent frameworks, such as Gaia, Aalaadin, HarmonIA and OperA, among others, have considered the agent paradigm to do so without taking into account that organizations are dynamic entities that evolve with the time and, consequently, agents must adapt to changes. Here we develop a model for flexible and open hierarchical organizations where agents can dynamically adapt themselves to organizational changes.
Generating all maximal induced subgraphs for hereditary and connected-hereditary graph properties
Authors: Sara Cohen, Benny Kimelfeld, Yehoshua Sagiv
Venue: Journal of Computer and System Sciences (2008)

This paper investigates a graph enumeration problem, called the maximalP-subgraphs problem, where P is a hereditary or connected-hereditary graph property. Formally, given a graph G, the maximal P-subgraphs problem is to generate all maximal induced subgraphs of G that satisfy P. This problem differs from the well-known node-deletion problem, studied by Yannakakis and Lewis [J. Lewis, On the complexity of the maximum subgraph problem, in: Proc. 10th Annual ACM Symposium on Theory of Computing, ACM Press, New York, USA, 1978, pp. 265-274; M. Yannakakis, Node- and edge-deletion NP-complete problems, in: Proc. 10th Annual ACM Symposium on Theory of Computing, ACM Press, New York, USA, 1978, pp. 253-264; J. Lewis, M. Yannakakis, The node-deletion problem for hereditary properties is NP-complete, J. Comput. System Sci. 20 (2) (1980) 219-230]. In the maximal P-subgraphs problem, the goal is to produce all (locally) maximal subgraphs of a graph that have property P, whereas in the node-deletion problem, the goal is to find a single (globally) maximum size subgraph with property P. Algorithms are presented that reduce the maximal P-subgraphs problem to an input-restricted version of this problem. These algorithms imply that when attempting to efficiently solve the maximal P-subgraphs problem for a specific P, it is sufficient to solve the restricted case. The main contributions of this paper are characterizations of when the maximal P-subgraphs problem is in a complexity class C (e.g., polynomial delay, total polynomial time).