ppxscal/embedding_network huggingface dataset:
Computationally efficient estimation of sinusoidal frequency at low SNR
Authors: S. Umesh
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05 (1996)

We propose a computationally efficient method for estimation of frequency of a single complex sinusoid at low SNR. This method is motivated by the cross-power spectrum method of Nelson (1993) and the weighted phase averager (WPA) methods of Tretter (1985), Kay (1988), and Lovell et al. (1991). We demonstrate that by a simple preprocessing, we can extend the threshold SNR of the WPA significantly. Further, unlike the WPA, the proposed method can be easily extended for estimation of frequencies of multiple sinusoids that are well-separated in frequency. We also derive the variance of the proposed estimator and provide simulation results comparing the proposed method with the WPA.
Simulation and optimisation of display visibility
Authors: Daniel Dreyer
Venue: Proceedings of the 4th symposium on Applied perception in graphics and visualization (2007)

This poster reviews the psychophysics literature on threshold contrast and incorporates the results into a technique for measuring display visibility. This work is validated, and subsequently studied in the context of air traffic control displays.
Homography-based Analysis of People and Vehicle Activities in Crowded Scenes
Authors: Sangho Park, Mohan M. Trivedi
Venue: Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision (2007)

This paper presents an new framework for homographybased analysis of pedestrian-vehicle activity in crowded scenes. Planar homography constraint is exploited to extract view-invariant object features including footage area and velocity of objects on the ground plane. Spatiotemporal relationships between people- and vehicle- tracks are represented by a semantic event. Context awareness of the situation is achieved by the estimated density distribution of objects and the anticipation of possible directions of near-future tracks using piecewise velocity history. Singleview and multi-view based homography mapping options are compared. Our framework can be used to enhance situational awareness for disaster prevention, human interactions in structured environments, and crowd movement analysis at wide regions.
Locally Excluding a Minor
Authors: Anuj Dawar, Martin Grohe, Stephan Kreutzer
Venue: Proceedings of the 22nd Annual IEEE Symposium on Logic in Computer Science (2007)

We introduce the concept of locally excluded minors. Graph classes locally excluding a minor are a common generalisation of the concept of excluded minor classes and of graph classes with bounded local tree-width. We show that first-order model-checking is fixed-parameter tractable on any class of graphs locally excluding a minor. This strictly generalises analogous results by Flum and Grohe on excluded minor classes and Frick and Grohe on classes with bounded local tree-width. As an important consequence of the proof we obtain fixed-parameter algorithms for problems such as dominating or independent set on graph classes excluding a minor, where now the parameter is the size of the dominating set and the excluded minor. We also study graph classes with excluded minors, where the minor may grow slowly with the size of the graphs and show that again, first-order model-checking is fixed-parameter tractable on any such class of graphs.
Fuzzy Logic Token Bucket Bandwidth Predictor for Assured Forwarding Traffic in a DiffServ-Aware MPLS Internet
Authors: Norashidah Md Din, Norsheila Fisal
Venue: Proceedings of the First Asia International Conference on Modelling & Simulation (2007)

The foundation of the Internet network architecture, which revolves around the transfer of packets over multitude of networks, need to be improvised to support multimedia communications. To this end the Internet community has proposed several architectures that potentially can address this requirement. Two promising architectures are Differentiated Services (DiffServ) and Multiprotocol Label Switching (MPLS). This paper describes a fuzzy logic token bucket bandwidth predictor for Assured Forwarding (AF) traffic aggregates at the DiffServ level in a DiffServ-aware MPLS Internet network. The AF traffic class comprise of variable bit rates traffic. The fuzzy logic bandwidth predictor facilitates bandwidth predicting and dynamic policing at the DiffServ level. A simulation study was performed for the fuzzy logic predictor using Network Simulator-2. The network and traffic models were developed and validated before the performance study is performed. The results show that the fuzzy logic predictor gave commendable bandwidth prediction compared to actual bandwidth utilization of the AF traffic class.
Joint prosody prediction and unit selection for concatenative speech synthesis
Authors: I. Bulyko
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02 (2001)

We describe how prosody prediction can be efficiently integrated with the unit selection process in a concatenative speech synthesizer under a weighted finite-state transducer (WFST) architecture. WFSTs representing prosody prediction and unit selection can be composed during synthesis, thus effectively expanding the space of possible prosodic targets. We implemented a symbolic prosody prediction module and a unit selection database as the synthesis components of a travel planning system. Results of perceptual experiments show that by combining the steps of prosody prediction and unit selection we are able to achieve improved naturalness of synthetic speech compared to the sequential implementation.
Blind channel identifiability/equalizability of single input multiple output nonlinear channels from second order statistics
Authors: R. Lopez-Valcarce
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05 (2000)

We explore the utility of second-order statistics for blind identification/equalization of nonlinear channels. Under standard assumptions it is shown that the channel cannot be identified to within a scaling factor from the output second order statistics, but that the ambiguity is at a level that permits equalization. We show that these results cover cases that the prior literature does not address.
Software composition with extended entity-relationship diagrams
Authors: Pornsiri Muenchaisri, Toshimi Minoura
Venue: Proceedings of the 2nd conference on USENIX Conference on Object-Oriented Technologies (COOTS) - Volume 2 (1996)

We introduce a compositional approach to application software construction. In our approach, an extended entity-relationship diagram (EERD), which represents the component types and the relationship types within an application domain, is used as a template of executable programs in that application domain. As we use structural active objects as the components of a program, we can obtain an executable program simply by connecting them as dictated by an EERD. Furthermore, the graphical editor in the proposed software development environment uses EERDs as menus in constructing application software. An EERD used as a menu can enforce legitimate patterns of relationships among components, besides that they provide an intuitive view of available components and possible relationships among them.
Applying Digital Evolution to the Development of Self-Adaptive ULS Systems
Authors: Philip K. McKinley, Betty H. C. Cheng, Charles A. Ofria
Venue: Proceedings of the International Workshop on Software Technologies for Ultra-Large-Scale Systems (2007)

A key characteristic for ultra-large scale (ULS) softwareintensive systems is the need to adapt at run time in response to changing environmental conditions. Given the scale, complexity, and heterogeneity of ULS elements, innovative, but rigorous software engineering techniques are needed to address the development and the evolution of these systems. The developer of self-adaptive ULS systems must anticipate how and when the software will need to adapt in the future, codify this behavior in decision-making components to govern the adaptation, and ensure that system integrity is not compromised during adaptations. We contend that the full potential of dynamically adaptive software systems cannot be realized without environments that enable the developer to actively explore the "adaptation space of the system during the early stages of design. We propose an approach to this problem that leverages and extends digital evolution techniques. By mapping models of adaptive software programs into digital organisms and studying traces of their evolution, the developer can gain critical insight into software decision making, software assurance, and the software infrastructure needed to support desired adaptations.
Safe, efficient garbage collection for C++
Authors: John R. Ellis, David L. Detlefs
Venue: Proceedings of the 6th conference on USENIX Sixth C++ Technical Conference - Volume 6 (1994)

We propose adding safe, efficient garbage collection to C++, eliminating the possibility of storage-management bugs and making the design of complex, object-oriented systems much easier. This can be accomplished with almost no change to the language itself and only small changes to existing implementations, while retaining compatibility with existing class libraries. Our proposal is the first to take a holistic, system-level approach, integrating four technologies. The language interface specifies how programmers access garbage collection through the language. An optional safe subset of the language automatically enforces the safe-use rules of garbage collection and precludes storage bugs. A variety of collection algorithms are compatible with the language interface, but some are easier to implement and more compatible with existing C++ and C implementations. Finally, code-generator safety ensures that compilers generate correct code for use with collectors.
List Ranking and Graph Algorithms on the Reconfigurable Multiple Bus Machine
Authors: C. P. Subbaraman, Jerry L. Trahan, R. Vaidyanathan
Venue: Proceedings of the 1993 International Conference on Parallel Processing - Volume 03 (1993)

The Reconfigurable Multiple Bus Machine (RMBM) is a model of parallel computation based on reconfigurable buses. We present constant time algorithms for list ranking, integer sorting and a number of fundamental graph problems on the RMBM. The algorithms are more efficient in terms of processors than the corresponding PARBS algorithms. The algorithms demonstrate some of the potential for computation available in the ability to manipulate communication paths as a vital part of computation.
PocketPad: Using Handhelds and Digital Pens to Manage Data in Mobile Contexts
Authors: Emory Al-Imam, Edward Lank
Venue: Proceedings of the First International Conference on the Digital Society (2007)

PocketPad is an information management system geared toward university students. The system is designed to support the capture, storage, browsing, editing and organization of handwritten notes via the complementary use of digital pens to capture information, handheld computers to browse and store prior information, and digital pens and handheld computers in combination to edit and organize information. Desktop computer software synchronizes the information from the digital pen and Pocket PC during editing and reorganization. Through the combined use of different hardware components supported by our software, we describe a system that bridges the paper-electronic information divide in mobile contexts. The system relies on human-in-the-loop techniques coupled with stroke timing to simplify coordination of content from different sources, rather than resorting to complex and often error-prone computer document recognition algorithms.
Interdisciplinary courses in electronics packaging using multiple instructors
Authors: W. D. Brown
Venue: Proceedings of the 26th Annual Frontiers in Education - Volume 01 (1996)

Two graduate-level courses were developed to support a research program in advanced electronics packaging at the University of Arkansas. Due to the interdisciplinary nature of electronics packaging technology, the course material was prepared and taught by faculty members from several departments. The goal of the two courses was not to make students experts in all the specialty areas covered, but to provide them with a basic understanding of the many disciplines involved in electronics packaging and the technical skills required to be professionally successful in the field. Consequently, students from several engineering and basic science departments were exposed to technical subjects from a number of different disciplines, each taught by a different instructor. The organizational philosophy, as well as the actual organization of the two courses, the performance and comments of students who have completed the courses, and the level of success of the courses as perceived by both the students and the instructors is discussed.
Adaptive nonlinear system identification using minimal radial basis function neural networks
Authors: Lu Yingwei
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

In this paper, an adaptive identification scheme for nonlinear systems using a minimal radial basis function neural network (RBFNN) is presented. This scheme combines the growth criterion of the resource-allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. While being applied to nonlinear system identification, this approach enables the number of hidden layer neurons in the network to be adjusted to the changing system dynamics, the resulting neural network also leads to a minimal topology for the RBFNN. Simulations are carried out to recursively identify two nonlinear systems with time-varying dynamics. The performance of the proposed algorithm is compared with the recursive hybrid algorithm for system identification proposed by Chen et al. (1992). The proposed algorithm in this paper is shown to realize a RBFNN with far fewer hidden neurons and better accuracy.
The user in the group: evaluating the effects of autonomous group dynamics
Authors: Rui Prada, Nuno Otero, Ana Paiva
Venue: Proceedings of the international conference on Advances in computer entertainment technology (2007)

Autonomous characters in virtual environmentshave the potential to improve the interaction experience of users, specially, their social experience.This effect is driven by the interactions occurring between users and the autonomous characters, that in certain scenarios can be in the context of a group.However, for these group interactions to be successful, it is not enough to assure that the autonomous charactersbehave in a coherent manner from an individual perspective,but it is also necessary that they exhibit behavioursthat are coherent with the group composition, context andstructure. Therefore, we have developed a model to support believable group dynamics of autonomous characters, inspired in theories developed in human social psychological sciences. This model defines the knowledge that eachindividual should build about the others and about the group it belongs, and how this knowledge drives their interactions in the group. The model was integrated in the mind of the autonomous characters that perform a collaborative task, in a computer game, with a human player. The game was used in a study that showed that players' interaction experience was better when interaction with groups that followed our model, namely, regarding trust and social identification with the group. In addition, we found some evidence that players prefer playing in groups that have higher level of conflict.
What if you could design tomorrow's system today?
Authors: Neal Wingen
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

This paper highlights a series of proven concepts aimed at facilitating the design of next generation systems. Practical system design examples are examined and provide insight on how to cope with today's complex design challenges.
Geometric Modeling and Visualization of Moving Objects on a Digital Map
Authors: Perihan Kilimci, Oya Kalipsiz
Venue: Proceedings of the Geometric Modelling and Imaging (2007)

In order to model geographic space, we should consider obviously anything qualifies that might appear on a paper map for example, states, cities, mountains, rivers, highway networks, hospitals, police stations etc. These varied entities can be modeled by using geometric modeling concepts. In many internet applications and Geographical Information Systems (GIS) digital maps are used to indicate locations of interested static or moving objects which change their futures in spatial and temporal domain. In this paper, we visualized a spatial map which stores its data according to geometric modeling concepts and retrieves its data from database. Then, we simulated moving objects on a digital map according to predefined scenario.
k-STARs: Sequences of Spatio-Temporal Association Rules
Authors: Florian Verhein
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

A Spatio-Temporal Association Rule (STAR) describes how objects move between regions over time. Since they describe only a single movement between two regions, it is very difficult to see larger patterns in the dataset by considering only the set of STARs. It is especially difficult on complex datasets where the underlying patterns overlap. At best we will miss important patterns - being unable to "see the forest for the trees", and at worst this can lead to false interpretations. We introduce the k-STAR pattern which describes the sequences of STARs that objects obey. Since a k-STAR captures sequences of object movements it solves these problems. We also allow space and time gaps between successive STARs, as well as supporting "replenishable' k-STARs so we are able to capture the rich set of patterns that exist in real world data. We define two important measures; min-l-support and min-l-confidence that allow us to achieve the above and present various antimonotonic and weakly anti-monotonic properties for reducing the search space.
First Steps in the Verified Software Grand Challenge
Authors: Jim Woodcock
Venue: Proceedings of the 30th Annual IEEE/NASA Software Engineering Workshop (2006)

The computer science research community is collaborating to develop verification technology that will demonstrably enhance the productivity and reliability with which software is designed, developed, integrated, and maintained.
Provisioning in complex networks: Research Articles
Authors: Ganesan Radhakrishnan
Venue: Bell Labs Technical Journal (2007)

IP Multimedia Subsystem (IMS) solutions emphasize services including all-Internet Protocol (IP) voice, data, video, multimedia, and other user-centric services. IMS applications allow network convergence among wireline, wireless, and packet networks and interaction of services in disparate, multivendor, multitechnology networks. This increased level of sophistication leads to increased operational complexity and underscores the need for a unified provisioning system to minimize the overhead of managing network data. This paper outlines a scalable, distributed, provisioning framework for disparate data networks. It examines the current approaches to provisioning and their shortcomings in dealing with disparate data. At a fundamental level, it views provisioning as two distinct problems of data management and process modeling. It depicts a unifying framework, by separating data management from the application that provisions the data, leading to data-model independence. It shows that unified provisioning requires the support of many distinct technologies including federated data management, software component framework, distributed computing framework, and formalisms for describing behavior. &copy; 2007 Alcatel-Lucent.
Inferring protein structure with discriminative learning and network diffusion
Authors: Christina Leslie
Venue:  (2006)

In this thesis, we apply several advanced learning techniques, including kernel methods and network diffusion algorithms, to address the remote homology detection problem and several related fundamental learning problems in protein structure inference. One major focus of this thesis is to address the problem of identifying the structural class of protein domains with no close evolutionary relationship, or homology, to any protein domain sequence in a database of known domains. We propose inexact matching string kernels and profile-based string kernels for use with the support vector machine (SVM) classifiers to detect subtle sequence similarities between remote homologs. We also use the kernel-based SVM classifier to extract discriminative sequence motifs, which can correspond to meaningful structural features in the protein data. In order to handle multi-domain protein sequences, we combine SVM classifiers trained from profile-based string kernels with several dynamic programming and linear programming algorithms for simultaneously solving multi-label protein classification and domain boundary identification as an optimization problem. Here, we use trained SVM classifiers as domain recognizers to find the optimal segmentation of a protein giving the largest sum of classification scores. A second major focus is the more general problem of retrieving remote homologs of a query sequence from a large database of protein sequences. We propose a new network diffusion algorithm, called MotifProp, based on a protein-motif network to detect more subtle sequence similarities than standard pairwise comparison methods, such as PSI-BLAST. Furthermore, top-ranked motifs and motif-rich regions induced by the propagation are also helpful for discovering conserved structural components in remote homologies. The last topic concerns the direct prediction of local structural information from protein sequence. We apply kernel methods to accurately predict protein backbone torsion angles, which helps to substantially improve the modeling of local structures of protein sequence segments, especially the loop conformations, which do not form regular structures. Our work provides effective and efficient solutions for the remote homology detection problem in protein classification and protein ranking, the domain segmentation problem and the protein backbone angle prediction problem, validated with large-scale experiments. The proposed new techniques, achieving high prediction performance and capable of handling huge datasets, will become increasingly important in this post-genomic era. (Abstract shortened by UMI.)
Public vs. private interest on the internet
Authors: Abbe Mowshowitz, Nanda Kumar
Venue: Communications of the ACM (2007)

Why network owners should not be the sole arbiters of access to networks, including the Internet.