ppxscal/embedding_network huggingface dataset:
Reasoning and hypothesizing about signaling networks
Authors: Chitta Baral
Venue:  (2006)

A living cell constantly receives and responds to signals from its environment. Signals come in the form of light, heat, or biochemical molecules. Signals initiate networks of biochemical reactions in the cell, which result in cell growth, cell movement, cell survival or programmed cell death. Malfunctions of these signaling networks usually cause diseases. For example, a signal may not be turned off after it has triggered cell division, thus resulting in excessive growth of cells and causing cancer. Consequently, effective therapeutic strategies may be based on correction or adjustment of broken-down signaling networks. The goal of this dissertation is to contribute toward computer-aided modeling and inference of signaling networks. Most of the existing approaches are based on quantitative models such as differential equations. The analysis of a model is usually done via simulation and perturbation. Such an analysis can easily answer prediction queries (about impact of events on the cell). However, it is computationally expensive to answer questions about explaining a particular observation, or about planning to alter the cell behavior. Moreover, quantitative information (e.g., kinetic data) is hard to obtain, thus rendering the quantitative models inexact. Several qualitative approaches have been proposed, but they have not yet addressed explanation and planning queries. Besides, they do not deal with the incompleteness of information about signaling networks, such as missing pathways or uncertainty of observations. In this dissertation, a knowledge-based approach is proposed for representing and reasoning about signaling networks. A non-monotonic knowledge representation language is presented that can handle incomplete or partial information about signaling networks. The language and a prototype of knowledge have been implemented using a state-of-the-art AnsProlog reasoning engine, which can compute prediction, explanation and planning queries. The proposed approach also allows for easy updating of the knowledge base when new knowledge becomes available.
Classification of landmine-like metal targets using wideband electromagnetic induction
Authors: Ping Gao
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04 (1999)

Our previous work has indicated that the careful application of signal detection theory can dramatically improve detectability of landmines using time-domain electromagnetic induction (EMI) data. In this paper, classification of various metal targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor. An algorithm that incorporates both the uncertainties regarding the target-sensor orientation and a theoretical model of the response of such a sensor is developed. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over the traditional matched filter approach, on average by 60%.
DVSleak: combining leakage reduction and voltage scaling in feedback EDF scheduling
Authors: Yifan Zhu, Frank Mueller
Venue: Proceedings of the 2007 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems (2007)

Recent trends in CMOS fabrication have the demand to conserve power of processors. While dynamic voltage scaling (DVS) is effective in reducing dynamic power, microprocessors produced in ever smaller fabrication processes are increasingly dominated bystatic power. For such processors, voltage/frequency pairs below acritical speed result in higher energy per cycle than entering a processor sleep mode. Yet, computational demand above this critical speed is best met by DVS techniques while still conserving power. We develop a novel combined leakage and DVS scheduling algorithm forreal-time systems, DVS leak, based on earliest-deadline-first scheduling (EDF). Our method trades off DVS with leakage, where the former slows down execution while the latter intelligently defers dispatching of jobs when sleeping is beneficial. We further capitalize on feedback knowledge about actual execution times to anticipate computational demands without sacrificing deadline guarantees. As such, we contribute a novel feedback delay policy for leakage awareness, which addresses structural limitations of prior approaches. Experiments show that this combined DVS/leakage algorithm results in an average of (a) 50% additional energy savings over a leakage-oblivious DVS algorithm, (b) 20% more energy savings over a more simplistic combination of DVS and sleep policies and (c) 8.5% or more over dynamic slack reclamation with procrastination. Particularly task sets with periods shorter than ten milliseconds profit from our approach with 15% energy savings over best prior schemes. This makes DVS leak the best combined DVS/leakage regulation approach for real-time systems that we know of.
Unsupervised analysis for decipherment problems
Authors: Kevin Knight, Anish Nair, Nishit Rathod, Kenji Yamada
Venue: Proceedings of the COLING/ACL on Main conference poster sessions (2006)

We study a number of natural language decipherment problems using unsupervised learning. These include letter substitution ciphers, character code conversion, phonetic decipherment, and word-based ciphers with relevance to machine translation. Straightforward unsupervised learning techniques most often fail on the first try, so we describe techniques for understanding errors and significantly increasing performance.
A new approach to power adjustment for spatial covariance based downlink beamforming
Authors: H. Boche
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05 (2001)

When using downlink beamforming at the base station of a cellular system, care must be taken to assure that all users satisfy the given SINR requirements. Generally, this leads to a computationally prohibitive joint optimization problem, therefore decoupled techniques based on eigenvalue decomposition of the spatial covariance have been proposed. However, these techniques require additional power adjustment of the steering vectors in order to satisfy the given SINR requirements. Conventional power control schemes are not suited for this purpose because the SINRs primarily depend on the shape of the radiation patterns. In this paper we propose a new power adjustment algorithm, which adjusts the beamforming weights, such that all users have the same SINR Additionally, it is possible to limit the maximum radiated user power.
Porting the coda file system to windows
Authors: Peter J. Braam, Michael J. Callahan, M. Satyanarayanan, Marc Schnieder
Venue: Proceedings of the annual conference on USENIX Annual Technical Conference (1999)

We first describe how the Coda distributed filesystem was ported to Windows 95 and 98. Coda consists of user level cache managers and servers and kernel level code for filesystem support. Severe reentrancy difficulties in the Win32 environment on this platform were overcome by extending the DJGPP DOS C compiler package with kernel level support for sockets and more flexible memory management. With this support library and kernel modules for Windows 9x filesystems in place, the Coda file system client could be ported with very little patching and will likely soon run as well on Windows 9x as on Linux. We ported Coda file servers to Windows NT. For fileservers the Cygwin32 kit was used. We will not report here on the port of the Coda client to Windows NT, which is in an early stage. In both cases cross compilation from a Linux environment was most helpful to get a good development environment.
Caubweb: detaching the web with Tcl
Authors: John R. Lo Verso, Murray S. Mazer
Venue: Proceedings of the 5th conference on Annual Tcl/Tk Workshop 1997 - Volume 5 (1997)

CaubwebTM is a research system that allows a user to create local collections of Web documents on the user's computer, for access to those collections when disconnected. The system is part of a project investigating ways to provide adaptive, ongoing read and update interaction with Web-based information, even under conditions of variable or intermittent network connectivity. Caubweb is architecturally an HTTP proxy augmented with value-adding capabilities. To accommodate our design principles of platform-portability and extensibility, we used Tcl as our implementation language. This paper reports on our experience in using Tcl/Tk to build Caubweb. We discuss the structure of our implementation, identify strengths and weaknesses of the language and its tools, contrast Tcl/Tk with alternatives, and present a "call to arms" for the Tcl/Tk community, to promote increased reuse and cooperation.
Ontology-Based Modeling of Breast Cancer Follow-up Clinical Practice Guideline for Providing Clinical Decision Support
Authors: Samina Raza Abidi
Venue: Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems (2007)

Breast cancer is the most common cancer among women in Canada. There are identified incentives to the transfer of breast cancer follow-up care to family physicians after primary treatment has been completed by oncologists at the tertiary care centers. This paper presents a semantic web approach to develop a clinical decision support system to support family physicians to provide breast cancer follow-up care. Our approach involved the computerization and execution of a breast cancer follow-up Clinical Practice Guideline. The computerization of the clinical practice guideline led to the development of a breast cancer ontology. We present our breast cancer ontology which models the knowledge inherent within the breast cancer follow-up clinical practice guideline--the breast cancer ontology serves as the knowledge source to determine patient-specific recommendations. In this paper, we discuss the ontology engineering process that highlights the specification of our breast cancer ontology in terms of clinical concepts and the relationships between the concepts expressed as OWL classes and properties, using the Protégé ontology development tool.
Protecting against unexpected system calls
Authors: C. M. Linn, M. Rajagopalan, S. Baker, C. Collberg, S. K. Debray, J. H. Hartman
Venue: Proceedings of the 14th conference on USENIX Security Symposium - Volume 14 (2005)

This paper proposes a comprehensive set of techniques which limit the scope of remote code injection attacks. These techniques prevent any injected code from making system calls and thus restrict the capabilities of an attacker. In defending against the traditional ways of harming a system these techniques significantly raise the bar for compromising the host system forcing the attack code to take extraordinary steps that may be impractical in the context of a remote code injection attack. There are two main aspects to our approach. The first is to embed semantic information into executables identifying the locations of legitimate system call instructions; system calls from other locations are treated as intrusions. The modifications we propose are transparent to user level processes that do not wish to use them (so that, for example, it is still possible to run unmodified third-party software), and add more security at minimal cost for those binaries that have the special information present. The second is to back this up using a variety of techniques, including a novel approach to encoding system call traps into the OS kernel, in order to deter mimicry attacks. Experiments indicate that our approach is effective against a wide variety of code injection attacks.
TIED, LibsafePlus: tools for runtime buffer overflow protection
Authors: Kumar Avijit, Prateek Gupta, Deepak Gupta
Venue: Proceedings of the 13th conference on USENIX Security Symposium - Volume 13 (2004)

Buffer overflow exploits make use of the treatment of strings in C as character arrays rather than as first-class objects. Manipulation of arrays as pointers and primitive pointer arithmetic make it possible for a program to access memory locations which it is not supposed to access. There have been many efforts in the past to overcome this vulnerability by performing array bounds checking in C. Most of these solutions are either inadequate, inefficient or incompatible with legacy code. In this paper, we present an efficient and transparent runtime approach for protection against all known forms of buffer overflow attacks. Our solution consists of two tools: TIED (Type Information Extractor and Depositor) and LibsafePlus. TIED extracts size information of all global and automatic buffers defined in the program from the debugging information produced by the compiler and inserts it back in the program binary as a data structure available at runtime. LibsafePlus is a dynamic library which provides wrapper functions for unsafe C library functions such as strcpy. These wrapper functions check the source and target buffer sizes using the information made available by TIED and perform the requested operation only when it is safe to do so. For dynamically allocated buffers, the sizes and starting addresses are recorded at runtime. With our simple design we are able to protect most applications with a performance overhead of less than 10%.
Panel: In Search of a Universal (But Useful) Model of Parallel Computation
Authors: Howard Jay Siegel, Panel Moderator
Venue: Proceedings of the 1993 International Conference on Parallel Processing - Volume 01 (1993)

Is it possible to create one idealized parallel com puter model to provide an interface between hardware and software performance that supports a machineindependent framework for specifying various compu tational costs? Is it necessary to have more than one? What attributes should this model(s) have? Does such a model(s) exist today? How can we create such a model(s)? Is it important to have such a model(s)?
Observer annotation of affective display and evaluation of expressivity: face vs. face-and-body
Authors: Hatice Gunes, Massimo Piccardi
Venue: Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56 (2006)

A first step in developing and testing a robust affective multimodal system is to obtain or access data representing human multimodal expressive behaviour. Collected affect data has to be further annotated in order to become usable for the automated systems. Most of the existing studies of emotion or affect annotation are monomodal. Instead, in this paper, we explore how independent human observers annotate affect display from monomodal face data compared to bimodal face-and-body data. To this aim we collected visual affect data by recording the face and face-and-body simultaneously. We then conducted a survey by asking human observers to view and label the face and face-and-body recordings separately. The results obtained show that in general, viewing face-and-body simultaneously helps with resolving the ambiguity in annotating emotional behaviours.
An Improved Feature Representation Method for Maximum Entropy Model
Authors: Guan Yi, Zhao Jian
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

In maximum entropy model (MEM), features are typi- cally represented by either 0-1 binary-valued function or real-valued function. However, both representations only examine the impact of specific value of some attributes but not their types. Such negligence not only causes the de- creasing of classification precision, but also slows the con- vergence speed of the generalized iterative scaling (GIS) al- gorithm, as will become more apparent to incomplete data. In this paper, an improved feature representation method is presented. The feature is composed of two parts: the first one is for specific value of an attribute; the second one is for the type of corresponding attribute. The experi- mental results on Mushroom dataset of UCI data repository showed that the average classifying precisions on incom- plete dataset and complete dataset were improved by 1.5% and 3.0% respectively, and the average convergence speed was improved by 42.9% and 90.7% respectively.
Model Driven Development with NORMA
Authors: Matthew Curland, Terry Halpin
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

Object-role Modeling (ORM) is a fact-oriented approach for specifying, transforming, and querying information at a conceptual level. Unlike Entity- Relationship (ER) modeling and Unified Modeling Language (UML) class diagrams, ORM is attribute-free, treating all elementary facts as relationships. For information modeling, fact-oriented graphical notations are typically far more expressive than other notations. Based on extensive industrial feedback, a second generation ORM (ORM 2) was recently specified. This paper provides a detailed discussion of NORMA (Neumont ORM Architect), a software tool that facilitates entry, validation, and mapping of ORM 2 models. Building on Microsoft's Domain Specific Language (DSL) technology, NORMA is implemented as an open-source plug-in to Visual Studio .NET. As well as supporting ORM 2, with automated verbalization and live error-handling, NORMA automatically generates code for relational database models, object models, and XML schemas.
Multi-media and World Wide Web resources for teaching DSP
Authors: J. H. McClellan
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02 (1996)

A new digital signal processing (DSP) course designed to build students intuition about signals and systems is presented. The course makes extensive use of multi-media demonstrations to relate real-world signals and discrete-time systems to their mathematical descriptions. In addition to the numerous in-class demonstrations, weekly laboratory exercises have been developed to further strengthen the ties between DSP theory and real-world signals and systems. Many of the in-class demonstrations have been captured on the World Wide Web (WWW), so the students can explore them outside of class. In the near future, the WWW-based demonstrations will be packaged on a CD-ROM as an integral part of a new text.
A Semantic Framework for Integrated Asset Management in Smart Oilfields
Authors: Ramakrishna Soma, Amol Bakshi, Viktor K. Prasanna
Venue: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (2007)

Integrated Asset Management (IAM) is the vision of ITenabled transformation of oilfield operations where information integration from a variety of tools for reservoir modeling, simulation, and performance prediction will lead to rapid decision making for continuous production optimization. This paper describes the design of a model-based IAM system for production forecasting. Domain knowledge is captured through a formal modeling language that forms the basis for an intuitive user interface to the system. An IAM metacatalog captures domain knowledge as well as metadata about computational resources and data sets in a single ontological framework, thereby providing a unified mechanism for application, data, and workflow integration . The framework is designed to be portable across oilfield assets, to allow different classes of end users to interact with the integrated system, and to accomodate new domain knowledge, software applications, data sets, and workflows for IAM.
Time-frequency estimation for cyclostationary signals
Authors: T. Frederick
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05 (1996)

This paper presents estimators of the Wigner-Ville spectrum for wide sense cyclostationary (WSCS) processes. It is shown that the smoothing kernel is a mixed continuous time/discrete time function under the cyclostationary assumption. For estimators of WSCS processes it is shown that the tradeoff between variance reduction and blurring of the nonstationary features of the process does not exist. The Cohen's class smoothing kernel is given for finite observation lengths of the process, and smoothed pseudo-Wigner distribution type kernels are illustrated in examples.
Measurements of blood vessel wall areas in black-blood MR images using global minimum snake algorithm
Authors: E. Lin
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06 (1999)

In this paper, we propose a novel boundary detection approach for three-dimensional shape modeling. Our method is based on finding surfaces of minimal weighted area in a Riemannian metric. In order to take advantage of intensity information of images, we further integrate this intensity information into the boundary detection algorithm. We apply this algorithm to identify the inner and outer boundaries of the blood vessel wall in magnetic resonance images, and assess its accuracy and reproducibility. Our algorithm is reasonably accurate (about 2% difference in comparison with the manual method) and highly reproducible.
Krill: an exploration in underwater sensor networks
Authors: I. Vasilescu, K. Kotay, D. Rus, L. Overs, P. Sikka
Venue: Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors (2005)

While sensor networks have now become very popular on land, the underwater environment still poses some difficult problems. Communication is one of the difficult challenges under water. There are two options: optical and acoustic. We have designed an optical communication board that allows the Flecks to communicate optically. We have tested the resulting underwater sensor nodes in two different applications.
Context-Inclusive Approach to Speed-up Function Evaluation for Statistical Queries: An Extended Abstract
Authors: Vijay Gandhi, James M. Kang, Shashi Shekhar, Junchang Ju, Eric D. Kolaczyk, Sucharita Gopal
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

Many statistical queries such as maximum likelihood estimation involve finding the best candidate model given a set of candidate models and a quality esti- mation function. This problem is common in impor- tant applications like land-use classification at multi- ple spatial resolutions from remote sensing raster data. Such a problem is computationally challenging due to the significant computation cost to evaluate the qual- ity estimation function for each candidate model. A recently proposed method of multiscale, multigranular classification has high computational overhead of func- tion evaluation for various candidate models indepen- dently before comparison. In contrast, we propose a context-inclusive approach that controls the computa- tional overhead based on the context, i.e. the value of the quality estimation function for the best candidate model so far. Experimental results using land-use clas- sification at multiple spatial resolutions from satellite imagery show that the proposed approach reduces the computational cost significantly while providing com- parable classification accuracy.