ppxscal/embedding_network huggingface dataset:
Shemp: secure hardware enhanced myproxy
Authors: John Marchesini
Venue:  (2005)

In 1976, Whitfield Diffie and Martin Hellman demonstrated how public key cryptography could enable secure information exchange between parties that do not share secrets. In order for public key cryptography to work in modern distributed environments, we need an infrastructure for finding and trusting other parties' public keys, i.e., a Public Key Infrastructure (PKI). While PKI applications differ in how they use keys, all applications share one assumption: users have keypairs. This thesis begins by examining the security aspects of some of the standard keystores and their interaction with the Operating System. We establish that desktop keystores are not safe places to store private keys, and our experiments demonstrate the permeability of such keystores. Additionally, desktop keystores are immobile, difficult to use, and make it hard or impossible for relying parties to make reasonable trust judgments. We show that these problems stem from the fact that the Trusted Computing Base (TCB) of modern desktops is too large and ill-defined, which makes standard desktops suboptimal PKI clients. Since we would like to use desktops as PKI clients and cannot realistically expect to redesign the entire desktop, this thesis presents a system that works within the confines of modern desktops to shrink the TCB needed for PKI applications. Our system is called Secure Hardware Enhanced MyProxy (SHEMP), and combines a number of techniques and technologies to shrink the TCB in space and allow the TCB's size to vary over time. In addition, the SHEMP system addresses the problems of immobility and usability, and allows relying parties to make reasonable trust judgments. Using analysis, experiments, and formal methods, we conclude that SHEMP makes standard desktops suitable for use as PKI clients. The contributions of this thesis include the discovery of techniques used to identify weaknesses in modern desktops; a prototype, analysis, and correctness proof of a system which makes desktops usable as PKI clients (SHEMP); a novel approach for reasoning about TCBs; and a formal framework for proving properties of PKI systems such as SHEMP.
Data Fusion for a Surveillance System: Addressing Some Practical Problems
Authors: Murat Efe, A. Egemen Yilmaz, Ozlem Donmez Dura
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

This paper presents solutions for some data fusion problems encountered in a real life command and control system and outlines the systems engineering approach used while addressing these problems. The systems engineering approach takes into account the functionality and the capabilities of the system, operational and user aspects, safety and security, subsystem interfaces, sustainability and design constraints. The system comprises several sensor points scattered around a vast region that collect radar plots and produce tracks and two central command points that receive these tracks and process them. The processing of tracks involve carrying the tracks obtained from geographically dispersed sensors onto a common reference point (e.g. coordinate conversion and registration), fusing the tracks originating from the same target provide full air picture of the surveillance region. Also, the proposed system reproduces the track states and corresponding covariance matrix from the track quality information.
Mining Approximate Frequent Itemsets from Noisy Data
Authors: Jinze Liu, Susan Paulsen, Wei Wang, Andrew Nobel, Jan Prins
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

Frequent itemset mining is a popular and important first step in analyzing data sets across a broad range of applications. The traditional, "exact" approach for finding frequent itemsets requires that every item in the itemset occurs in each supporting transaction. However, real data is typically subject to noise, and in the presence of such noise, traditional itemset mining may fail to detect relevant itemsets, particularly those large itemsets that are more vulnerable to noise. In this paper we propose approximate frequent itemsets (AFI), as a noise-tolerant itemset model. In addition to the usual requirement for sufficiently many supporting transactions, the AFI model places constraints on the fraction of errors permitted in each item column and the fraction of errors permitted in a supporting transaction. Taken together, these constraints winnow out the approximate itemsets that exhibit systematic errors. In the context of a simple noise model, we demonstrate that AFI is better at recovering underlying data patterns, while identifying fewer spurious patterns than either the exact frequent itemset approach or the existing error tolerant itemset approach of Yang et al. [11].
An Efficient Index-Based Checkpointing Protocol with Constant-Size Control Information on Messages
Authors: Jichiang Tsai
Venue: IEEE Transactions on Dependable and Secure Computing (2005)

Communication-induced checkpointing (CIC) protocols can be used to prevent the domino effect. Such protocols that belong to the index-based category were shown to have a better performance. In this paper, we propose an efficient index-based CIC protocol. The fully informed (FI) protocol proposed in the literature has been known to be the best index-based CIC protocol that one can achieve since the optimal protocol needs to acquire the future information. We discover that the enhancement adopted by such a protocol rarely takes effect in practice. By discarding this enhancement, we obtain a new protocol, called NMMP. Simulation results show that our protocol is almost as efficient as FI in some typical computational environments. Especially, we demonstrate that the two protocols have the same behavior over a tree communication network. Surprisingly, NMMP only has to piggyback on each message control information of constant size, regardless of the number of processes.
Evolving Effective Color Features for Improving FRGC Baseline Performance
Authors: Peichung Shih, Chengjun Liu
Venue: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03 (2005)

This paper presents a novel color feature extraction method for face recognition. Firstly, a new color space, LC1C2, consisting of one luminance (L) channel and two chrominance channels (C1,C2) is introduced as a linear transformation of the input RGB color space. The specific transformation from the RGB color space to the LC1C2 color space is then optimized by Genetic Algorithms (GAs) where a fitness function guides the evolution toward higher recognition accuracy. The feasibility of our feature extraction method has been successfully demonstrated using Face Recognition Grand Challenge (FRGC) databases and the Biometric Experimentation Environment (BEE) baseline algorithm. Specifically, when experimenting with the FRGC version 1 experiment #4, the extracted color features achieve 75% and 73% rank-one face recognition rates using the Principal Component Analysis (PCA) and the Fisher Linear Discriminant (FLD) methods, respectively. When using the FRGC version 2 experiment #4, the extracted color features improve the face verification rate (at 0.1% false acceptance rate) of the BEE baseline algorithm from 12% to 32% and 55% using PCA and FLD, respectively.
A Time Optimization Algorithm for Scheduling Bag-of-Task Applications in Auction-based Proportional Share Systems
Authors: Anthony Sulistio, Rajkumar Buyya
Venue: Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing (2005)

Grid and peer-to-peer (P2P) network technologies enable aggregation of distributed resources for solving large-scale and computationally-intensive applications. These technologies are well-suited for Bag-of-Tasks (BoT) applications, because each application consists of many parallel and independent tasks. With multiple users competing for the same resources, the key challenge is to finish a user application within a specified deadline. In this paper, we propose a time optimization algorithm that schedules a user application on auction-based resource allocation systems. These allocation systems, which are based on proportional share, allow users to bid higher in order to gain more resource shares. Therefore, this algorithm adjusts a user bid periodically on these systems in order to finish the application on time.
Spatial auditory display: Comments on Shinn-Cunningham et al., ICAD 2001
Authors: Barbara G. Shinn-Cunningham, Timothy Streeter
Venue: ACM Transactions on Applied Perception (TAP) (2005)

Spatial auditory displays have received a great deal of attention in the community investigating how to present information through sound. This short commentary discusses our 2001 ICAD paper (Shinn-Cunningham, Streeter, and Gyss), which explored whether it is possible to provide enhanced spatial auditory information in an auditory display. The discussion provides some historical context and discusses how work on representing information in spatial auditory displays has progressed over the last 5 years.
Natural language processing of lyrics
Authors: Jose P. G. Mahedero, Álvaro MartÍnez, Pedro Cano, Markus Koppenberger, Fabien Gouyon
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

We report experiments on the use of standard natural language processing (NLP) tools for the analysis of music lyrics. A significant amount of music audio has lyrics. Lyrics encode an important part of the semantics of a song, therefore their analysis complements that of acoustic and cultural metadata and is fundamental for the development of complete music information retrieval systems. Moreover, a textual analysis of a song can generate ground truth data that can be used to validate results from purely acoustic methods. Preliminary results on language identification, structure extraction, categorization and similarity searches suggests that a lot of profit can be gained from the analysis of lyrics.
The dynamic re-evaluation of alternatives and the emulation of human decision making
Authors: Joe K. Clema, Mark Fynewever
Venue: ACM SIGSIM Simulation Digest (1973)

CONSIM (Conflict Simulator), a computer program designed to heuristically simulate decision making (3), has incorporated Bayes' Theorem in its structure to re-evaluate alternatives as new information becomes available to the decision maker. With this ability to re-evaluate alternatives utilizing both present and past information, even complex areas such as international affairs may be heuristically studied and analyzed. By employing elements of game theory, computer modeling and simulation, Bayes' Theorem, and probabilistic utility theory, the authors have developed a program that simulates decision making for n-person, zero-sum games with an arbitrary number of alternatives for each participant. The capability of human misunderstanding and misinterpretation of opponent's decisions has been included in the model. The methodology of CONSIM is general and may be applied in virtually any area of decision making where the alternatives are mutually exclusive, exhaustive, and the decision maker is willing to use a Bayesian approach in the assignment of probabilities, or has statistical records available.
Sensor bean: a component platform for sensor-based services
Authors: Cristina Marin, Mikael Desertot
Venue: Proceedings of the 3rd international workshop on Middleware for pervasive and ad-hoc computing (2005)

Sensor-based services propose to gather, manage, analyze, access and react to sensor data. These services are distributed over heterogeneous platforms. The complexity of the implementation of such services requires software engineering tools to relieve the architect and the developer who are often business experts and not technologies experts. Our proposal relates to the definition of a component model dedicated to the development of SBS, called Sensor Bean. Sensor Bean differs from the usual components models by its service orientation and dynamic architecture and the introduction of data-centric connectors well adapted to measurement flows. This proposal is validated by a prototype coupling OSGi and J2EE and by a first component container for OSGi gateways.
Adaptive scheduling for an incremental flexible forging cell
Authors: Amr Mohammed Elbadan
Venue:  (2004)

An adaptive forging schedule procedure that targets forging simple 3-D external profiles on a flexible forging cell is presented. The cell is composed of a numerically controlled press, a robot for workpiece manipulation, a shape feedback system and a main supervisory computer. The scheduling procedure utilizes the shape error, computed from the required and the initial workpiece shapes, to plan and execute a set of forging steps. The feedback system is then used to capture the deformed shape of the workpiece and update the shape error to adaptively plan the next set of forging steps. This adaptive control loop iterates until the workpiece shape is forged within a preset tolerance. The reduction in height and the bite values used in the schedule are limited by a set of technological bounds. Several of those bounds were identified and modelled as constraints on the adaptive scheduling procedure. Illustrative case studies showed that the adaptive scheduling procedure successfully forged external profiles (given an arbitrarily set tolerance limit) when a-priori approaches failed. The studies also showed that the new scheduling procedure could forge 2-D and 3-D profiles as easily (in terms of programming effort) as forging a uniform billet. Three new spread prediction models are developed, of which the newly developed Incrementally Updated Upper Bound model proved to provide the most accurate spread estimate compared to other developed and traditional spread prediction models. This model was then chosen to be integrated with the adaptive scheduling procedure to form an intelligent forging schedule procedure. The integration provided predictive capabilities to the adaptive scheduling module and enabled the generation of better quality forging schedules. In a typical example the number of forging steps in the forging schedule were reduced from six (using the adaptive scheduling procedure) to two (using the intelligent scheduling one).
The Wenst&oslash;p language
Authors: Henk Koppelaar
Venue: ACM SIGSIM Simulation Digest (1981)

This paper deals with a new kind of modelling social processes: linguistic modelling. The new method makes use of linguistic variables and linguistic causal relationships instead of numerical variables and relations which are usual in social systems modelling and simulation.
Study on Optimized Bandwidth Selection Approach of Drifting Learning
Authors: Feng Rui, Zhang `Yuejie, Song Chunlin
Venue: Proceedings of the The Fifth International Conference on Computer and Information Technology (2005)

Drifting Learning (DL) is an effective method to solve the regression problem in the field of data mining. The approach is established based on the combination of Local Weighted Learning (LWL) algorithm and Statistical Learning Theory (SLT). It is shown from the theoretic analysis and simulation that better performance on estimation precision and generalization ability than the traditional methods can be achieved. And this method is suitable for modeling complex industrial process with multiple work modes. In the algorithm, the optimized bandwidth selection is a key factor on the generalization performance and real-time performance. This paper first analyzes the effect of the optimized bandwidth on drifting learning method based on theoretic analysis and simulation, and then provides a novel optimized bandwidth selection algorithm. The simulation results show that the proposed approach can achieves performance superior to the existed methods.
Unsupervised Clustering using Self-Optimizing Neural Networks
Authors: Adrian Horzyk
Venue: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (2005)

Self-Optimizing Neural Networks (SONNs) [7] are very effective in solving different classification tasks. They have been successfully used to many different problems [5-10,15,16]. The classical SONN [7] adaptation process has been defined as supervised. This paper introduces a new very interesting SONN feature - the unsupervised clustering ability. The unsupervised SONNs (US-SONNs) are able to find out most differentiating features for some training data and recursively divide them into subgroups. USSONNs can also characterize the importance of features differentiating these groups. The division of the data is recursively performed till the data in subgroups differ imperceptibly. The SONN clustering proceeds very fast in comparison to other unsupervised clustering methods [1,3,4,11,12,14].
Analysis of cycle stealing with switching times and thresholds
Authors: Takayuki Osogami, Mor Harchol-Balter, Alan Scheller-Wolf
Venue: Performance Evaluation (2005)

We consider two processors, each serving its own M/GI/1 queue, where one of the processors (the "donor") can help the other processor (the "beneficiary") with its jobs, during times when the donor processor is idle. That is the beneficiary processor "steals idle cycles" from the donor processor. There is a switching time required for the donor processor to start working on the beneficiary jobs, as well as a switching back time. We also allow for threshold constraints on both the beneficiary and donor sides, whereby the decision to help is based not only on idleness but also on satisfying threshold criteria in the number of jobs.We analyze the mean response time for the donor and beneficiary processors. Our analysis is approximate, but can be made as accurate as desired, and is validated via simulation. Results of the analysis illuminate principles on the general benefits of cycle stealing and the design of cycle stealing policies.
Diffie-hellman key exchange protocol, its generalization and nilpotent groups
Authors: Spyros Magliveras
Venue:  (2005)

This dissertation has two chapters. In the first chapter we talk about the discrete logarithm problem, more specifically we concentrate on the Diffie-Hellman key exchange protocol. We survey the current state of security for the Diffie-Hellman key exchange protocol. We also motivate the reader to think about the Diffie-Hellman key exchange in terms of group automorphisms. In the second chapter we study two key exchange protocols similar to the Diffie-Hellman key exchange protocol using an abelian subgroup of the automorphism group of a non-abelian group. We also generalize group no. 92 of the Hall-Senior table, for arbitrary prime p and study the automorphism group of these generalized group. We show that for those groups, the group of central automorphisms is an abelian group. We use these central automorphisms for the key exchange we are studying. We also develop a signature scheme.
Design of Transport Triggered Architecture Processors for Wireless Encryption
Authors: Panu Hamalainen, Jari Heikkinen, Marko Hannikainen, Timo D. Hamalainen
Venue: Proceedings of the 8th Euromicro Conference on Digital System Design (2005)

Transport Triggered Architecture (TTA) offers a costeffective trade-off between the size and performance of ASICs and the programmability of general-purpose processors. In this paper TTA processors for the RC4 and AES encryption algorithms of the new IEEE 802.11i WLAN security standard are designed. Special operations efficiently supporting the ciphers are developed. The TTA design flow is utilized for finding configurations with the best performance-size ratios. The size of the configuration supporting both the algorithms is 69.4 kgates and the throughput 100 Mb/s for RC4 and 68.5 Mb/s for AES at 100 MHz in the 0.13 µm CMOS technology. Compared to commercial processors of the same wireless application domain, higher throughputs are achieved at significantly smaller area and lower clock speed, which also results in decreased energy consumption.
Serving queries to multi-resolution datasets on disk-based storage clusters
Authors: Xi Zhang, T. Pan, U. Catalyurek, T. Kurc, J. Saltz
Venue: Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid (2004)

This paper is concerned with efficient querying of very large multi-resolution datasets on storage and compute clusters. We present a suite of services that support storage, indexing, and data processing (data sampling and data aggregation) on datasets that consist of a collection of multi-resolution Grids. We empirically evaluate the performance impact of different data declustering, indexing, and query processing strategies. The experimental evaluation is carried out using a data server implemented to serve multi-terabyte multi-resolution volumetric datasets to remote visualization clients and a one-terabyte multi-resolution volumetric dataset on a PC cluster with distributed disk space.
Efficient indexing and query processing techniques on spatial time series data
Authors: Vipin Kumar, Shashi Shekhar
Venue:  (2005)

The explosive growth in size and spatio-temporal nature of data collected by advanced data collecting tools, such as satellites, sales transactions, medical instruments, and sensors, pose significant challenges for data analysis. The typical data&mdash;spatial time series data&mdash;are a collection of time series, each referencing a location. Researchers often retrieve interacting relationships among observations in spatial time series data by finding highly correlated time series. For example, such queries were used in the investigation of ocean teleconnections, i.e., identifying the land locations on the Earth where the climate was often affected by the El Nino, the anomalous warming of the eastern tropical region of the Pacific. However, such correlation queries are computationally expensive due to large number of spatial locations and long time series. Previous methods ignored intrinsic spatio-temporal properties in processing correlation queries, and thus their efficiencies deteriorate substantially for large data. The major contributions of this work lie in proposing a novel spatial cone tree indexing structure and designing efficient query processing algorithms to facilitate correlation queries in spatial time series data by exploiting the spatial autocorrelation. The spatial autocorrelation is the property that the values of attributes in nearby spatial locations tend to be similar. The spatial cone tree abstracts groups of time series in space proximity into a disk page by a cone around a single time series. The spatial cone tree can be used to design hierarchical filter and refine query processing strategies to eliminate large amounts of computational costs without affecting the query accuracy. Algebraic analyses using cost models and experimental evaluations with long term climate data from the National Aeronautics and Space Administration (NASA) were carried out to show that the proposed indexing structure and query processing algorithms saved a large portion of computational cost. The proposed techniques have been successfully used in the investigation of teleconnections in NASA's Earth science data. These techniques have the potential to be extended to many application domains including the NASA, the National Geospatial-Intelligence Agency (NGA), the National Cancer Institute (NCI), and the United States Department of Transportation (USDOT).