ppxscal/embedding_network huggingface dataset:
Timing variation-aware high-level synthesis
Authors: Jongyoon Jung, Taewhan Kim
Venue: Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design (2007)

The timing closure problem is one of the most important problems in the design automation. However, the rapid increase of the impact of the process variation on circuit timing makes the problem much more complicated and unpredictable to tackle in synthesis. This work addresses a new problem of high-level synthesis (HLS) that effectively takes into account the timing variation. Specifically, the work addresses the following four problem: (1) how can the statistical static timing analysis (SSTA) used in logic synthesis be modified and applied to the delay and yield computation in HLS? (2) how does the resource binding affect yield? (3) how does the scheduling affect yield? (4) how can scheduling and resource binding tasks be combined together to efficiently solve the problem with the objective of minimizing latency under yield constraint?
The Improvement of the Background Subtraction and Shadow Detection in Grayscale Video Sequences
Authors: Yung-Gi Wu, Chung-Ying Tsai
Venue: Proceedings of the International Machine Vision and Image Processing Conference (2007)

Users often select training images from video sequences at random, but it is hard for users to know the correctness of selection for the system. In this paper, we propose a small improvement to select training images, and incorporate a simple technique for foreground detection in grayscale video sequences.
Original and Normalized Web Log Metrics as Functions of Controllable Variables of Log Study
Authors: Nikolai Buzikashvili
Venue: Proceedings of the 2007 Latin American Web Conference (2007)

Different studies of Web search engine query logs calculate the same metrics using logs sampled during different periods and processed under different values of two controllable variables: a client discriminator used to exclude clients who are agents and a temporal cutoff used to segment client transactions into sessions. These peculiar to the Web log analysis variables markedly affect the resulting metrics, and metrics calculated under different values of the variables may not be compared directly. To extract a contribution of the controllable variables we introduce metrics normalized by their values corresponding to the unit values of the variables. Whilst differences between values of the original metric may be enormous among different logs, normalized metrics are relatively similar functions of the controllable variables. As a result, one can use available logs as a learning collection to determine these functions and to use them to approximate metrics values for unavailable logs by reported values of metrics and controllable variables. The Yandex (2005, 2007) and Excite (2001) logs were used as a learning collection to estimate log-free normalized metrics as functions of the controllable variables. The revealed functions were applied to previously reported AltaVista (1998, 2002) results to extrapolate values reported for one combination of controllable variables onto all possible combinations of variables.
Fault Dictionary Size Reduction for Million-Gate Large Circuits
Authors: Yu-Ru Hong, Juinn-Dar Huang
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

In general, fault dictionary is prevented from practical applications for its extremely large size. Several previous works are proposed for the fault dictionary size reduction. However, they might not be able to handle today's million-gate circuits due to the high time and space complexity. In this paper, we propose an algorithm to significantly reduce the size of fault dictionary while still preserving high diagnostic resolution. The proposed algorithm possesses extremely low time and space complexity by avoiding constructing the huge distinguishability table, which inevitably boosts up the required computation complexity. Experimental results demonstrate that the proposed algorithm is fully capable of handling industrial million-gate large circuits in a reasonable amount of runtime and memory.
A Multi-object Motion-tracking Method for Video Surveillance
Authors: Jiang Dan, Yu Yuan
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

In this paper, a multi-object motion-tracking method based both on region and feature tracking is proposed for the purpose of real-time tracking in video surveillance system. Moving object is detected through foreground detection. Then we extract three features of each moving objects such as centroid, area, and average luminance. At last, the similarity function is applied to tracking. It is proved that the method has good performance under dynamic circumstances for real-time tracking.
An emotionally intelligent user interface: modelling emotion for user engagement
Authors: Matthew Willis
Venue: Proceedings of the 19th Australasian conference on Computer-Human Interaction: Entertaining User Interfaces (2007)

This paper presents a model for simulating emotion and personality in interactive systems. This paper argues that by introducing simulated emotional responses and state dynamics to future systems, they will provide a more life-like, engaging and interactive experience to their users, and provide a more effective and efficient user interaction. Further, by simulating Emotional Intelligence in a system, a developer may be able to provide a more tailored user experience, and be provided with more control over the outcome of a user's interaction with their system. Emotion theory and expression are explored, and a model is presented based upon emotional states. Implementation of this model is then presented as an intelligent back end process that utilises dynamic video stream analysis that feeds into an interactive display. The proposed system, and its hardware implementation is presented, followed by a discussion of future areas of research.
Knowledge-Based Recognition of Utility Map Sub-Diagrams
Authors: S. Hickinbotham, A. Cohn
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01 (2007)

An integrated map of all utility services in a locale would facilitate better management of the road infrastructure and the utilities themselves. To meet this goal, there exists a need to integrate raster scans of paper maps into GIS by capturing the semantic relationships between the ob- jects in the drawings. In this context, commercially avail- able vectorisation algorithms do not produce a su fficiently rich object representation. We present a structural object recognition system that successfully isolates sectional sub- diagrams in maps of underground utilities. This is built upon a vectorisation system based on a constrained Delau- nay triangulation of pen strokes.
A Meta-Learning Failure Predictor for Blue Gene/L Systems
Authors: Prashasta Gujrati, Yawei Li, Zhiling Lan, Rajeev Thakur, John White
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

The demand for more computational power in science and engineering has spurred the design and deployment of ever-growing cluster systems. Even though the individual components used in these systems are highly reliable, the presence of large number of components inevitably increases the failure probability of such systems. Successful prediction of potential failures can greatly enhance various fault tolerance mechanisms used in large clusters, thereby mitigating the adverse impact of failures on system productivity and total cost of ownership. In this paper, we present a three-phase failure predictor to automatically process RAS events and further discover failure patterns for prediction in Blue Gene/L systems. In particular, this paper explores the use of metalearning to adaptively integrate base methods with the goal to boost prediction accuracy. Experiments with two RAS logs collected from Blue Gene/L systems at ANL and SDSC demonstrate the effectiveness of the proposed failure predictor.
Recent advances in evolvable systems-ices 96 (international conference on evolvable systems)
Authors: Ian Frank, Bernard Manderick, Tetsuya Higuchi
Venue: Evolutionary Computation (1997)

This paper reviews the developments in evolvable hardware systems presented at the First International Conference on Evolvable Systems (ICES 96). The main body of the review gives an overview of the 34 papers presented orally, splitting them into three broad groups according to whether they involve (1) evolving a fit solution to a problem as a member of a population of competing candidates, (2) evolving solutions that can individually learn from and adapt to their environments, or (3) the embryonic growth of solutions. We also review the discussion sessions of the conference and give pointers to related upcoming events.
Automating multidimensional design from ontologies
Authors: Oscar Romero, Alberto Abelló
Venue: Proceedings of the ACM tenth international workshop on Data warehousing and OLAP (2007)

This paper presents a new approach to automate the multidimensional design of Data Warehouses. In our approach we propose a semi-automatable method aimed to find the business multidimensional concepts from a domain ontology representing different and potentially heterogeneous data sources of our business domain. In short, our method identifies business multidimensional concepts from heterogeneous data sources having nothing in common but that they are all described by an ontology.
Active Web Service Registries
Authors: Martin Treiber, Schahram Dustdar
Venue: IEEE Internet Computing (2007)

Atom news feeds can work both as an infrastructure for distributed Web service registries and to inform users about Web service changes. The authors consider an approach in which every Web service provider offers a Web service registry news channel that serves as part of a globally distributed registry. They also apply this approach to a real-world case study. Their future plans include providing a Web service aggregator that offers additional metadata for Web service descriptions, which users could generate as they rate the services they use.
DIPS: A Framework for Distributed Intrusion Prediction and Prevention Using Hidden Markov Models and Online Fuzzy Risk Assessment
Authors: Kjetil Haslum, Ajith Abraham, Svein Knapskog
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

This paper proposes a Distributed Intrusion Prevention System (DIPS), which consists of several IPS over a large network (s), all of which communicate with each other or with a central server, that facilitates advanced network monitoring. A Hidden Markov Model is proposed for sensing intrusions in a distributed environment and to make a one step ahead prediction against possible serious intrusions. DIPS is activated based on the predicted threat level and risk assessment of the protected assets. Intrusions attempts are blocked based on (1) a serious attack that has already occurred (2) rate of packet flow (3) prediction of possible serious intrusions and (4) online risk assessment of the assets possibly available to the intruder. The focus of this paper is on the distributed monitoring of intrusion attempts, the one step ahead prediction of such attempts and online risk assessment using fuzzy inference systems. Preliminary experiment results indicate that the proposed framework is efficient for real time distributed intrusion monitoring and prevention.
A Predictive and Synchronized Neighborhood Tracking Scheme for Mobile Ad Hoc Networks
Authors: Hui Xu, Jinsung Cho, Sungyoung Lee
Venue: Proceedings of the 7th IEEE International Conference on Computer and Information Technology (2007)

In mobile ad hoc networks (MANETs), nodes mobility causes network topologies to change dynamically over time, which complicates important tasks such as broadcasting and routing. Neighborhood tracking is a task to determine the neighborhood local view of a mobile node in time which can facilitate the forwarding decision in the design of net- work protocols. In this paper, we propose a predictive syn- chronization solution to construct the updated and synchro- nized local view. Based on neighbors' historical informa- tion extracted from "Hello" messages, a node can track its time difference with any neighbor. Then the node can pre- dict each neighbor's future location at the same time based on certain mobility prediction model to construct synchro- nized neighborhood view by collecting predicted locations. Simulation results validate the accuracy of our proposed tracking schemes. 1
Synthesis of Finite State Algorithms in a Galois Field GF[pn]
Authors: W. R. English
Venue: IEEE Transactions on Computers (1981)

This correspondence describes a method for achieving synthesis of finite state algorithms by the use of a set of logic elements that execute field operations from the Galois field GF[pn]. The method begins with a definition of the algorithm to be synthesized in a completely specified finite state flow table form. A polynomial expansion of this flow table function is derived. A canonical sequential circuit corresponding to this polynomial expansion is defined. Subsequently, the given algorithm is synthesized using the canonical circuit by specification of a number of arbitrary constants in the canonical circuit. A mechanical method for deriving constants used in the canonical circuits is given. Finally, some estimates on complexity of the given circuit structure are stated assuming the most fundamental logic element structures.
Fault Recognition with Labeled Multi-category Support Vector Machine
Authors: Xue Wang, Daowei Bi, Sheng Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

Support vector machine is intrinsically a binary classifier providing no theoretically formulated procedure for multi-category classification. Several methods have been developed to extend it to multi-category problems. Combining strengths of them, an improved "labeled multi-category support vector machine" is proposed. The proposed method explicitly labels samples and performs multi-category classification with only a single support vector machine classifier. Labeling samples leads to the sample number disparity between positive and negative classes. The techniques of setting different cost parameters for different classes are employed to enhance the algorithm's performance. Generalization error bound estimates are theoretically derived by the new technique of maximal discrepancy. Experiments with a benchmark dataset show that the algorithm can accurately classify multi-category data. Rotor mechanical fault recognition applications confirm that the algorithm can efficiently perform multicategory fault detection and identification.
Note: On the complexity of 4-coloring graphs without long induced paths
Authors: Van Bang Le, Bert Randerath, Ingo Schiermeyer
Venue: Theoretical Computer Science (2007)

We show that deciding if a graph without induced paths on nine vertices can be colored with 4 colors is an NP-complete problem, improving a previous NP-completeness result proved by Woeginger and Sgall in 2001. The complexity of 4-coloring graphs without induced paths on five vertices remains open. We show that deciding if a graph without induced paths or cycles on five vertices can be colored with 4 colors can be done in polynomial time. A step in our algorithm uses the well-known and deep fact due to Grotschel, Lovasz and Schrijver stating that perfect graphs can be optimally colored in polynomial time.
A framework for the automated generation of power-efficient classifiers for embedded sensor nodes
Authors: Ari Y. Benbasat, Joseph A. Paradiso
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

This paper presents a framework for power-efficient detection in embedded sensor systems. State detection is structured as a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors (termed groggy wakeup), such that only the data necessary to determine the system state is collected at any given time. This classifier can be tuned to trade-off accuracy and power in a structured, parameterized fashion. An embedded instantiation of these classifiers, including real-time sensor control, is described. An application based on a wearable gait monitor provides quantitative support for this framework. The decision tree classifiers achieved roughly identical detection accuracies to those obtained using support vector machines while drawing three times less power. Both simulation and real-time operation of the classifiers demonstrate that our multi-tiered classifier determines states as accurately as a single-trigger (binary) wakeup system while drawing as little as half as much power and with only a negligible increase in latency.
Injection Guidance System for Cellular Microinjections
Authors: Pasi Kallio, Tuukka Ritala, Mikko Lukkari, Seppo Kuikka
Venue: International Journal of Robotics Research (2007)

In this paper we present a novel injection guidance system that assists an operator in the microinjection of living adherent cells. The system includes a micromanipulator, an injection system, an impedance measurement device and control software. The impedance measurement system detects changes in the resistance of an injection capillary during cell injections. From the measurement signal, the software determines parameters that can be used to detect several injection events such as cell&#x2014;capillary contact, capillary breakage and capillary clogging, which are all unavailable in the current microinjection systems. Real cell injection experiments indicate that injection success rates can be significantly increased: from 30&#x2014;40% without the system to 65% with the system.
Helix: The Architecture of the XMS Distributed File system
Authors: M. Fridrich
Venue: IEEE Software (1985)

With abstraction layering and system decomposition, all the user sees is one homogeneous system. In actuality, the architecture is supporting 15 LANs and close to 1000 workstations.
Generalized Association Rule Mining Algorithms based on Data Cube
Authors: Zhang Hong, Zhang Bo, Kong Ling-Dong, Cai Zheng-Xing
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

This paper defined a kind of multi-dimension data cube model, and presented a new formalization of generalized association rule based on data cube model. After comprehending the weaknesses of the current generalized association rule mining algorithms based on data cube, we proposed a new algorithm GenHibFreq which was suitable for mining multi-level frequent itemset based on data cube. By taking advantage of the item taxonomy, algorithm GenHibFreq reduced the number of candidate itemsets counted, and had better efficiency. We also designed an algorithm GenerateLHSs-Rule for generating generalized association rule from multi-level frequent itemset. Demonstrated through examples, algorithms proposed in this paper had better efficiency and less generated redundant rules than several existing mining algorithms, such as Cumulate, Stratify and ML_T2L1, and had good performance in flexibility, scalability and complexity and had new ideas on conducting the Generalized Association Rule Mining Algorithms in multi-dimension enviornment nad it also has great theroritical meaning and practical value..
VizTree: a tool for visually mining and monitoring massive time series databases
Authors: Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, Daonna M. Nystrom
Venue: Proceedings of the Thirtieth international conference on Very large data bases - Volume 30 (2004)

Moments before the launch of every space vehicle, engineering discipline specialists must make a critical go/no-go decision. The cost of a false positive, allowing a launch in spite of a fault, or a false negative, stopping a potentially successful launch, can be measured in the tens of millions of dollars, not including the cost in morale and other more intangible detriments. The Aerospace Corporation is responsible for providing engineering assessments critical to the go/no-go decision for every Department of Defense (DoD) launch vehicle. These assessments are made by constantly monitoring streaming telemetry data in the hours before launch. For this demonstration, we will introduce VizTree, a novel time-series visualization tool to aid the Aerospace analysts who must make these engineering assessments. VizTree was developed at the University of California, Riverside and is unique in that the same tool is used for mining archival data and monitoring incoming live telemetry. Unlike other time series visualization tools, VizTree can scale to very large databases, giving it the potential to be a generally useful data mining and database tool.
Digital Filter Implementation on 16-Bit Microcomputers
Authors: H. Nagle, V. Nelson
Venue: IEEE Micro (1981)

Today's 16-bit microprocessors with built-in multiply offer a new option for digital filter implementation, meeting the needs of a wide range of signal processing application.
The Ampos Multiprocessor?A Computer System for Laboratory Use
Authors: Malcolm Harrison, Owen Smith
Venue: IEEE Micro (1985)

This highly modular processor management system has been designed to provide computational facilities for biomedical research laboratories. It uses a command language and interprocessor communications similar to those of the Unix system.
Split-ballot voting: everlasting privacy with distributed trust
Authors: Tal Moran, Moni Naor
Venue: Proceedings of the 14th ACM conference on Computer and communications security (2007)

In this paper we propose a new voting protocol with desirable security properties. The voting stage of the protocol can be performed by humans without computers; it provides every voter with the means to verify that all the votes were counted correctly (universal verifiability) while preserving ballot secrecy. The protocol has "everlasting privacy": even a computationally unbounded adversary gains no information about specific votes from observing the protocol's output. Unlike previous protocols with these properties, this protocol distributes trust between two authorities: a single corrupt authority will not cause voter privacy to be breached. Finally, the protocol is receipt-free: a voter cannot prove how she voted even she wants to do so. We formally prove the security of the protocol in the Universal Composability framework, based on number-theoretic assumptions.
Maximum matchings in planar graphs via gaussian elimination
Authors: Marcin Mucha, Piotr Sankowski
Venue: Algorithmica (2006)

We present a randomized algorithm for finding maximum matchings in planar graphs in timeO(n &#x03c9;/2), where&#x03c9; is the exponent of the best known matrix multiplication algorithm. Since&#x03c9;<2.38, this algorithm breaks through theO(n 1.5) barrier for the matching problem. This is the first result of this kind for general planar graphs. We also present an algorithm for generating perfect matchings in planar graphs uniformly at random usingO(n &#x03c9;/2) arithmetic operations. Our algorithms are based on the Gaussian elimination approach to maximum matchings introduced in [16].