ppxscal/embedding_network huggingface dataset:
Hybrid Cache Architecture for High Speed Packet Processing
Authors: Zhen Liu, Kai Zheng, Bin Liu
Venue: Proceedings of the 13th Symposium on High Performance Interconnects (2005)

The exposed memory hierarchies employed in many network processors (NPs) are expensive and hard to be effectively utilized. On the other hand, conventional cashe cannot be directly incorporated into NP either because of its low efficiency in locality explotation for network applications. In this paper, a novel memory hierarchy component, called split control cache, is presented. The proposed scheme employs two independent low latency memory stores to temporarily hold the flow-based and application-relevant information, exploiting the different locality behaviors exhibited by these two types of data. Data movement is manipulated by specially designed hardware to relieve the programmers from details of memory management. Performance evaluation shows that this component can achieve a hit rate of over 90% with only 16 KB of memories in route lookup under link rate of OC-3c and provide enough flexibility for the implementation of most network applications.
Robust Autofocusing for Automated Microscopy Imaging of Fluorescently Labelled Bacteria
Authors: Volker Hilsenstein
Venue: Proceedings of the Digital Image Computing on Techniques and Applications (2005)

In this paper, we present a robust focus algorithm tailored to the requirements of automated microscopy of fluorescently labelled bacteria. We combine two strategies to make the algorithm robust against focus errors due to low bacteria densities and the presence of dirt. First, we acquire a number of focus estimates for different fields on each slide well using a correlation-based focus measure. A planar model is fitted to these initial estimates using robust regression. The fit parameters provide information about slide tilt and the variability of the focus positions. We use this to constrain the search range to the locally optimal range for each field. Second, we introduce a heuristic pre-filteringstep, based on a morphological area opening, into the computation of the focus measure to suppress false maxima arising from the presence of dirt particles. The effectiveness of both steps is demonstrated by focus results obtained for a sample slide.
Consistency Management among Replicas in Peer-to-Peer Mobile Ad Hoc Networks
Authors: Takahiro Hara, Sanjay K. Madria
Venue: Proceedings of the 24th IEEE Symposium on Reliable Distributed Systems (2005)

Recent advances in wireless communication along with Peer-to-peer (P2P) paradigm have led to increasing interest in P2P mobile ad hoc networks. In this paper, we assume an environment where each mobile peer accesses data items held by other peers which are connected by a mobile ad hoc network. Since peers¿ mobility causes frequent network partitions, replicas of a data item may be inconsistent due to write operations performed by mobile peers. In such an environment, the global consistency of data items is not desirable by many applications. Thus, new consistency maintenance based on local conditions such as location and time need to be investigated. This paper attempts to classify different consistency levels according to requirements from applications and provides protocols to realize them. We report simulation results to investigate the characteristics of these consistency protocols in a P2P wireless ad hoc network environment and their relationship with the quorum sizes.
A Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indexes
Authors: Hidenao Abe, Shusaku Tsumoto, Miho Ohsaki, Takahira Yamaguchi
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

In this paper, we present a novel rule evaluation support method for post-processing of mined results with rule evaluation models based on objective indexes. Post-processing of mined results is one of the key issues to make a data mining process successfully. However, it is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation procedures, we have developed the rule evaluation support method with rule evaluation models, which are obtained with objective rule evaluation indexes and evaluations of a human expert for each rule. Since the method is needed more accurate rule evaluation models, we have compared learning algorithms to construct rule evaluation models with the actual meningitis data mining result and actual rule sets from UCI datasets. Then we show the availability of our adaptive rule evaluation support method.
Dynamic Selection of Composite Web Services Based on a New Structured TCNN
Authors: Lei Yang, Yu Dai, Bin Zhang, Yan Gao
Venue: Proceedings of the IEEE International Workshop (2005)

In order to realize a high-quality and goodperformance service composition, based on current approach, we propose a new QoS-driven dynamic selection of composite Web Services, which takes account of both the QoS properties and interface parameters matching degree. When doing the selection, we aware that the task is more or less a multistage decision-making process. Motivated by transiently chaotic neural networks' high parallel performance and powerful computation ability, a new structured transiently chaotic neural network algorithm is proposed in this paper for such task. In order to make this algorithm more adaptable for multistage decision-making problem, we propose a new structured neural network to express the composed service instead of using the traditional neural networks, which minimizes the neurons involved and shows high performance than the earlier ones. Finally, through experimentation one can find that method proposed in this paper is more practical and effective than others.
Formal Analysis of Human-computer Interaction using Model-checking
Authors: Antonio Cerone, Peter A. Lindsay, Simon Connelly
Venue: Proceedings of the Third IEEE International Conference on Software Engineering and Formal Methods (2005)

Experiments with simulators allow psychologists to better understand the causes of human errors and build models of cognitive processes to be used in Human Reliability Assessment (HRA). This paper investigates an approach to task failure analysis based on patterns of behaviour, by contrast to more traditional event-based approaches. It considers, as a case study, a formal model of an air traffic control (ATC) system which incorporates controller behaviour. The cognitive model is formalised in the CSP process algebra. Patterns of behaviour are expressed as temporal logic properties. Then a model-checking technique is used to verify whether the decomposition of the operator's behaviour into patterns is sound and complete with respect to the cognitive model. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the analysis of the data provided by the experiments with the simulator. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.
The impact of traffic aggregation on the memory performance of networking applications
Authors: Javier Verdú, Jorge García, Mario Nemirovsky, Mateo Valero
Venue: Proceedings of the 2004 workshop on MEmory performance: DEaling with Applications , systems and architecture (2004)

The trend of the networking processing is to increase the intelligence of the routers (i.e. security capacities). This means that there is an increment in the workload generated per packet and new types of applications are emerging, such as stateful programs. On the other hand, Internet traffic continues to grow vigorously. This fact involves an increment of the traffic aggregation levels and overloades the processing capacities of the routers.In this paper we show the importance of traffic aggregation level on networking application studies. We also classify the applications according to the data management of the packet processing. Hence, we present the different impacts on the data cache performance depending on the application category. Our results show that traffic aggregation level may affect the cache performance depending on the networking application category. Stateful applications show a significant sensitivity to this impact.
An interactive system for mental face retrieval
Authors: Yuchun Fang, Donald Geman, Nozha Boujemaa
Venue: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval (2005)

We propose a system to "retrieve" the mental image of a face from a large database using Bayesian inference and relevance feedback. Since the "target image" exists only in the mind of the user, mental image retrieval differs sharply from standard, example-based retrieval and has not been widely studied. In designing the relevance feedback engine, we adopt probabilistic models for the display and answer processes. The answer model is designed to capture properties of human cognition in choosing among displayed faces. The images in each display are selected according to heuristics inspired by maximizing the conditional mutual information between the answer and the target given the previous feedback. Simulations and real tests validate show that the relevance feedback engine operates in real-time and locates the target in a reasonable number of displays.
High-performance copying garbage collection with low space overhead
Authors: Narendran Sachindran
Venue:  (2005)

Managed run-time environments such as Java and .NET are now used for a wide variety of applications. They provide a number of advantages to application developers, including reduced application development time, safer code, and portability. A major component of the run-time systems for these environments is the garbage collector, which relieves application developers from having to manage memory explicitly. While garbage collectors should provide good throughput and short response times with low space overheads, most modern garbage collectors that meet the first two requirements tend to impose a significant space overhead. Copying garbage collection, one of many garbage collection techniques, provides a number of advantages over other collection methods including cheap allocation and prevention of fragmentation. However, the space overhead of previous copying techniques have been particularly high. In this thesis, we first perform a detailed study of previous copying collection techniques, including generational copying collection and the Train collector. We identify limitations of the Train collector that cause it to perform poorly especially in the presence of large, cyclic data structures. We describe a new copying garbage collection technique, Mark-Copy, that extends generational copying collection, and overcomes the factor-of-two space overheads of a regular copying collector. Additionally, it overcomes some of the limitations of the Train collector. We show that Mark-Copy can provide high throughput while running in limited amounts of memory. We then present MC2, a collector that extends Mark-Copy and provides high throughput and short pause times while running with low space overhead, thus making it suitable for applications running on memory-constrained devices. These qualities also make MC 2 attractive for other environments, including desktops and servers.
Information extraction from multiple syntactic sources
Authors: Ralph Grishman
Venue:  (2005)

Information Extraction is the automatic extraction of facts from text, which includes detection of named entities, entity relations and events. Conventional approaches to Information Extraction try to find syntactic patterns based on deep processing of text, such as partial or full parsing. The problem these solutions have to face is that as deeper analysis is used, the accuracy of the result decreases, and one cannot recover from the induced errors. On the other hand, lower level processing is more accurate and it can also provide useful information. However, within the framework of conventional approaches, this kind of information can not be efficiently incorporated. This thesis describes a novel supervised approach based on kernel methods to address these issues. In this approach customized kernels are used to match syntactic structures produced from different preprocessing phases. Using properties of a kernel, individual kernels are combined into a composite kernel to integrate and extend all the information. The composite kernels can be used with various classifiers, such as Nearest Neighbor or Support Vector Machines (SVM). The main classifier we propose to use is SVM due to its ability to generalize in large dimensional feature spaces. We will show that each level of syntactic information can contribute to IE tasks, and low level information can help to recover from errors in deep processing. The new approach has demonstrated state-of-the-art performance on two benchmark tasks. The first task is detecting slot fillers for management succession events (MUC-6). For this task two types of kernels were designed, a surface kernel based on word n-grams and a kernel built on sentence dependency trees; the second task is the ACE RDR evaluation, which is to recognize relations between entities in text from newswire and broadcast news transcript. For this task, five kernels were built to represent information from sentence tokenization, syntactic parsing and dependency parsing. Experimental results for the two tasks will be shown and discussed.
Rate-based Flow-control for the CICQ Switch
Authors: Kenji Yoshigoe
Venue: Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary (2005)

A combined input and crosspoint queued (CICQ) switch with a flow control latency of round-trip time (RTT) packets requires each crosspoint (CP) buffer to hold the RTT packets in order to support any traffic pattern without blocking. In this paper, a distributed rate-based flow control internal to a CICQ switch is proposed. The new rate-based flow control allocates bandwidth to each virtual output queue (VOQ) using simple counters, and a minimal CP buffer requirement can be achieved without packet loss. The proposed rate-based flow control is shown to be more stable than the existing credit-based flow control previously proposed in the literature for realistic traffic while significantly reducing the CP memory size, thus making the implementation of a distributed CICQ switch practical.
Server directed file domain allocation for noncontiguous file access
Authors: Bin Jia, Mei-Hwa Chen, W. Maniatty
Venue: Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid (2004)

Parallel I/O systems frequently use collective I/O to improve the performance of applications that have small and noncontiguous data access patterns. The performance of a collective I/O implementation depends on how well its I/O libraries and parallel file system cooperate. We present a novel server directed file domain allocation (SDFDA) approach motivated by a study of interaction between MPICH/ROMIO and PVFS for noncontiguous file access. In our SDFDA approach, the file domains are bound to processes, according to the data layout scheme of I/O servers, as well as the number and placement of the ROMIO aggregators. We conducted an empirical study on an MPICH/PVFS cluster platform and compared the performance of our SDFDA approach with the existing ROMIO allocation approach. The results show that by providing a light correlation between MPICH/ROMIO and PVFS, our approach conforms I/O distribution to PVFS file striping and achieves higher performance improvement for noncontiguous file access.
Zero Steady State Current Power on Reset Circuit with Brown-Out Detector
Authors: Sanjay Kumar Wadhwa, G. K. Siddhartha, Anand Gaurav
Venue: Proceedings of the 19th International Conference on VLSI Design held jointly with 5th International Conference on Embedded Systems Design (2006)

A novel Power on reset (POR) circuit with Brown out (BO) detector having zero steady state current consumption is proposed. The circuit has been designed in 65nm CMOS process at a single supply of 1.1V. Both the POR and BO thresholds are independently adjustable in the circuit. Simulation results show that the POR threshold does not depend upon the supply ramp-rate at fixed process and temperature corner. BO circuit works for a large range of supply ramp down rates. Due to zero steady state current consumption, the proposed circuit is well suited for low power applications.
Cherry-MP: Correctly Integrating Checkpointed Early Resource Recycling in Chip Multiprocessors
Authors: Meyrem Kyrman, Nevin Kyrman, Jose F. Martynez
Venue: Proceedings of the 38th annual IEEE/ACM International Symposium on Microarchitecture (2005)

Checkpointed Early Resource Recycling (Cherry) is a recently-proposed micro-architectural technique that aims at improving critical resource utilization by performing aggressive resource recycling decoupled from instruction retirement, using a checkpoint/rollback mechanismto recover from occasional incorrect execution. In this paper, we explore correctness and performance issues that arise when Cherryenabled processors are used in chip multiprocessor architectures. We propose mechanisms to address cache coherence, memory consistency, and forward progress issues in such environments. We also provide quantitative insight on the performance impact of the Cherry mechanism on parallel processing.
The effects of prior knowledge and hyperlinks on learning from a computerized aid
Authors: Partha S. Mohapatra
Venue:  (2005)

Firms can potentially increase their organizational memory and enhance learning from computerized aids. One potential application of computerized aids in auditing firms is learning about strategic systems auditing (SSA), which is increasingly popular in these firms. Even the AICPA (American Institute of Certified Public Accountants) has endorsed the use of SSA for evaluating audit evidence (AICPA, 2002; O'Donnell and Schultz, 2005). However, recently SSA has been criticized for its misapplication in detecting accounting frauds. Better expertise of SSA among auditors might help to avoid the problems that have been observed with this method. Thus, it is essential to explore factors that might enhance or hinder learning from SSA-based computerized aids. Using a 3 x 2 between-participant study, I research two factors that might affect learning from SSA-based computerized aids: (1) the prior transaction-based schema, and, (2) the hyperlinks. One hundred ninety four accounting students from eight universities participated in an Internet-based study. The study induced a transaction-based schema in one set of randomly assigned participants, while another set of participants did not receive the induced schema. All participants used a SSA-based computerized aid which either included or excluded hyperlinks. Results show that participants with the induced schema were unhindered in learning SSA compared with participants without induced schema. Thus, induced schema did not affect SSA learning. The study also finds that carefully designed hyperlinks may improve SSA learning. Additionally, participants provided with hyperlinks increased effort. Hence, we find no evidence that prior knowledge hinders learning SSA, and, that well-designed hyperlinks can facilitate the use of SSA.
A Low-Power Partitioning Methodology by Maximizing Sleep Time and Minimizing Cut Nets
Authors: Payam Ghafari, Ehsan Mirhadi, Mohab Anis, Shawki Areibi, Mohamed Elmasry
Venue: Proceedings of the Fifth International Workshop on System-on-Chip for Real-Time Applications (2005)

The rising objective in VLSI design is to minimize the average power consumption. Sleep time maximization along with minimization of cut nets are explored as ways to decrease and minimize the power consumption. The major motivation is to deactivate parts of a circuit when they are idle, while simultaneously keeping the cut nets as low as possible. This dual objective problem is separately formulated as two single objectives and then combined into one normalized objective function. The joint problem is shown to be NP-hard, hence heuristic approaches were introduced. A modified version of the Genetic algorithm is presented along side with an efficient implementation of a geometric iterative improvement technique using segmented trees. Results are presented for three hypothetical test cases and the results demonstrate more than 40% improvement.
Intelligent exploration method for XCS
Authors: Ali Hamzeh, Adel Rahmani
Venue: Proceedings of the 2005 workshops on Genetic and evolutionary computation (2005)

Exploration/Exploitation equilibrium is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. In this paper, an intelligent method is proposed to control exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS. It
Analysis of game playing agents with fingerprints
Authors: Eun-Youn Kim
Venue:  (2005)

Evolutionary computation (EC) can create a vast number of strategies for playing simple games in a short time. Analysis of these strategies is typically more time-consuming than their production. As a result, analysis of strategies produced by an EC system is often lacking or restricted to the extraction of superficial summary statistics. This thesis presents a technique for extracting a functional signature from evolved agents that play games. This signature can be used as a visualization of agent behavior in games with two moves and also provides a numerical target for clustering and other forms of automatic analysis. The fingerprint can be used to induce a similarity measure on the space of game strategies. This thesis develops fingerprints in the context of the iterated prisoner's dilemma; we note that they can be computed for any two player simultaneous game with a finite set of moves. When using a clustering algorithm, the results are strongly influenced by the choice of the measure used to find the distance between or to compare the similarity of the data being clustered. The Euclidean metric, for example, rates a convex polytope as the most compact type of object and builds clusters that are contained in compact polytopes. Presented here is a general method, called multi-clustering, that compensates for the intrinsic shape of a metric or similarity measure. The method is tested on synthetic data sets that are natural for the Euclidean metric and on data sets designed to defeat k-means clustering with the Euclidean metric. Multi-clustering successfully discovers the designed cluster structure of all the synthetic data sets used with a minimum of parameter tuning. We then use multi-clustering and filtration on fingerprint data. Cellular representation is the practice of evolving a set of instructions for constructing a desired structure. This thesis presents a cellular encoding for finite state machines and specializes it to play the iterated prisoner's dilemma. The impact on the character and behavior of finite state agents of using the cellular representation is investigated. For the cellular representation resented a statistically significant drop in the level of cooperation is found. Other differences in the character of the automaton generated with a direct and cellular representation are reported.