ppxscal/embedding_network huggingface dataset:
On the Feasibility of Time Estimation under Isolation Conditions in Wireless Sensor Networks
Authors: Daniela Tulone
Venue: Algorithmica (2007)

We study the problem of providing a sensor with an accurate estimate of the time, from a novel perspective which is complementary to the well-studied clock synchronization problem. More precisely, we analyze the case in which a sensor node is temporarily unable to run a clock synchronization protocol due to failures or intermittent connectivity, or is willing to skip one or more clock adjustments to save energy, but still requires an accurate estimate of the reference time. We propose and analyze two simple and efficient clock reading methods, one deterministic and the other probabilistic, which are designed to work in synergy with a clock synchronization protocol. Our deterministic method achieves a better time accuracy by exploiting information regarding the sign of the deviation of the hardware clock from the reference time. This algorithm leads to noticeable energy savings since it can be applied to reduce the frequency of the periodic clock adjustments by a factor of 2, while maintaining the same error bound. Moreover, our method is of theoretical interest since it shows how a stronger but realistic clock model leads to a refinement of the optimality bound for the maximum deviation of a clock that is periodically synchronized. We also propose two simple versions of this algorithm: a method that guarantees the monotonicity of the clock values, and a generalization that improves the accuracy in case of clock stability. Our probabilistic method is based on time series forecasting, and provides a probabilistically accurate estimate of the reference time with a constant error bound. It is more flexible than our previous methods since it does not depend on the frequency at which clock synchronization occurs, and can be dynamically tuned according to the application requirements and resource availability. All these methods have broad applicability for their generality. In sensor networks they can be applied to improve the clock accuracy of a sensor node in conditions of network isolation, or to reduce the frequency of the clock adjustments, thus saving energy and increasing the system lifetime.
In vivo evaluation of the nitroimidazole-based thioflavin-T derivatives as cerebral ischemia markers
Authors: Taiwei Chu, Zejun Li, Xinqi Liu, Xiangyun Wang
Venue: Journal of Biomedical Imaging (2007)

Timely imaging and accurate interpretation of cerebral ischemia are required to identify patients who might benefit from more aggressive therapy, and nuclear medicine offers a noninvasive method for demonstrating cerebral ischemia. Three nitroimidazole-based thioflavin-T derivatives, N-[4-(benzothiazol-2-yl)phenyl]-3-(4-nitroimidazole-1-yl) propanamide (4NPBTA), N-[4-(benzothiazol-2-yl)phenyl]-3-(4-nitroimidazole-1-yl)-N-methylpropanamide (4NPBTA-1), and N-[4-(benzothiazol-2-yl)phenyl]-3-(2-nitroimidazole-1-yl) propanamide (2NPBTA), were radioiodinated and evaluated as possible cerebral ischemia markers. In normal mice, these compounds showed good permeation of the intact blood-brain barrier (BBB), high initial brain uptake, and rapid washout. In gerbil stroke models that had been subjected to right common carotid artery ligation to produce cerebral ischemia, [131I]2NPBTA, uptake in the right cerebral hemisphere decreased more slowly than that of the left, and the right/left hemisphere uptake ratios increased with time. Also, the right/left hemisphere uptake ratios correlated positively with the severity of the stroke. The results showed that [131I]2NPBTA had a specific location in the cerebral ischemic tissue. This represented a first step in finding new drugs and might provide a possible cerebral ischemic marker.
PNN Algorithm for PC Grid System
Authors: Akiyoshi Wakatani
Venue: Proceedings of the International Conference on Software Engineering Advances (2007)

PC Grid is a promising platform to achieve a high performance computing with less construction cost. A kind of parameter sweep applications (PSA) can be efficiently carried out on the PC Grid, but there exist parallel applications are not suitable for it.We focus on PNN (Pairwise Nearest Neighbor) codebook generation algorithm for the VQ compression algorithm and propose a parallel version of the PNN algorithm suitable for the PC Grid, called "One-step Grid PNN", as well as "multistep Grid PNN", which reduces the computation complexity on a master computer. The Grid PNN is a modified version of the PNN algorithm that creates a different codebook than the original PNN does, thus the quality of a codebook created by using the Grid PNN may be worse than that of a codebook by the original PNN. However, our experimental results show that the quality of the codebook is almost same as that of the original one. We also confirm the effectiveness of the one-step and multi-step Grid PNN by the evaluation of the computational complexity of the algorithm and the preliminary experiment executed on a PC cluster system.
Hierarchical Classification for Imbalanced Multiple Classes in Machine Vision Inspection
Authors: Bing Luo, Yun Zhang
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

Quality inspection based on machine vision is a multi-classification for imbalanced samples from many minority default-classes and a majority normal-class. Traditional methods seeking classification accuracy over a full range of instances are not suitable to deal with this case, since they tend to classify all samples into the majority class, usually the less important class. This paper proposed a hierarchical classification method that a simple bi-classifier with less features input made out most normal-class samples with a permitted low error rate for minority samples, then the rest less imbalanced samples were learned to establish a complicated multi-classifier with more features input. In classification after learning, two classifiers worked parallel and the simple classifier of the first layer can end the second one when normal-class result has been got. Comparative experimental results showed that this approach could effectively improve learning performance and accelerate classification speed.
An empirical study of the impact of team size on software development effort
Authors: Parag C. Pendharkar, James A. Rodger
Venue: Information Technology and Management (2007)

In this paper, we investigate the impact of team size on the software development effort. Using field data of over 200 software projects from various industries, we empirically test the impact of team size and other variables--such as software size in function points, ICASE tool and programming language type--on software development effort. Our results indicate that software size in function points significantly impacts the software development effort. The two-way interactions between function points and use of ICASE tool, and function points and language type are significant as well. Additionally, the interactions between team size and programming language type, and team size and use of ICASE tool were all significant.
Special Feature: Developing a Multiple-Instructon-Stream Single-Chip Processor
Authors: W. J. Kaminsky
Venue: Computer (1979)

Multiprocessing within a chip may be the next step in the evolution of single-chip microprocessors. The methodology described here achieves multiple-instruction-stream concurrency with a shared-resource pipeline architecture.
A Comparison of Shanghai Housing Price Index Forecasting
Authors: Xiangsheng Xie, Gang Hu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

We forecast Shanghai housing price index using the classical time series analysis method: auto-regressive integrated moving average (ARIMA) model and two nonparametric techniques: artificial neural networks (NN) and support vector machines (SVMs). By evaluating prediction errors, we find that NN method and SVM method are obviously superior to ARIMA for the long-term forecast. It shows that NN model and SVM model are better the ability of generalization. Our study also shows that the forecasting results of NN method and SVM method are more accurate than ARIMA in the short-time forecast.
AWafer-Level Defect Screening Technique to Reduce Test and Packaging Costs for "Big-D/Small-A" Mixed-Signal SoCs
Authors: S. Bahukudumbi
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

Product cost is a key driver in the consumer electronics market, which is characterized by low profit margins and the use of a variety of "big-D/small-A" mixed-signal system-on-chip (SoC) designs. Packaging cost has recently emerged as a major contributor to the product cost for such SoCs. Wafer-level testing can be used to screen defective dies, thereby reducing packaging cost. We propose a new correlation-based signature analysis technique that is especially suitable for mixed-signal test at the wafer-level using low-cost digital testers. The proposed method overcomes the limitations of measurement inaccuracies at the wafer-level. A generic cost model is developed to evaluate the effectiveness of wafer-level testing of analog and digital cores in a mixed-signal SoC, and to study its impact on test escapes, yield loss and packaging costs. Experimental results are presented for a typical mixed-signal "big-D/small-A" SoC, which contains a large section of flattened digital logic and several large mixed-signal cores.
A New Auto-focusing Method Based on the Center Blocking DCT
Authors: Quan Feng, Ke Han, Xiu-chang Zhu
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

First, this paper analyses the imaging relations of the lens system and the properties of image clarity evaluation function under the condition where the object distances of many objects are different. Then, a center window method is employed to stand out the interesting objects. Finally, the image clarity evaluation function based on the center blocking DCT (Discrete Cosine Transform / DCT) method is proposed for evaluating the image clarity in this paper. Experimental results indicate that the proposed method can effectively focus the images both under long-short-object-distance situation and under long-object- distance situation.
Emulating an embedded firewall
Authors: Clifford Neuman, Deepak Dayama, Arun Viswanathan
Venue: Proceedings of the DETER Community Workshop on Cyber Security Experimentation and Test on DETER Community Workshop on Cyber Security Experimentation and Test 2007 (2007)

The Adventium Labs Embedded Distributed Firewall provides a simple interface for securely managing approved network flows between computers on a network. A "conversation" manager provides a simple interface for managing flows, defining the connections authorized between nodes on a network. These policies are enforced in hardware embedded in the network interface card of each computer. The policies are managed to create groups of communicating machines and services and to exclude undesired traffic. This paper describes the emulation of the Adventium Labs distributed embedded firewall, using an additional node associated with each user node emulated on the DETER testbed. We provide observations on our implementation and current experiments, and discuss how the emulation can be used by other experimenters.
Comparison of Data Mining and Neural Network Methods on Aero-engine Vibration Fault Diagnosis
Authors: Dongxiang Jiang, Kai Xiong, Yongshan Ding, Kai Li
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Data mining and Artificial Neural Network (ANN) have been extensively applied on machinery fault diagnosis. Aero-engine, as one kind of rotating machine with complex structure and high rotating speed, has complicated vibration faults. ANN is a good tool for aero-engine fault diagnosis, since they have strong ability to learn complex nonlinear functions. Data mining has advantages of discovering knowledge from mountain of data, providing a simple way to interpret complex decision problem, and automatically extract diagnostic rules to replace the expert's advice. This paper presents application of the two methods on aero-engine vibration fault diagnosis and then makes a comparison between them. From the study of this paper, both the two methods are effective on aero- engine vibration fault diagnosis, while each of them has its individual quality.
An Adaptive Particle Swarm Optimization for Global Optimization
Authors: Ziyang Zhen, Zhisheng Wang, Yuanyuan Liu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

The paper suggests a new modified approach to improve the performance of particle swarm optimization (PSO). Inspired by the intelligent behaviors of the natural biotic populations, the modified PSO is based on an adaptive strategy, the particle should stop the inertia movement to enhance the learning from its experiences and its neighbors when it is found to be in wrong searching direction, and stop the learning process to fly straight when it is found to be the nearest to the destination in the swarm. Furthermore, four different forms of the adaptive PSO model are presented. Comparison results with the standard PSO on the examination of some unconstrained and constrained global optimization functions show the effectiveness of the new modified approach.
Integrating Access-Oriented Programming into a Multiparadigm Environment
Authors: M. J. Stefik
Venue: IEEE Software (1986)

The Loops knowledge programming system integrates function-oriented, system object-oriented, rule-oriented, and something notfound in most other systems access-oriented programming.
Operator scheduling in a data stream manager
Authors: Don Carney, Uğur Çetintemel, Alex Rasin, Stan Zdonik, Mitch Cherniack, Mike Stonebraker
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

Many stream-based applications have sophisticated data processing requirements and real-time performance expectations that need to be met under high-volume, time-varying data streams. In order to address these challenges, we propose novel operator scheduling approaches that specify (1) which operators to schedule (2) in which order to schedule the operators, and (3) how many tuples to process at each execution step. We study our approaches in the context of the Aurora data stream manager. We argue that a fine-grained scheduling approach in combination with various scheduling techniques (such as batching of operators and tuples) can significantly improve system efficiency by reducing various system overheads. We also discuss application-aware extensions that make scheduling decisions according to per-application Quality of Service (QoS) specifications. Finally, we present prototype-based experimental results that characterize the efficiency and effectiveness of our approaches under various stream workloads and processing scenarios.
An Application of Statistical Databases in Manufacturing Testing
Authors: S. P. Ghosh
Venue: IEEE Transactions on Software Engineering (1985)

This paper discusses some applications of statistical and database techniques for integrating tests in manufacturing products. It shows how statistical databases can be used for automatically controlling a manufacturing process in real time. Some new statistical methods of manufacturing testing, e. g., test-compression, testing-by-sampling, testing-by-factorial-design, are discussed. All these techniques are possible because of the availability of a statistical database.
Internal Model Control Based on Dynamic Fuzzy Neural Network
Authors: ZhiJun Zhang, XueMiao Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

A novel internal model control (IMC) approaches is proposed to control for process with large time delays. A dynamic fuzzy neural network (DFNN) was applied to model the process and its mathematical inverse to control the process. The presented strategy can overcome the unstable problem caused by time delays and the problem of time consuming and complex and incapable obtaining mathematical inverse of model using the classical internal model control. The automatic configuration and learning of the networks is carried out by using new adding techniques and on-line learning algorithm. The effectiveness of the proposed control scheme is verified by simulated results.
Proceedings of the ACM first Ph.D. workshop in CIKM
Venue: Conference on Information and Knowledge Management (2007)

PIKM 2007 is the first Ph.D. Workshop in CIKM. The goal of this workshop is to encourage Ph.D. students to present their dissertation research at a relatively early stage. The targeted students are those with a focus in any of the CIKM research tracks, i.e., databases, information retrieval and knowledge management. Interdisciplinary work across the three tracks is particularly encouraged. It is noticed that a broad range of topics are addressed in CIKM related to various issues in databases and information systems, information retrieval and semantic web, knowledge management and data mining. The CIKM conference with its confluence several research tracks provides an excellent opportunity for learning about the latest developments in the respective fields. It provides a good environment for a Ph.D. workshop where graduate students can expose their research early in their academic career. Particularly, such a workshop encourages further studies across the three major fields addressed by CIKM: databases, information retrieval and knowledge management. The PIKM workshop enables graduate students to present their dissertation proposals and / or ongoing research on their dissertation sub-problems. It gives them an opportunity to get feedback on their work from researchers worldwide. The reviewers' comments help them assess and possibly revise their work. Selected papers are published in the workshop proceedings. This gives considerable recognition to the work of the students allowing them to showcase their research. Moreover, students attending the workshop get an opportunity to critique the work of their peers. This promotes an open forum for discussion similar to that in a doctoral consortium. A wide range of topics on any area in databases, information retrieval and knowledge management is presented at the PIKM workshop. The areas of interest are similar to those at the CIKM main conference. Although this workshop has been the first of its kind, we received around 40 submissions which exceeded our expectations. After a tough review process 20 papers have been selected for publication in PIKM.
On the Collaborative Development of Para-Consistent Conceptual Models
Authors: Ebrahim Bagheri, Ali A. Ghorbani
Venue: Proceedings of the Seventh International Conference on Quality Software (2007)

There have been various proposals for the formalization of appropriate viewpoint-based frameworks. Each of these approaches have been devised with a specific motive and to address an important concern. The model that we pro- pose in this paper attempts to provide a basis for conceptual model integration particularly with the existence of partial ignorance and uncertainty. The model attempts to formal- ize the degree of uncertainty present in experts' expressions, and proposes tools for conceptual model integration and formal consensus building between the involved viewpoints. Metrics for measuring integration effectiveness have also been proposed in this paper. The model proposed in this paper has been employed in a case study to collaboratively develop a conceptual model for the Pet Store application.
X-Tolerant Compactor with On-Chip Registration and Signature-Based Diagnosis
Authors: Jerzy Tyszer, Janusz Rajski, Grzegorz Mrugalski, Nilanjan Mukherjee, Mark Kassab, Wu-Tung Cheng, Manish Sharma, Liyang Lai
Venue: IEEE Design Test (2007)

This article presents a two-stage test response compactor with scan selection logic and an on-chip compare-and-response collector. This compactor is capable of handling a wide range of X state profiles, offers compression far higher than the ratio of scan chains to compactor outputs, and provides excellent diagnostic resolution.
A cluster based mobility prediction scheme for ad hoc networks
Authors: Lyes Dekar, Hamamache Kheddouci
Venue: Ad Hoc Networks (2008)

The ad hoc networks are completely autonomous wireless networks where all the users are mobile. These networks do not work on any infrastructure and the mobiles communicate either directly or via other nodes of the network by establishing routes. These routes are prone to frequent ruptures because of nodes mobility. If the future movement of the mobile can be predicted in a precise way, the resources reservation can be made before be asked, which enables the network to provide a better QoS. In this aim, we propose a virtual dynamic topology, which on one hand, will organize the network as well as possible and decreases the impact of mobility, and on the other hand, is oriented user mobility prediction. Our prediction scheme uses the evidence theory of Dempster-Shafer in order to predict the future position of the mobile by basing itself on relevant criteria. These ones are related to mobility and network operation optimisation. The proposed scheme is flexible and can be extended to a general framework. To show the relevance of our scheme, we combine it with a routing protocol. Then, we implemented the prediction-oriented topology and the prediction scheme which performs on it. We implemented also a mobility prediction based routing protocol. Simulations are made according to a set of elaborate scenarios.
Modeling and Simulating Terrorist Networks in Social and Geospatial Dimensions
Authors: Il-Chul Moon, Kathleen M. Carley
Venue: IEEE Intelligent Systems (2007)

Over time, people change whom they interact with and where they are. Understanding how changes in social and geospatial relations interact is critical to a number of areas--from general social change to counternarcotics and counterterrorism. For instance, as terrorists attempt different tasks, they move to new locations and interact with different groups. This article introduces a simple theoretical multiagent model for reasoning about the criticality of agents and locations as agent interactions coevolve in geographical and social spaces. The model simulates social changes in whom agents interact with and spatial changes in where they relocate to as a function of learning and social-network evolutions. The analysis suggests that terrorists will disperse around the world rather than gather at a specific location. However, terrorists who have been the center of social networks will stay the same. This model helps us gain insights into the complexities of organizations evolving in the social and the geospatial dimensions simultaneously. This article is part of a special issue on social computing.
Boolean Differential Calculus and its Application to Switching Theory
Authors: A. Thayse
Venue: IEEE Transactions on Computers (1973)

After a brief outline of classical concepts relative to Boolean differential calculus, a theoretical study of the main differential operators is undertaken. Algebraic equations relating the classical concepts of prime implicants and of the discrete Fourier transform of a Boolean function to the differential operators are derived. Application of these concepts to several important problems arising in switching practice is mentioned.