ppxscal/embedding_network huggingface dataset:
Control of overhead cranes using a fuzzy logic controller
Authors: Michael J. Nalley, Mohamed B. Trabia
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2000)

Transportation of massive loads using overhead cranes can excite undesirable payload swinging. These oscillations have to be damped before other payload manipulation is done. A fuzzy logic controller moves the overhead crane along a desired path while ensuring that the payload is swing-free at the end of the motion. The control action is divided between displacement and swing controllers to enable tuning the controllers. The fuzzy controller is used with a swing-damped bang-bang motion profile based on the natural frequency of the payload is used that ensures that large amplitudes are not excited. Examples, along with comparisons with a PD controller, are included. Experimental implementation of the proposed controller is presented.
SOR: A Static File Assignment Strategy Immune to Workload Characteristic Assumptions in Parallel I/O Systems
Authors: Tao Xie
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

The problem of statically assigning nonpartitioned files in a parallel I/O system has been extensively investigated. A basic workload characteristic assumption of existing solutions to the problem is that there exists a strong inverse correlation between file access frequency and file size. In other words, the most popular files are typically small in size, while the large files are relatively unpopular. Recent studies on the characteristics of web proxy traces suggested, however, the correlation, if any, is so weak that it can be ignored. Hence, the following two questions arise naturally. First, can existing algorithms still perform well when the workload assumption does not hold? Second, if not, can one develop a new file assignment strategy that is immune to the workload assumption? To answer these questions, in this paper we first evaluate the performance of three well-known file assignment algorithms with and without the workload assumption, respectively. Next, we develop a novel static file assignment strategy for parallel I/O systems, called static round-robin (SOR), which is immune to the workload assumption. Comprehensive experimental results show that SOR consistently and noticeably improves the performance in terms of mean response time over the existing schemes.
Structure and semantics for expressive text kernels
Authors: Stephan Bloehdorn, Alessandro Moschitti
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.
Immunology, diversity, and homeostasis: The past and future of biologically inspired computer defenses
Authors: Anil Somayaji
Venue: Information Security Tech. Report (2007)

While biological metaphors have a long history of use in computer security, they have been more successful in describing malicious software than in helping to create better defenses. Immune system mechanisms, diversity, and homeostasis have helped inspire the development of promising computer security technologies. To fulfill the promise of biologically inspired security, however, more work is needed in understanding how living systems defend themselves and how those ideas can be brought to computers. This paper presents past successes, limitations, and opportunities for future work in this promising area. It then also addresses why there are significant barriers to such advances.
Aspects of Broad Folksonomies
Authors: Mathias Lux, Michael Granitzer, Roman Kern
Venue: Proceedings of the 18th International Conference on Database and Expert Systems Applications (2007)

Folksonomies, collaboratively created sets of metadata, are becoming more and more important for organising information and knowledge of communites in the Web. While for a single user the difference to keyword assignment is marginal, the power of folksonomies emerges from the collaborative aspects. Folksonomies are already issue of research. Within this publication we analyse underlying statistical properties of broad folksonomies aiming to identify laws and characteristics, which allow inferring properties for folksonomy based retrieval. The actual benefit of folksonomies for retrieval and the derived methods are concluded from experiments with aggregated data from del.icio.us.
Knowledge and Database Management
Authors: G. Wiederhold
Venue: IEEE Software (1984)

Artificial intelligence techniques are effective in dealing with the complexity of large databases. Their use can move well-understood human decision-making processes into the computer system.
Streaming Random Forests
Authors: Hanady Abdulsalam, David B. Skillicorn, Patrick Martin
Venue: Proceedings of the 11th International Database Engineering and Applications Symposium (2007)

Many recent applications deal with data streams, conceptually endless sequences of data records, often arriving at high flow rates. Standard data-mining techniques typically assume that records can be accessed multiple times and so do not naturally extend to streaming data. Algorithms for mining streams must be able to extract all necessary information from records with only one, or perhaps a few, passes over the data. We present the Streaming Random Forests algorithm, an online and incremental stream classification algorithm that extends Breiman's Random Forests algorithm. The Streaming Random Forests algorithm grows multiple decision trees, and classifies unlabelled records based on the plurality of tree votes. We evaluate the classification accuracy of the Streaming Random Forests algorithm on several datasets, and show that its accuracy is comparable to the standard Random Forest algorithm.
Application of Radial Basis Function Network and Locality Preserving Projections for Face Recognition
Authors: Jian-qiang Mei, Zheng-guang Liu, Ming Ming
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

Locality Preserving Projections (LPP) is a linear method that optimally preserves the local structure of the data set. However, because of the continuing existence of transformation difference, LPP subspace is failed to detect the important nonlinear variations of the face manifold. In order to improve the recognition performance, we propose to use radial basis function network (RBFN) to classify the features in the LPP subspace. The Multi-quadrics function is taken as the activation function of the RBFN and the hidden layer of RBFN is trained via the gradient descent algorithm in our approach. The experimental results show that the LPP+RBFN method has achieved a better rate than Laplacianfaces, Fisherfaces and Eigenfaces.
OCTOPUS: A Hybrid Scheduling Strategy for P2P VoD Services
Authors: Xiaofei Liao, Hai Jin
Venue: Proceedings of the Sixth International Conference on Grid and Cooperative Computing (2007)

Peer-to-Peer (P2P) networks have been shown to be a promising approach to provide large-scale Video on Demand (VoD) services over Internet for its potential high scalability. However, for a normal peer, how to efficiently schedule media data to multiple asynchronous peers for VoD services in such networks remains a major challenge. Hereby we propose a hybrid scheduling scheme for data dissemination in P2P VoD system in this paper, called OCTOPUS, which is combined with adaptive scheduling scheme at normal time and special scheduling strategy emergently. Usually each peer is given a value, meaning scheduling bandwidth to represent service capacity, which is an integrated evaluation for peer's bandwidth and services ability, also including statistical characteristics about historical data transferring. According to the value, one peer can adjust the expectation of each neighbor's abilities and send scheduling request of media data to all partners. In case of failure scheduling, emergent scheduling scheme will be called and limited data requests will be sent to media servers. OCTOPUS scheme helps to make sufficient utilization of end-peers' resource, alleviate the load of source server and improve their scalability. Experimental result from a P2P VoD system, GridCast, based on OCTOPUS schemes, shows that OCTOPUS achieves obvious effect.
On Fourier Transforms Over Extensions of Finite Rings
Authors: D. P. Maher
Venue: IEEE Transactions on Computers (1980)

A divisibility criterion is given for the existence of a Fourier transform over algebraic extensions of the ring of integers mod M which is generally easy to apply. We also present examples of how such transforms may be used to compute two-dimensional convolutions.
Implementing and Evaluating Security Controls for an Object-Based Storage System
Authors: Zhongying Niu, Ke Zhou, Dan Feng, Hong Jiang, Frank Wang, Hua Chai, Wei Xiao, Chunhua Li
Venue: Proceedings of the 24th IEEE Conference on Mass Storage Systems and Technologies (2007)

This paper presents the implementation and performance evaluation of a real, secure object-based storage system compliant to the T10 OSD standard. In contrast to previous work, our system implements the entire three security methods of the OSD security protocol defined in the standard, namely CAPKEY, CMDRSP and ALLDATA, and an Oakley-based authentication protocol by which the Metadata Server (MDS) and client can be sure of each other's identities. Moreover, our system supports concurrent operations from multiple clients to multiple OSDs. The MDS, a combination of security manager and storage/policy manager, performs access control, global namespace management, and concurrency control. We also evaluate the performance and scalability of our implementation and compare it with iSCSI, NFS and Lustre storage configurations. The overhead of access control is small: compared with the same system without any security mechanism, bandwidth for reads and writes with the CAPKEY and CMDRSP methods decreases by less than 5%, while latency for metadata operations with any of the security methods increases by less than 0.3 ms (5%). The system with the ALLDATA method suffers a higher performance penalty: large sequential accesses run at 46% and 52% of the maximum bandwidth of unsecured storage for reads and writes respectively. The aggregate throughput scales with the number of OSDs (up to 8 in our experiments). The overhead of the SET KEY commands for partition and working keys refreshed frequently is less than 2 ms.
Classification of gene expression data using fuzzy logic
Authors: Lucila Ohno-Machado, Staal Vinterbo, Griffin Weber
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2002)

Microarray technologies have allowed the measurement of expression of multiple genes simultaneously. Gene expression levels can be used to classify tissues into diagnostic or prognostic categories. As measurements from different microarray technologies are made in different scales (which are not guaranteed to be linear and not easily re-scalable), it is helpful to develop an easy-to-interpret technology-independent classification scheme. To capture the essentials of the problem of classification using gene expression data, we show how fuzzy logic can be applied using two examples. Using information from genes previously shown to be important, the classification performance of the fuzzy inference is similar to that of other classifiers, but simpler and easier to interpret. The fuzzy inference system has the theoretical advantage that it does not need to be retrained when using measurements obtained from a different type of microarray. Although the data sets for gene expression analysis utilized in this paper are relatively small, they are among the largest available in this domain.
Balancing accuracy and parsimony in genetic programming
Authors: Byoung-Tak Zhang, Heinz Mühlenbein
Venue: Evolutionary Computation (1995)

Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this article we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.
Fast median filters based on histogram and multilevel staged search
Authors: Quanhua Tang, Yan Zhou, Jine Lei
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

This paper describes the algorithms and testing of a new method of median computation. The proposed method is based on histogram and multilevel staged search. To obtain the high speed, an efficient way is used to partition the statistic histogram space into small child intervals. Experimental results show that the proposed solution outperforms the conventional median filter in terms of the filtering speed.
Revision of the Buffer Length Derivation for a Modified Ek/D/1 Systems by Maritsas and Hartley
Authors: J. S. Hill
Venue: IEEE Transactions on Computers (1979)

The problem of loss of items arriving at a queue of limited length is relevant to the design of computers with real-time inputs and of similar equipments where only a limited input buffer size is possible. The length of buffer required to reduce the overflow loss to an acceptably low level is an important design parameter.
A health-care data model based on the HL7 reference information model
Authors: Thomas J. Eggebraaten, Jeffrey W. Tenner, Joel C. Dubbels
Venue: IBM Systems Journal (2007)

The integration of medical information from various sources is gaining in importance as hospitals and medical research centers attempt to gain new insights into existing data. The Health Level Seven® (HL7®) organization has developed an abstract information model for health-care data, the HL7 Reference Information Model (RIM). We describe in this paper our approach to implementing a physical data model based on RIM. Our approach, which combines elements of entity-relationship data modeling and entity-attribute-value data modeling, involves the modeling of base RIM classes, RIM inheritance, and RIM data types. We incorporated the resulting data model into IBM Clinical Genomics, a product that integrates clinical and genomic data in a way that enables medical researchers to carry out clinical research.
Pruning LS-SVM Based Battery Model for Electric Vehicles
Authors: Xiao Lei, C. C. Chan, Kaipei Liu, Li Ma
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

This paper presents a new method to estimate the battery state of charge (SOC) in electric vehicles (EVs). The key of the proposed method is to establish the relationship of the SOC to the battery current, voltage and temperature by using least squares support vector machine (LS-SVM). For ease of practical application, the pruning procedure is developed to reduce the number of support vectors in terms of their significance. The results show that the proposed method can simulate the battery dynamics for the accurate estimation of the SOC in EVs.
FATTY: A Reliable FAT File System
Authors: Liang Alei, Liu Kejia, Li Xiaoyong, Guan Haibing
Venue: Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (2007)

Most portable digital devices use the conventional FAT file system because of its compatibility. Howevel; the FAT file system is unreliable across system crashes and power failures. This paper proposes the delayed sequential write method to improve the FATJile system's reliability. Compared to other methods such as synchronous write and logging, the DSW method is simple and effective. Simulation results show that it is also quite eficient, about 50-80% better than synchronous write.
Loop Nest Splitting for WCET-Optimization and Predictability Improvement
Authors: H. Falk, M. Schwarzer
Venue: Proceedings of the 2006 IEEE/ACM/IFIP Workshop on Embedded Systems for Real Time Multimedia (2006)

This paper presents the effect of the loop nest splitting source code optimization on worst-case execution time (WCET). Loop nest splitting minimizes the number of executed if-statements in loop nests of multimedia applications. It identifies iterations where all if-statements are satisfied and splits the loop nest such that if-statements are not executed at all for large parts of the loop nest's iteration space. Especially loops and if-statements are an inherent source of unpredictability and loss of precision for WCET analysis. This is caused by the difficulty to obtain safe and tight worst-case estimates of an application's high-level control flow. In addition, assembly-level control flow redirections reduce predictability even more due to complex processor pipelines and branch prediction units. Loop nest splitting bases on precise mathematical models combined with genetic algorithms. On the one hand, these techniques achieve a significantly more homogeneous control flow structure. On the other hand, the precision of our analyses enables to generate very accurate high-level flow facts for loops and if-statements. The application of our implemented algorithms to three real-life benchmarks leads to average speed-ups by 25.0%-30.1%, while WCET is reduced by 34.0%-36.3%
A Learning Algorithm with Boosting for Fuzzy Reasoning Model
Authors: Hiromi Miyajima, Noritaka Shigei, Shinya Fukumoto, Nobuya Nakatsu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

There have been proposed many learning algorithms for fuzzy reasoning models based on the steepest descend method. However, any learning algorithm known as a su- perior one does not always work well. This paper proposes a new learning algorithm with boosting. Boosting is a gen- eral method which attempts to boost the accuracy of any given learning algorithm. The proposed method consists of three sub-learners. The first sub-learner is constructed by performing the conventional learning algorithm with ran- domly selected data from given data space. The second sub-learner is constructed by performing the conventional learning algorithm with the data selected with equal proba- bility from correctly and incorrectly learned data in the first learning. The third sub-learner is constructed with the data for which either the first or the second sub-learner is incor- rectly learned. The output for any input data is given as de- cision by majority among the outputs of three sub-learners. That is, the method attempts to boost correctly learned data by learning incorrectly learned data repeatedly. In order to show the effectiveness of the proposed algorithm, numerical simulations are performed.
Dynamic Multi-objective Optimization Evolutionary Algorithm
Authors: Chun-an Liu, Yuping Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

A new evolutionary algorithm for Dynamic multi-objective optimization is proposed in this paper. First, the time period is divided into several random subperiods. In each subperiod, the problem is approximated by a static multi-objective optimization problem. Thus, the dynamic multi-objective optimization problem is approximately transformed into several static multiobjective problems. Second, for each static multiobjective optimization problem, the expected rank variance and the expected density variance of the population are firstly defined. By using the expected rank variance and the expected density variance of the population, the dynamic multiobjective optimization problem is transformed into a bi-objective optimization problem. Third, a new evolutionary algorithm is proposed based on a new self-check operator which can automatically check out the time variation. At last, the simulation is made and the results demonstrate the effectiveness of the proposed algorithm.
Power Consumption Forecast by Combining Neural Networks with Immune Algorithm
Authors: Yang Shuxia
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

Considering the impact of multi-factors, established a forecasting model, and then designed the structure of BP neural network while applying immune algorithm to optimize its network structure and weights. a nonlinear network model between power consumption and the affected factors was obtained through training the relative data of power consumption from 1980 to 2005 in China, and power consumption was forecasted. The result shows that the forecast by this model is more accurate.
1st Workshop on Measurement-based Cockpits for Distributed Software and Systems Engineering Projects (SOFTPIT 2007)
Authors: Marcus Ciolkowski, Jens Heidrich
Venue: Proceedings of the International Conference on Global Software Engineering (2007)

SOFTPIT 2007 is the first workshop on Measurement- based Cockpits for Distributed Software and Systems Engineering Projects. Its goal is to discuss technical and societal challenges for software cockpits in global software development projects.
A Cellular Array for Binary to BCD Conversion
Authors: J. C. Majithia
Venue: IEEE Transactions on Computers (1973)

A repeated application of nested multiplication by ten yields an extremely simple method for binary to binary-coded decimal (BCD) conversion where the input is a binary fraction. This technique can be implemented in a cellular array or in a serial system using an adder and storage register. The cellular array is, however, a faster method and can be implemented with available circuits.
Two-phase electrohydrodynamic simulations using a volume-of-fluid approach
Authors: G. Tomar, D. Gerlach, G. Biswas, N. Alleborn, A. Sharma, F. Durst, S. W. J. Welch, A. Delgado
Venue: Journal of Computational Physics (2007)

A numerical methodology to simulate two-phase electrohydrodynamic flows under the volume-of-fluid paradigm is proposed. The electric force in such systems acts only at the interface and is zero elsewhere in the two fluids. Continuum surface force representations are derived for the electric field force in a system of dielectric-dielectric and conducting-conducting fluids. On the basis of analytical calculations for simple flow problems we propose a weighted harmonic mean interpolation scheme to smoothen the electric properties in the diffused transition region (interface). It is shown that a wrong choice of interpolation scheme (weighted arithmetic mean) may lead to a transition region thickness dependent electric field in the bulk. We simulate a set of problems with exact or approximate analytical solutions to validate the numerical model proposed. A coupled level set and volume-of-fluid (CLSVOF) algorithm has been used for simulations presented here.