ppxscal/embedding_network huggingface dataset:
A Scheduling Middleware for Scheduling on a Grid Environment
Authors: Moohun Lee, Janguk In, Sunghoon Cho, Changbok Jang, Euiin Choi
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

A grid consists of high-end computational, storage, and network resources that, while known a priori, are dynamic with respect to activity and availability. Efficient scheduling of requests to use grid resources must adapt to this dynamic environment while meeting administrative policies. This paper discusses the necessary requirements of such a scheduler and proposes a framework that can administrate grid policies and schedule complex. The paper design allows for a number of functional modules to flexibly plan and schedule workflows representing multiple applications on the grids. It also allows for performance evaluation of multiple algorithms for each functional module. We present early experimental results that effectively utilizes other grid infrastructure such as workflow management systems and execution systems.
An Evolutionary Algorithm for Constrained Multi-objective Optimization Problems
Authors: Hua-Qing Min, Yu-Ren Zhou, Yan-Sheng Lu, Jia-zhi Jiang
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

Constrained multi-objective optimization problems (CMOP) are challenging and difficult to solve. In this paper, a simple and practical evolutionary algorithm for constrained multi-objective optimization problems (EACMOP) is presented, by defining constraints using non-parameter punitive functions, using Pareto strength value to represent Pareto order strength among individuals and using crowding density to ensure group diversity. It defines the evolutionary algorithm fitness functions by combining constraint treatment, comparison of Pareto strength optimization and crowding density. Test results on several benchmark functions showed that the approach is effective and robust.
Logistic mapping as the means of the description of the speech rhythm in the vicinity of the critical point
Authors: O. P. Skljarov
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 2 (2003)

The concept of the speech rhythm as a sequence of duration of the segments in the acoustic signal is introduced. These segments are obtained in accordance with a principle "is a voice-is not a voice" (principle "Voice/Unvoice"). By an evaluation of Kolmogorov entropy for the empirically obtained rhythms is shown that the rhythm of normal speech is exhibiting irregular chaotic dynamics and the rhythm of samples consisting only of violations of smoothness in stutterer's speech is exhibiting regular dynamic process with a zero entropy. Thus the rhythm of speech is exhibiting dynamics of a system near to critical point. The direct evaluation of indexes of a critical behavior shows, that for exposition of dynamics of speech rhythm it is possible to use Feigenbaum scenario of route to chaos for logistics mapping with control parameter. Hopfield discrete neural net is offered to be use as neurodynamics model of a complex system with critical behavior. The total modification of the "synaptic weights" in such net during "learning" is featured by the same Feigenbaum scenario of route to chaos for logistics mapping in which parameter of "learning" in net is used as control parameter. The requirement is fixed at which both the segments of the rhythm accepted by such model and segments of the rhythm generated by this net are identical lo total modification of the "synaptic weights" on the each step of the net's evolution during learning. The outcomes are used in practice of correction of stuttering in Saint Petersburg Research Institute of Ear, Throat, Nose and Speech.
Workshop on Evolution of Software Systems in a Business Context (ESSBC)
Authors: Keith Bennett, Damiano Distante, Maria Tortorella
Venue: Proceedings of the 13th IEEE International Workshop on Software Technology and Engineering Practice (2005)

The capability of an enterprise to innovate and promptly and effectively respond to change requests coming from both internal and external drivers has nowadays become a measure of the competitiveness of the enterprise in all business sectors. Innovation often implies modifying the enterprise business processes and, as a consequence, requires the evolution of the enterprise software systems. In this context, the workshop on Evolution of Software Systems in a Business Context (ESSBC), held in Budapest, Hungary, as part of the IEEE Software Technology and Engineering Practice workshop series (STEP 2005), aimed at discussing experiences and research outcomes and identifying research issues in supporting the evolution of enterprise software systems to reach innovation goals.
Recognition of Arabic Cursive Handwriting
Authors: Nabil Hassan
Venue: Proceedings of the conference on Geometric Modeling and Imaging: New Trends (2006)

This paper presents a process for cursive Arabic writing recognition. This problem is complicated by the wide variety of styles, personal attitudes, and multi-level writing. Therefore, we implement a hierarchical strategy for recognition. Simultaneously, this requires the definition of primitives, connectives, and laws of concatenation. Consequently, we develop a linguistic recognition system together with its grammar and parser. The presented system, a new approach to this important area of research that reveals the complexities and advantages of Arabic cursive writing, then providing adequate solutions to the encountered difficulties. The resulting pattern grammar belongs to the context-free class. We complement the process by a lexicon, whose entries are the chosen primitives. The resulting system has a corresponding non-deterministic recursive automaton implementation.
A Real-time High-fidelity Driving Simulator System Based on PC Clusters
Authors: Duoduo Liao
Venue: Proceedings of the 11th IEEE International Conference on Engineering of Complex Computer Systems (2006)

There are many challenges to create a real-time highfidelity driving simulator based on PC clusters with multi-channeled projector support, while still maintaining at least 30 frame rate for complex scenes. This paper describes how such a system was conceived and implemented to support behavioral research on highway safety and operations. The real highway safety research study is used to compare the driver behavior derived from real-world roadway data with data collected in a simulated environment to valid the simulator. The hardware and software architectures are discussed in detail, respectively. Such architectures have been constructed to support upgradeability, scalability, extendibility, and flexibility. The experimental results of the real study are shown in daytime and nighttime, respectively. The conclusions and future work are described in the end.
A Hybrid Inductive Learning-based Scheduling Knowledge Acquisition Algorithm
Authors: Weida Wang, Wenjian Liu
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

It is a crucial issue that constructing a successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. Therefore, a hybrid inductive learning-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal subset of manufacturing system attributes and determine the optimal parameters of decision tree (DT) under different scheduling objectives; DT was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal DT's parameters. The experimental results demonstrate that the proposed algori t hm produces signi f i c ant performance improvements over other machine learning-based algorithms.
Naming the Same Entities from Visual or from Auditory Stimulation Engages Similar Regions of Left Inferotemporal Cortices
Authors: Daniel Tranel, Thomas J. Grabowski, Jill Lyon, Hanna Damasio
Venue: Journal of Cognitive Neuroscience (2005)

We have proposed that the left inferotemporal (IT) region contains structures that mediate between conceptual knowledge retrieval and word-form retrieval, and we have hypothesized that these structures are utilized for word retrieval irrespective of the sensory modality through which an entity is apprehended, thus being "modality neutral." We tested this idea in two sensory modalities, visual and auditory, and for two categories of concrete entities, tools and animals. In a PET experiment, 10 normal participants named tools and animals either from pictures or from characteristic sounds (e.g., "scissors" from a picture of a scissors or from the sound of a scissors cutting; "rooster" from a picture of a rooster or from the sound of a rooster crowing). Visual and auditory naming of tools activated the left posterior/lateral IT; visual and auditory naming of animals activated the left anterior/ventral IT. For both tools and animals, the left IT activations were similar in location and magnitude regardless of whether participants were naming entities from pictures or from sounds. The results provide novel evidence to support the notion that left IT structures contain "modality-neutral" systems for mediating between conceptual knowledge and word retrieval.
An adaptive optimal-kernel time-frequency representation
Authors: D. L. Jones, R. G. Bariniuk
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

Signal-dependent time-frequency representations perform well for a much wider range of signals than any fixed-kernel distribution. The time-frequency representation presented here, based on a signal-dependent radially Gaussian kernel that adapts over time, tracks signal component variations over time and supports online implementation for signals of arbitrary length. The method uses a short-time ambiguity function for kernel optimization and as an intermediate step in computing constant-time slices of the time-frequency representation. While somewhat more expensive than fixed-kernel representation, this technique often provides much better performance.
A potential game approach to distributed power control and scheduling
Authors: T. Heikkinen
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2006)

Distributed solutions to resource allocation are motivated by the need to cope with the complexity in modern communication networks. The purpose of this paper is to discuss decentralized resource allocation in a self-organizing network from the viewpoint of potential games. The focus is on power allocation and scheduling in a congested distributed network such as a wireless ad hoc network.Noncooperative resource allocation is studied as a "potential" game, where the potential function is a common proxy objective, formalizing the implicit joint target of the noncooperative players. The potential function can be used to evaluate the system-level efficiency of noncooperative resource allocation. Examples of potential games are discussed in various contexts of distributed resource allocation. A game with discrete or convex strategy sets possessing a potential function has convergent greedy dynamics. A resource price determines the structure of a potential game.
Image Retrieval Based on Color and Texture
Authors: Xiaoying Tai, Chengyu Wu, Fuji Ren, Kenji Kita
Venue: Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (2006)

In this paper, a clustering algorithm based on mean shift is used to extract the dominant color in CIE L*a*b* color space. Earth Mover's Distance is used to calculate the color dissimilarity. In order to overcome disadvantages of color feature, a new method based on texture is proposed. The technique of relevance feedback is used to enhance the effectiveness. Finally, a prototype system is developed to compare the retrieval precision, the rank and the execution time by two experiments. The results show that the proposed approach is effective.
Fast algorithms for exponential data modeling
Authors: H. Park, S. Van Huffel, L. Elden
Venue: Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04 (1994)

A new fast algorithm is presented for quantifying signals modeled by a sum of exponentially damped sinusoids. The new algorithm is applied to the quantitative analysis of Nuclear Magnetic Resonance (NMR) data and is shown to be up to an order of magnitude more efficient than currently used linear prediction and state-space based methods.
Group Insert Tree with Projection for Handling Vehicle Information Effectively in LBS
Authors: Young Jin Jung, Keun Ho Ryu
Venue: Proceedings of the Seventh International Conference on Web-Age Information Management Workshops (2006)

The dynamic advances of wireless telecommunication and positioning technologies lead to develop the moving object management system and provide suitable services according to the vehicle locations. To provide these location based services smoothly, it is necessary for an index technique to store and search lots of moving object data rapidly. However, most of the existing bulk loading techniques of moving object indices have not good search performance, because they focus on the effective insert manner. To solve this problem, we propose a GIP(Group Insert tree with Projection) in this paper. The proposed GIP reduces the input cost effectively since the manner stores the moving object location data in a group. Besides, it also reduces the search cost with Projection storage. Then we confirm the effect of the GIP which reduces the insert cost and improves the search performance in a time slice query from the experiment to compare the operation with some existing indices. The proposed GIP would be useful in the environments that require frequent insert as well as search of location information such as a transportation vehicle management and a tour-guide system.
From Commodity to Value: A Privacy-Preserving e-Business Architecture
Authors: Yitao Duan, John Canny
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2006)

Privacy has been recognized as a very important issue in electronic commerce. However, many privacy techniques were not adopted and many online anonymity services failed. In this paper we propose treating privacy as a "value" that is to be added to other services to avoid the adoption pitfall. We present an architecture that anonymizes online transactions and makes them unlinkable to any customer or to each other. Our scheme guarantees fair payment to the vendor and provides efficient fraud detection and tracing mechanisms. With minimal change to existing delivery services, it can also protect transactions that require delivery of physical items. Moreover, our system could give the vendor accurate aggregate information about customers' usage pattern, which can be very valuable for its business operation. Our scheme utilizes existing financial infrastructure thus has very low adoption cost.
Adaptive Blocking: Learning to Scale Up Record Linkage
Authors: Mikhail Bilenko, Beena Kamath, Raymond J. Mooney
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

Many data mining tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as well as in clustering and schema mapping algorithms. Because the number of object pairs grows quadratically with the size of the dataset, computing similarity between all pairs is impractical and becomes prohibitive for large datasets and complex similarity functions. Blocking methods alleviate this problem by efficiently selecting approximately similar object pairs for subsequent distance computations, leaving out the remaining pairs as dissimilar. Previously proposed blocking methods require manually constructing an index-based similarity function or selecting a set of predicates, followed by hand-tuning of parameters. In this paper, we introduce an adaptive framework for automatically learning blocking functions that are efficient and accurate. We describe two predicate-based formulations of learnable blocking functions and provide learning algorithms for training them. The effectiveness of the proposed techniques is demonstrated on real and simulated datasets, on which they prove to be more accurate than non-adaptive blocking methods.
From implementation to theory in product synthesis
Authors: Don Batory
Venue: Proceedings of the 34th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages (2007)

Future software development will rely on product synthesis, i.e., the synthesis of code and non-code artifacts for a target component or application. Prior work on feature-based product synthesis can be understood and generalized using elementary ideas from category theory. Doing so reveals (a) practical and previously unrecognized properties that product synthesis tools must satisfy, and (b) non-obvious generalizations of current techniques that will guide future research efforts in automated product development.
Optimal Power Scheduling for Data Fusion in Inhomogeneous Wireless Sensor Networks
Authors: Thakshila Wimalajeewa, Sudharman K. Jayaweera
Venue: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (2006)

We consider the problem of optimal power scheduling for the decentralized detection of a deterministic signal in an inhomogeneous wireless sensor network. The observation noise at local sensors is assumed to be independently and identically distributed (i.i.d.) and at the local sensors each node performs analog-relay amplifier processing for its observation independently. The communication between the local sensors and the fusion center is assumed to be through an inhomogeneous channel. The optimal power scheduling scheme suggests that the sensors with poor observation quality and channels should be inactive in order to save total power expenditure of the system. For the remaining active sensors the optimal transmit power is determined jointly by the individual channel gains, total number of active sensors, local observation signal to noise ratio (SNR) and the required probability of error at the fusion center. We show that the optimal scheme can provide significant system power savings compared to the uniform power allocation scheme when the number of sensors in the system is large.
Block-aware instruction set architecture
Authors: Ahmad Zmily, Christos Kozyrakis
Venue: ACM Transactions on Architecture and Code Optimization (TACO) (2006)

Instruction delivery is a critical component for wide-issue, high-frequency processors since its bandwidth and accuracy place an upper limit on performance. The processor front-end accuracy and bandwidth are limited by instruction-cache misses, multicycle instruction-cache accesses, and target or direction mispredictions for control-flow operations. This paper presents a block-aware instruction set (BLISS) that allows software to assist with front-end challenges. BLISS defines basic block descriptors that are stored separately from the actual instructions in a program. We show that BLISS allows for a decoupled front-end that tolerates instruction-cache latency, facilitates instruction prefetching, and leads to higher prediction accuracy.
Rotary electrostatic micromirror switches using wafer-scale processing and assembly
Authors: Ching-Chen Tu, Kuohao Fanchiang, Cheng-Hsien Liu
Venue: Microsystem Technologies (2006)

The design, micromachining process, wafer-scale assembly, and experimental characterization of our rotary electrostatic micromirrors for optical switching applications are reported in this paper. Some of the proposed microelectromechanical systems (MEMS) optical switches utilize surface-micromachined thin films as the reflection micromirrors which might result in optical degradation due to dynamic warping. Some of these devices fabricated by bulk micromachining highly rely on a delicate post-assembly process to assemble the micromirrors onto the top of MEMS actuators. In this research, we focus on developing rotary electrostatic micromirror switches without the requirement of delicate post-assembly processes. We use the spin-on-glass, which is used as an intermediate layer in a 280&#x00b0;C low-temperature wafer-bonding process, to fabricate our rotary optical switches with a wafer-scale assembly process. The rotary electrostatic actuator and the vertical micromirror are fabricated simultaneously by the same deep RIE process in our design. We have successfully demonstrated the rotary electrostatic micromirror switches via the wafer-scale assembly process with a yield rate of above 70%. Experimental results show that our rotary vertical micromirrors rotate about 1.5&#x00b0; under an applied voltage of 150&#x00a0;V for steady-state measurements. For dynamic measurements, the rotary vertical micromirrors rotate about 3.6&#x00b0; under an applied voltage of 30&#x00a0;V at the resonant frequency. The first vibration mode of our rotary switch is an in-plane rotational mode and appears around 3.4&#x00a0;kHz, which is characterized via a Polytec laser doppler vibrometer.
Adaptive Transmit Diversity with Quadrant Phase Constraining Feedback
Authors: Jingxian Wu, Henry Horng, Jinyun Zhang, Chengshan Xiao
Venue: Wireless Personal Communications: An International Journal (2006)

An adaptive transmit diversity scheme with quadrant phase constraining feedback is proposed in this paper. With simple linear operations at both transmitter and receiver, the proposed algorithm can achieve better system performances with only 2M¿2 bits of feedback information for systems with M transmit antennas. Theoretical performance bounds of the proposed transmit diversity scheme are derived. Simulation examples and theoretical analyses show that the proposed transmit diversity scheme outperforms not only the conventional open-loop transmit diversity techniques, but also some closed-loop transmit diversity techniques with more information transmitted in the feedback channel.
Grammatical Distinctions in the Left Frontal Cortex
Authors: Kevin A. Shapiro, Alvaro Pascual-leone, Felix M. Mottaghy, Massimo Gangitano, Alfonso Caramazza
Venue: Journal of Cognitive Neuroscience (2001)

Selective deficits in producing verbs relative to nouns in speech are well documented in neuropsychology and have been associated with left hemisphere frontal cortical lesions resulting from stroke and other neurological disorders. The basis for these impairments is unresolved: Do they arise because of differences in the way grammatical categories of words are organized in the brain, or because of differences in the neural representation of actions and objects? We used repetitive transcranial magnetic stimulation (rTMS) to suppress the excitability of a portion of left prefrontal cortex and to assess its role in producing nouns and verbs. In one experiment subjects generated real words; in a second, they produced pseudowords as nouns or verbs. In both experiments, response latencies increased for verbs but were unaffected for nouns following rTMS. These results demonstrate that grammatical categories have a neuroanatomical basis and that the left prefrontal cortex is selectively engaged in processing verbs as grammatical objects.