ppxscal/embedding_network huggingface dataset:
HAL-based cascaded model for variable-length semantic pattern induction from psychiatry web resources
Authors: Liang-Chih Yu, Chung-Hsien Wu, Fong-Lin Jang
Venue: Proceedings of the COLING/ACL on Main conference poster sessions (2006)

Negative life events play an important role in triggering depressive episodes. Developing psychiatric services that can automatically identify such events is beneficial for mental health care and prevention. Before these services can be provided, some meaningful semantic patterns, such as <lost, parents>, have to be extracted. In this work, we present a text mining framework capable of inducing variable-length semantic patterns from unannotated psychiatry web resources. This framework integrates a cognitive motivated model, Hyperspace Analog to Language (HAL), to represent words as well as combinations of words. Then, a cascaded induction process (CIP) bootstraps with a small set of seed patterns and incorporates relevance feedback to iteratively induce more relevant patterns. The experimental results show that by combining the HAL model and relevance feedback, the CIP can induce semantic patterns from the unannotated web corpora so as to reduce the reliance on annotated corpora.
Approximate fixed point sequence for a finite family of asymptotically pseudocontractive mappings
Authors: Feng Gu
Venue: Computers Mathematics with Applications (2007)

Let E be a real Banach space and K be a nonempty, closed, convex, and bounded subset of E. Let T"i:K->K, i=1,2,...,N, be N uniformly L-Lipschitzian, uniformly asymptotically regular with sequences {@e"n}, and asymptotically pseudocontractive mappings with sequences {k"n^(^i^)}, where {@e"n} and {k"n^(^i^)}, i=1,2,...,N, satisfy certain mild conditions. Let a sequence {x"n} be generated from x"1@?K by x"n"+"1@?@l"n@q"nx"1+[1-@l"n(1+@q"n(1+@m"n))]x"n+@l"nT"n^nx"n+@l"n@q"n@m"nu"n, for all integers n>=1, where T"n=T"n"("m"o"d"N"), {u"n} be a sequence in K, and {@l"n}, {@q"n} and {@m"n} are three real sequences in [0,1] satisfying appropriate conditions; then @?x"n-T"lx"n@?->0 as n->~ for each l@?{1,2,...,N}.
Generalized joint signal representations and optimum detection
Authors: A. M. Sayeed
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03 (1996)

Generalized joint signal representations (JSRs) extend the scope of joint time-frequency representations (TFRs) to a richer class of nonstationary signals, but their use, just as in the case of TFRs, has been primarily limited to qualitative, exploratory data analysis. To exploit their potential more fully, JSR-based statistical signal processing techniques need to be developed that can be successfully applied in real-world problems. We present an optimal detection framework based on arbitrary generalized quadratic JSRs, thereby making it applicable in a wide variety of detection scenarios involving nonstationary stochastic signals, noise and interference. For any given class of generalized JSRs, we characterize the corresponding class of detection scenarios for which such JSRs constitute canonical detectors, and derive the corresponding JSR-based detectors. Our formulation also yields a very useful subspace-based interpretation in terms of corresponding linear JSRs that we exploit to design optimal detectors based on only partial signal information.
A Perspective on the Future of Middleware-based Software Engineering
Authors: Valerie Issarny, Mauro Caporuscio, Nikolaos Georgantas
Venue: 2007 Future of Software Engineering (2007)

Middleware is a software layer that stands between the networked operating system and the application and provides well known reusable solutions to frequently encountered problems like heterogeneity, interoperability, security, dependability. Further, with networks becoming increasingly pervasive, middleware appears as a major building block for the development of future software systems. Starting with the impact of pervasive networking on computing models, manifested by now common grid and ubiquitous computing, this paper surveys related challenges for the middleware and related impact on the software development. Indeed, future applications will need to cope with advanced non-functional properties such as contextawareness and mobility, for which adequate middleware support must be devised together with accompanying software development notations, methods and tools. This leads us to introduce our view on next generation middleware, considering both technological advances in the networking area but also the need for closer integration with software engineering best practices, to ultimately suggest middleware-based software processes.
Knowledge acquisition for adaptive game AI
Authors: Marc Ponsen, Pieter Spronck, Héctor Muñoz-Avila, David W. Aha
Venue: Science of Computer Programming (2007)

Game artificial intelligence (AI) controls the decision-making process of computer-controlled opponents in computer games. Adaptive game AI (i.e., game AI that can automatically adapt the behaviour of the computer players to changes in the environment) can increase the entertainment value of computer games. Successful adaptive game AI is invariably based on the game's domain knowledge. We show that an offline evolutionary algorithm can learn important domain knowledge in the form of game tactics (i.e., a sequence of game actions) for dynamic scripting, an offline algorithm inspired by reinforcement learning approaches that we use to create adaptive game AI. We compare the performance of dynamic scripting under three conditions for defeating non-adaptive opponents in a real-time strategy game. In the first condition, we manually encode its tactics. In the second condition, we manually translate the tactics learned by the evolutionary algorithm, and use them for dynamic scripting. In the third condition, this translation is automated. We found that dynamic scripting performs best under the third condition, and both of the latter conditions outperform manual tactic encoding. We discuss the implications of these results, and the performance of dynamic scripting for adaptive game AI from the perspective of machine learning research and commercial game development.
Welcome Message
Venue: Proceedings of the 2004 IEEE International Workshop on Defect Based Testing (2004)

The following topics are dealt with: IDDX methods; IDDX circuits; IDDQ development; mixed signal LSI relationship; delay analysis; speed binning, fault diagnosis; and bridging defects; memory diagnosis and test data compression. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
An ILP formulation for system-level application mapping on network processor architectures
Authors: Chris Ostler, Karam S. Chatha
Venue: Proceedings of the conference on Design, automation and test in Europe (2007)

Current day network processors incorporate several architectural features including symmetric multi-processing (SMP), block multi-threading, and multiple memory elements to support the high performance requirements of networking applications. We present an automated system-level design technique for application development on such architectures. The technique incorporates process transformations and block multi-threading aware data mapping to maximize the worst case throughput of the application. We propose integer linear programming formulations for process allocation and data mapping on SMP and block multi-threading based network processors. The paper presents experimental results that evaluate the technique by implementing representative network processing applications on the Intel IXP 2400 architecture. The results demonstrate that our technique is able to generate high-quality mappings of realistic applications on the target architecture within a short time.
The adoption and diffusion of computing and internet technologies in historically black colleges and universities
Authors: Raj Singh, Lilburn Hoehn
Venue:  (2007)

The rapid growth of technologies is providing the opportunity for innovative design and delivery of curriculum and instructional materials to college students; yet many college faculty members, especially in historically Black Colleges and Universities (HBCU), are not using this technology. With a widening gap in access to technologies for minorities, this is a significant issue. In this study, specific research questions focused on HBCU faculty's current use of technology, desire to incorporate new technology, view of organizational support, and potential gender or age differences. Surveys were conducted at five HBCU with established records of leadership in education. Using quantitative statistics, the results showed there is a strong willingness of HBCU faculty to incorporate new technology. Faculty members, however, were handicapped by the lack of organizational support, access to computing resources, and availability of technology support staff. The result did not show gender or age differences in the adoption of technology in the classroom. It is recommended that HBCU consider upgrading aging computing and network infrastructures. Sustainable technology-related training and professional development workshops should also be made available to the faculty members. These initiatives could help faculty in HBCU move to increased use of technology to deliver instructions, encourage the use of technology by minorities, and foster the growth of online instruction in this important segment of our society.
Two-dimensional FIR signal adapted filter banks: Optimality and design
Authors: V. S. Sheeba, Elias Elizabeth
Venue: Signal Processing (2007)

The effect of different subsampling matrices with the same sampling density, on the optimality of two-dimensional filter banks is studied in this paper. It is seen that, in the case of two channel filter banks, there can be three distinct filter banks for an input power spectral density, with each one optimum for the corresponding subsampling matrix. For most of the natural images, the quincunx filter bank is optimum. In addition to this, a suboptimal solution that extends one-dimensional (1D) signal adapted filter bank design procedure, to two-dimensional (2D) nonseparable and separable signal adapted filter banks, using iterative greedy algorithm, is proposed. The nonseparable filter bank is designed for both four channel and two channel quincunx filter banks. A cascade structure is used for the factorization of 2D paraunitary polyphase matrix in the nonseparable case and a 2D separable FIR PU polyphase matrix is used in the separable case. The simulated FIR responses match the ideal responses very closely.
An admission control scheme to provide end-to-end statistical QoS provision in IP networks
Authors: Bin Pang
Venue: Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International (2002)

This paper presents a distributed and scalable admission control scheme to provide end-to-end statistical QoS guarantees in Differentiated Services (DiffServ) networks. The basic idea of the scheme is that the ingress routers make admission decisions according to the network status information obtained by sending probing packets from the ingress to the egress of the network. Each router passively monitors the arriving traffic and marks the probing packets with its network status. The performance of our scheme is evaluated with a variety of traffic models, QoS metrics and network topologies. The simulation results show that the proposed scheme can accurately control the admissible region and effectively improve the utilization of network resource.
Teaching and Learning Applications Related to the Automated Interpretation of ERDs
Authors: Kevin Waugh, Pete Thomas, Neil Smith
Venue: Proceedings of the 24th British National Conference on Databases (2007)

In this paper we report ongoing work in the development of diagram interpretation and assessment applied to entity-relationship diagrams (ERDs). We report on our development of an automated marking tool and the results of two large-scale experiments with this tool. We give an overview and the results of some initial, informal, evaluation of a student revision tool that we developed as a result of our experiences working on the automated marking system.
QoS-Driven Service Composition Modeling with Extended Hierarchical CPN
Authors: Wen-jun LI, Xiao-jun LIANG, Hua-mei SONG, Xiao-cong ZHOU
Venue: Proceedings of the First Joint IEEE/IFIP Symposium on Theoretical Aspects of Software Engineering (2007)

Quality of Service (QoS) plays an important role in the composition of services. A Colored Petri Net (CPN) based approach is proposed to model and analyze QoS-driven service composition in a Service-Oriented Architecture (SOA), where all services are considered as dynamic joining and quitting resources. Modeling with an extension of the hierarchical CPN, which is named QSC-net, the QoS-driven features are expressed in the service composition model explicitly, along with the dynamic change of service resources and the uncertain execution of composition processes at runtime. QSC-nets support not only the classical property analysis provided by CPN tools, but also new properties concerning the maximal concurrency of services in the composition model. Compared with existing Petri Net based modeling approaches, QSCnets are specially suitable for the modeling and analysis of service composition in a dynamic environment, such as a Virtual Organization (VO) defined by Grid computing.
New approaches to weighted frequent pattern mining
Authors: John J. Leggett
Venue:  (2005)

Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use of tree/other data structures, top down/bottom up traversal and vertical/horizontal formats for frequent pattern mining have been developed. Most frequent pattern mining algorithms use a support measure to prune the combinatorial search space. However, support-based pruning is not enough when taking into consideration the characteristics of real datasets. Additionally, after mining datasets to obtain the frequent patterns, there is no way to adjust the number of frequent patterns through user feedback, except for changing the minimum support. Alternative measures for mining frequent patterns have been suggested to address these issues. One of the main limitations of the traditional approach for mining frequent patterns is that all items are treated uniformly when, in reality, items have different importance. For this reason, weighted frequent pattern mining algorithms have been suggested that give different weights to items according to their significance. The main focus in weighted frequent pattern mining concerns satisfying the downward closure property. In this research, frequent pattern mining approaches with weight constraints are suggested. Our main approach is to push weight constraints into the pattern growth algorithm while maintaining the downward closure property. We develop WFIM (Weighted Frequent Itemset Mining with a weight range and a minimum weight), WLPMiner (Weighted frequent Pattern Mining with length decreasing constraints), WIP (Weighted Interesting Pattern mining with a strong weight and/or support affinity), WSpan (Weighted Sequential pattern mining with a weight range and a minimum weight) and WIS (Weighted Interesting Sequential pattern mining with a similar level of support and/or weight affinity) The extensive performance analysis shows that suggested approaches are efficient and scalable in weighted frequent pattern mining.
Image restoration by 1-D Kalman filtering on oriented image decompositions
Authors: M. Mattavelli
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04 (1996)

This paper introduces a new image restoration method based on a 1-D Kalman filtering. Using the model of tuned channels, the corrupted image is decomposed into a set of perceptual components characterized by different orientations and frequencies. The restoration step is then performed on each component in one dimension following the appropriate orientation with the well-known Kalman algorithm. Since the decomposition provides perfect reconstruction, the restored image is the recomposition of all the restored components. This approach yields relevant results for 2-D blurred images, using 1-D low order models. Unlike traditional 2-D Kalman restoration techniques, its implementation has no excessive computational load.
MIMO Based Radio Resource Management for UMTS Multicast Broadcast Multimedia Services
Authors: Armando Soares, João C. Silva, Nuno Souto, Filipe Leitão, Américo Correia
Venue: Wireless Personal Communications: An International Journal (2007)

MIMO systems promise high performance gains over conventional single antenna systems. To accomplish high data rates over wireless links the use of multiple transmit and receive antennas is an alternative that does not require any extra bandwidth. It has the potential to address the unprecedented demand for wireless services, particularly for the Multimedia Broadcast Multicast Service (MBMS). This service supports downlink streaming and download-and-play type services to large groups of users. From the radio perspective, MBMS includes point-to-point (PtP) and point-to-multipoint (PtM) modes. New proposed MBMS related enhancements in the physical layer specifications and their effects on resource requirements are presented in this paper, such as the use of High Speed Downlink Packet Access (HSDPA) to broadcast/multicast video streaming using the Deficit Round Robin (DRR) scheduler algorithm as a way to maintain an acceptable QoS for all the users. Other improvements, based on the use of MIMO including macro-diversity, are independent of the mode used and are dependent on deployment scenarios, but can yield substantial reduction in resource demand.
Cognition, Emotion, Satisfaction, and Post-Adoption Behaviors of E-Service Customers
Authors: Sophea Chea, Margaret Meiling Luo
Venue: Proceedings of the 40th Annual Hawaii International Conference on System Sciences (2007)

In today's turbulent E-commerce environment, online companies need to have a long-term client relationship strategy to keep customer satisfied. It is believed that satisfied customers not only continue to use the product or service but also help to recruit more customers through word of mouth. Similarly unsatisfied customers discontinue the product or service usage and discourage others from using the product or service. Therefore, understanding the determinants of customer satisfaction and post-adoption behaviors are important issues. The present study proposes and tests a model that integrates cognition, emotion, satisfaction, and three post purchase behaviors. The results show that negative affective experience mediates the effect of confirmation and directly predicts complaint intention in addition to satisfaction. Furthermore, perceived usefulness and confirmation predict the level of satisfaction with e-service and the latter in turn predicts continuance intention, recommendation, and complaint.
Diagnosing network disruptions with network-wide analysis
Authors: Yiyi Huang, Nick Feamster, Anukool Lakhina, Jim (Jun) Xu
Venue: Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems (2007)

To maintain high availability in the face of changing network conditions, network operators must quickly detect, identify, and react to events that cause network disruptions. One way to accomplish this goal is to monitor routing dynamics, by analyzing routing update streams collected from routers. Existing monitoring approaches typically treat streams of routing updates from different routers as independent signals, and report only the "loud" events (i.e., events that involve large volume of routing messages). In this paper, we examine BGP routing data from all routers in the Abilene backbone for six months and correlate them with a catalog of all known disruptions to its nodes and links. We find that many important events are not loud enough to be detected from a single stream. Instead, they become detectable only when multiple BGP update streams are simultaneously examined. This is because routing updates exhibit network-wide dependencies. This paper proposes using network-wide analysis of routing information to diagnose (i.e., detect and identify) network disruptions. To detect network disruptions, we apply a multivariate analysis technique on dynamic routing information, (i.e., update traffic from all the Abilene routers) and find that this technique can detect every reported disruption to nodes and links within the network with a low rate of false alarms. To identify the type of disruption, we jointly analyze both the network-wide static configuration and details in the dynamic routing updates; we find that our method can correctly explain the scenario that caused the disruption. Although much work remains to make network-wide analysis of routing data operationally practical, our results illustrate the importance and potential of such an approach.
A robust speech detection algorithm for speech activated hands-free applications
Authors: Duanpei Wu
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 04 (1999)

This paper describes a novel noise robust speech detection algorithm that can operate reliably in severe noisy car conditions. High performance has been obtained with the following techniques: (1) noise suppression based on principal component analysis for pre-processing, (2) robust endpoint detection using dynamic parameters, and (3) speech verification using the periodicity of voiced signals with harmonic enhancement. Noise suppression improves the SNR as compared with nonlinear spectrum subtraction by about 20 dB. This makes the endpoint detection operate reliably in SNRs down to -10 dB. In car environments, road bump noises are problematic for speech detectors causing mis-detection errors. Speech verification helps to remove these errors. This technology is being used in Sony car navigation products.
Enhancement of Chinese speech based on nonlinear dynamics
Authors: Junfeng Sun, Nengheng Zheng, Xinlong Wang
Venue: Signal Processing (2007)

Based on recently observed nonlinear dynamic features of human speech, the local projection (LP) method, originally developed for noisy chaotic time series, is generalized and adapted to the enhancement of Chinese speech. The analysis of minimum embedding dimensions estimated by the false nearest neighbor algorithm shows that all the basic phonemes and syllables in Chinese can be faithfully embedded in some low-dimensional phase space. Over-embedding is applied to reconstruct the dynamics of continuous speech in some extended phase space of higher dimension, thus solving the problem of nonstationarity in continuous speech. A generalization of the LP method, named the local subspace method, is presented for speech enhancement in the phase space. It is demonstrated that, the local subspace method is essentially an extension of the well-known linear subspace technique in the local phase space, and the LP method is the least square case of this generalization. Noise reduction is then carried out in the local phase space. Results show that the LP method, with 2 or 3 iterations, achieves better performances than the local subspace method. For both isolated and continuous speech with additive white noise, experiments show the superiority of the LP method over two other popular algorithms.