ppxscal/embedding_network huggingface dataset:
An Integrated Framework for Visualized and Exploratory Pattern Discovery in Mixed Data
Authors: Chung-Chian Hsu, Sheng-Hsuan Wang
Venue: IEEE Transactions on Knowledge and Data Engineering (2006)

Data mining uncovers hidden, previously unknown, and potentially useful information from large amounts of data. Compared to the traditional statistical and machine learning data analysis techniques, data mining emphasizes providing a convenient and complete environment for the data analysis. In this paper, we propose an integrated framework for visualized, exploratory data clustering, and pattern extraction from mixed data. We further discuss its implementation techniques: a generalized self-organizing map (GSOM) and an extended attribute-oriented induction (EAOI), which not only overcome the drawbacks of their original algorithms, but also provide additional analysis capabilities. Specifically, the GSOM facilitates the direct handling of mixed data, including categorical and numeric values. The EAOI enables exploration for major values hidden in the data and, in addition, offers an alternative for processing numeric attributes, instead of generalizing them. A prototype was developed for experiments with synthetic and real data sets, and comparison with those of the traditional approaches. The results confirmed the feasibility of the framework and the superiority of the extended techniques.
Matching 2.5D Face Scans to 3D Models
Authors: Xiaoguang Lu, Anil K. Jain, Dirk Colbry
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.
A simulation model for equipment selection in a steel plant
Authors: Pedro A. Nassau
Venue: ACM SIGSIM Simulation Digest (1979)

A simulation model was developed to determine the optimal policy for utilization and replacement of moving equipment in a steel plant. Specifically, this paper deals with the problem of slag removal in a melt-shop composed of a batch of five electric-arc furnaces. The environment of high temperatures in which service has to take place leads to conditions of frequent equipment break-down and a dynamic rescheduling of activities, which makes the use of simulation a necessity. One important feature that was included in the model was the process of "aging", that is, that service time is a function of the number of minutes that the slag has been waiting for its removal.
A component model for internet-scale applications
Authors: Steven P. Reiss
Venue: Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering (2005)

This paper describes a component model where the overall semantics of a component is included in the interface definition. Such a model is necessary for future computing where programs will run at Internet-scales and will employ a combination of web services, grid technologies, peer-to-peer sharing, autonomic capabilities, and open source implementations. The component model is based on packages and supports static and dynamic objects, interfaces, structures, and exceptions. The interface definitions provide a practical approach to defining functional semantics and include appropriate extensions to provide semantics for security, privacy, recovery, and costs. The component model has been implemented in a prototype framework and demonstrated in an Internet-scale example.
Defensive Execution of Transactional Processes against Attacks
Authors: Meng Yu, Wanyu Zang, Peng Liu
Venue: Proceedings of the 21st Annual Computer Security Applications Conference (2005)

It is a well known problem that the attack recovery of a self-healing system rolls back not only malicious transactions, but also legitimate transactions that are dependent on the malicious transactions. Rolling back and re-executing damaged transactions increase the response time of the system and may cause a significant processing delay. In such situations, the availability of the system is compromised and the system suffers the vulnerability of Denial of Service (DoS). In this paper, we propose a defensive executing technique and analyze its effectiveness. Our technique concurrently executes multiple paths of a transactional processes based on the prediction generated by a Discrete Time Markov Chain. The defensive execution can reduce the delay caused by recovery. We also propose a branch cutting technique to reduce the extra cost introduced by defensive execution. Our analytical results show that our technique is practical against transactional level attacks.
Target Tracking based on the Dynamic Cluster Method in the Acoustic Sensor Network
Authors: Chuanzhi Zang, Yu Haibin, Liang Wei, Jieyin Bai, Guangwei Li
Venue: Proceedings of the The Fifth International Conference on Computer and Information Technology (2005)

When finding the moving target in the acoustic sensor network, a dynamic cluster is formed to triangulate the target's position. While the target is moving, the old cluster is revoked and a new cluster is formed to follow the target. An election strategy based on the probability is used to select the cluster head and thus improve the cluster formation probabilities. Given the random function distribution, the paper also presents how to design the random function. The time window which is associated with the data association process is a very important parameter. The paper also presents the domain of the time window. The simulation shows that choosing right random function and right value of the time window is very important to the tracking precision.
A Case for Protocol Dependency
Authors: Juhani Eronen, Marko Laakso
Venue: Proceedings of the First IEEE International Workshop on Critical Infrastructure Protection (2005)

Vulnerabilities infest information technology. There is a lack of tools in risk assessment for understanding the impact that the disclosed vulnerabilities have on the critical information infrastructures. To address this need, this work derives a new dimension of dependency from practical vulnerability work, namely that of protocol dependency. Classic technology dependency views were reviewed, a chain of systematic vulnerability disclosures was followed as a case study and analysis revealed evidence of protocol dependency. Extrapolating from the experiences of a complex case, this new dependency dimension can be modelled. The model will benefit from going beyond a narrow technical view.
Manipulation Skills Acquisition through State Classification and Dimension Decrease
Authors: Shen Dong, Fazel Naghdy
Venue: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)

The work carried out to explore the feasibility of reconstructing human constrained motion manipulation skills is reported. This is achieved by tracing and learning the manipulation performed by a human operator in a haptic rendered virtual environment. The peg-in-hole insertion problem is used as a case study. In the developed system, force and position variables generated in the haptic rendered virtual environment combined with a priori knowledge about the task are used to identify and learn the skills in the newly demonstrated task. The data obtained from the virtual environment is classified into different cluster sets using Fuzzy Gustafson-Kessel Model (FGK). Principal Component Analysis (PCA) is applied to each cluster to reduce the dimension of the data. The clusters in the optimum cluster set are tuned using Locally Weighted Regression (LWR) to produce prediction models for robot trajectory performing the physical assembly based on the force/position information received from the rig.
Electrostatic Tactile Display with Thin Film Slider and Its Application to Tactile Telepresentation Systems
Authors: Akio Yamamoto, Shuichi Nagasawa Hiroaki Yamamoto Toshiro Higuchi
Venue: IEEE Transactions on Visualization and Computer Graphics (2006)

A new electrostatic tactile display is proposed to realize compact tactile display devices that can be incorporated with virtual reality systems. The tactile display of this study consists of a thin conductive film slider with stator electrodes that excite electrostatic forces. Users of the device experience tactile texture sensations by moving the slider with their fingers. The display operates by applying two-phase cyclic voltage patterns to the electrodes. The display is incorporated into a tactile telepresentation system to realize explorations of remote surface textures with real-time tactile feedback. In the system, a PVDF tactile sensor and a DSP controller automatically generate voltage patterns to present surface texture sensations through the tactile display. A sensor, in synchronization with finger motion on the tactile display, scans a texture sample and outputs information about the sample surface. The information is processed by a DSP and fed back to the tactile display in real time. The tactile telepresentation system was evaluated in texture discrimination tests and demonstrated a 79 percent correct answer ratio. A transparent electrostatic tactile display is also reported in which the tactile display is combined with an LCD to realize a visual-tactile integrated display system.
The network of life
Authors: Meg McGinity Shannon
Venue: Communications of the ACM (2006)

Service providers must work together to ensure a fail-safe communication system.
Design and Implementation of Commerce Data Mining System Based on Rough Set Theory
Authors: Xiang Yang, Wu Weiying, Mao Hairong, Song Qingwei
Venue: Proceedings of the IEEE International Conference on e-Business Engineering (2005)

On the basis of analyzing characters of commercial data and Rough Set, this paper proposes a data mining system using Rough Set, and presents detailed design and implementation. The novel commercial data mining system presented in this paper include three parts: data preparation, data preprocessing, mining and evaluation. During the design and implementation of three levels, this paper presents several novel algorithms using rough set. These algorithms can accelerate data mining process and improve quality of data mining. Finally, the paper tests the result of research and algorithm through an instance.
Portable checkpointing and communication for BSP applications on dynamic heterogeneous Grid environments
Authors: Raphael Y. De Camargo, Fabio Kon, Alfredo Goldman
Venue: Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing (2005)

Executing long-running parallel applications in Opportunistic Grid environments composed of heterogeneous, shared user workstations, is a daunting task. Machines may fail, become unaccessible, or may switch from idle to busy unexpectedly, compromising the execution of applications. A mechanism for fault-tolerance that supports these heterogeneous architectures is an important requirement for such a system. In this paper, we describe the support for fault-tolerant execution of BSP parallel applications on heterogeneous, shared workstations. A precompiler instruments application source code to save state periodically into checkpoint files. In case of failure, it is possible to recover the stored state from these files. Generated checkpoints are portable and can be recovered in a machine of different architecture, with data representation conversions being performed at recovery time. The precompiler also modifies BSP parallel applications to allow execution on a Grid composed of machines with different architectures. We implemented a monitoring and recovering infrastructure in the InteGrade Grid middleware. Experimental results evaluate the overhead incurred and the viability of using this approach in a Grid environment.
SensEye: a multi-tier camera sensor network
Authors: Purushottam Kulkarni, Deepak Ganesan, Prashant Shenoy, Qifeng Lu
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

This paper argues that a camera sensor network containing heterogeneous elements provides numerous benefits over traditional homogeneous sensor networks. We present the design and implementation of senseye---a multi-tier network of heterogeneous wireless nodes and cameras. To demonstrate its benefits, we implement a surveillance application using senseye comprising three tasks: object detection, recognition and tracking. We propose novel mechanisms for low-power low-latency detection, low-latency wakeups, efficient recognition and tracking. Our techniques show that a multi-tier sensor network can reconcile the traditionally conflicting systems goals of latency and energy-efficiency. An experimental evaluation of our prototype shows that, when compared to a single-tier prototype, our multi-tier senseye can achieve an order of magnitude reduction in energy usage while providing comparable surveillance accuracy.
DRMs, fair use and users' experience of sharing music
Authors: Margaret Jackson, Supriya Singh, Jenine Beekhuyzen, Jenny Waycott
Venue: Proceedings of the 5th ACM workshop on Digital rights management (2005)

There is a mismatch between the law relating to fair use, personal use and copying; the central thrust of Digital Rights Management (DRM) and users' behavior relating to the listening and sharing of music. This paper reports on the different copyright regimes in the United States and Australia. It describes some of the current DRM systems. Against this background, the paper draws on a qualitative study to explore Australian users' experience of listening to and sharing music. A design for a good DRM has to take into account the schism between the copyright regimes and users' sharing behavior.
Reading SCORM compliant multimedia courses using heterogeneous pervasive devices
Authors: Te-Hua Wang, Hsuan-Pu Chang, Yun-Long Sie, Kun-Han Chan, Mon-Tin Tzou, Timothy K. Shih
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

The Sharable Content Object Reference Model (SCORM) provides some important representation for distance learning content and the learning behavior. In general, SCORM-Compliant learning content can be viewed via the Web browsers. In this paper, we built an environment which allows people to read SCORM-Compliant course materials on hardcopy papers with a pen-like OCR device. A computer, a PDA, or a cellular phone can be used in conjunction with the pen device for multimedia presentations. Our project is called the Hard SCORM. Consequently, users can read textbooks in a traditional manner while behavior of reading is incorporated with the SCORM specification.
On the Theory of Patching
Authors: Zoltan Pap, Gyula Csopaki, Sarolta Dibuz
Venue: Proceedings of the Third IEEE International Conference on Software Engineering and Formal Methods (2005)

We study the problem of patching, i.e., modifying the behavior of an existing system. We consider systems modelled as finite state machines (FSMs), and define edit operators for them based on a traditional fault model. We argue that sequences of edit operations can be considered as models of patches defining modifications to an FSM system. We utilize recent results in graph matching theory as mathematical foundations. We introduce a new type of problem which we call the optimal patch or optimal update problem: Given an FSMM modeling the behavior of an existing system and an other machine M' modeling a new design, find an optimal patch, i.e., the edit operations changing M to M' that are minimal according to a given cost function associated with the edit operations. We analyze the complexity of the problem, and conclude that it is unlikely to have a polynomial time solution for it. We also show that the problem can be easily transformed to a state-space search problem, for which many heuristic approximation algorithms have been developed.
A hybrid heuristic model for classification rule discovery
Authors: Gokbora Uran
Venue:  (2005)

This dissertation studies hybrid heuristic models in the context of classification rule discovery. Nature inspired search algorithms such as Genetic Algorithms, Ant Colonies and Particle Swarm Optimization have been previously studied on data mining tasks, in particular, classification rule discovery. We extended this work by applying a hybrid model which combines GA, PSO and hill climbers, in same type of classification tasks. Such models have been tested and proved to be performing better than individual search algorithms, in various combinatorial optimization problems. Our research focused on studying the same kind of performance enhancements in classification rule discovery tasks. As a part of this dissertation, we developed a model for a hybrid heuristic based classifier and implemented different variations of it in Java. These algorithms have been benchmarked against the well known decision tree induction algorithm C4.5. Results have been compared in terms of prediction accuracy, speed and comprehensibility. Our results showed that, heuristic based classifiers can compete with C4.5 in terms of prediction accuracy on certain data sets and outperform C4.5 in general in terms of comprehensibility. C4.5 always outperformed heuristic based classifiers in terms of speed due to relative inefficiency inherent in heuristic based classification models. We also showed that hybridization of heuristics could bring improvements in terms of execution speed in comparison to plain heuristic based classifiers.
Performance and Energy Benefits of Instruction Set Extensions in an FPGA Soft Core
Authors: Partha Biswas, Sudarshan Banerjee, Nikil Dutt, Paolo Ienne, Laura Pozzi
Venue: Proceedings of the 19th International Conference on VLSI Design held jointly with 5th International Conference on Embedded Systems Design (2006)

Performance of applications can be boosted by executing application-specificInstruction Set Extensions (ISEs) on a specialized hardware coupled with a processor core. Many commercially available customizable processors have communication overheads in their interface with the specialized hardware. However, existing ISE generation approaches have not considered customizable processors that have communication overheads at their interface. Furthermore, they have not characterized the energy benefits of such ISEs. We present a soft-processor customization framework that takes an input 'C' application and realizes a customized processor capturing the microarchitectural details of its interface with the specialized unit. We are able to accurately measure the speedup, energy, power and code size benefits of our ISE approach on a real system implementation by applying the design flow to a popular Xilinx Microblaze soft-processor core synthesized for four real-life applications. We show that only one large ISE per application is sufficient to get an average 1.41× speedup over pure software execution in spite of incurring communication overheads in the ISE implementation. We also observe a simultaneous savings in energy (up to 40%) and power (up to 12% peak power reduction) with this increased performance.
Temporal Visualization of Planning Polygons for Efficient Partitioning of Geo-Spatial Data
Authors: Poonam Shanbhag, Penny Rheingans, Marie desJardins
Venue: Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization (2005)

Partitioning of geo-spatial data for efficient allocation of resources such as schools and emergency health care services is driven by a need to provide better and more effective services. Partitioning of spatial data is a complex process that depends on numerous factors such as population, costs incurred in deploying or utilizing resources and target capacity of a resource. Moreover, complex data such as population distributions are dynamic i.e. they may change over time. Simple animation may not effectively show temporal changes in spatial data. We propose the use of three temporal visualization techniques - wedges, rings and time slices - to display the nature of change in temporal data in a single view. Along with maximizing resource utilization and minimizing utilization costs, a partition should also ensure the long-term effectiveness of the plan. We use multi-attribute visualization techniques to highlight the strengths and identify the weaknesses of a partition. Comparative visualization techniques allow multiple partitions to be viewed simultaneously. Users can make informed decisions about how to partition geo-spatial data by using a combination of our techniques for multi-attribute visualization, temporal visualization and comparative visualization.
A modified hybrid neural network for pattern recognition and its application to SSW complex in EEG
Authors: Nurettin Ac&#x0131;r
Venue: Neural Computing and Applications (2006)

In this study, a modified hybrid neural network with asymmetric basis functions is presented for feature extraction of spike and slow wave complexes in electroencephalography (EEG). Feature extraction process has a great importance in all pattern recognition and classification problems. A gradient descent algorithm, indeed a back propagation type, is adapted to the proposed artificial neural network. The performance of the proposed network is measured using a support vector machine classifier fed by features extracted using the proposed neural network. The results show that the proposed neural network model can effectively be used in pattern recognition tasks. In experiments, real EEG data are used.
Small Errors in "Toward Formalizing Domain Modeling Semantics in Language Syntax'
Authors: Richard J. Botting
Venue: IEEE Transactions on Software Engineering (2005)

A recent paper on domain modeling had State Charts with semantic errors.
Type-based information flow analysis for the &#x03c0;-calculus
Authors: Naoki Kobayashi
Venue: Acta Informatica (2005)

We propose a new type system for information flow analysis for the &#x03c0;-calculus. As demonstrated by recent studies, information about whether each communication succeeds is important for precise information flow analysis for concurrent programs. By collecting such information using ideas of our previous type systems for deadlock/livelock-freedom, our type system can perform more precise analysis for certain communication/synchronization patterns (like synchronization using locks) than previous type systems. Our type system treats a wide range of communication/synchronization primitives in a uniform manner, which enabled development of a clear proof of type soundness and a sound and complete type inference algorithm.