ppxscal/embedding_network huggingface dataset:
A DFT Approach for Testing Embedded Systems Using DC Sensors
Authors: Soumendu Bhattacharya, Abhijit Chatterjee
Venue: IEEE Design Test (2006)

Today's consumer electronics must be portable, reliable at various operating environments, and power efficient. Thus, semiconductor manufacturers constantly upgrade their production technologies and incorporate intelligent circuit design techniques. With widespread advances in system integration techniques, manufacturers can bundle multiple functionalities onto a single chip, reducing the end product's form factor. However, with higher levels of integration and reduced pin count, test issues are becoming more critical. During high-volume production, variations in process parameters cause devices to vary significantly from their performance metrics, and test engineers have only limited test resources to perform at-speed testing. Generating diagnosis information is also challenging during product ramp-up, as very little information is available from the output pins about the different modules' functionalities. DFT seems to be the only viable solution in such a scenario. DFT can address various issues related to at-speed testing and high-speed test response capture by performing signal conditioning to more easily capture information at lower speeds. The authors present a method that uses embedded DC sensors at test observation nodes to simplify data capture and enhance test quality while performing at-speed tests during production testing. Experiments show that monitoring sensor outputs provides a very good estimate of complex, system-level specifications.
Two-Level Hierarchical Hybrid SVM-RVM Classification Model
Authors: Catarina Silva, Bernardete Ribeiro
Venue: Proceedings of the 5th International Conference on Machine Learning and Applications (2006)

Support Vector Machines (SVM) and Relevance Vector Machines (RVM) constitute two state-of-the-art learning machines that are currently focus of cutting-edge research. SVM present accuracy and complexity preponderance, but are surpassed by RVM when probabilistic outputs or kernel selection come to discussion. We propose a two-level hierarchical hybrid SVM-RVM model to combine the best of both learning machines. The proposed model first level uses an RVM to determine the less confident classified examples and the second level then makes use of an SVM to learn and classify the tougher examples. We show the benefits of the hierarchical approach on a text classification task, where the two-levels outperform both learning machines.
Determining a scene's atmosphere by film grammar rules
Authors: A. Aner-Wolf
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

A new approach for bridging over the semantic gap is presented. This approach is based solely on the extraction of low-level illumination features, and by using well defined film rules regarding scene lighting, it generates high level semantic descriptors for scenes. These illumination descriptors could be further combined with other descriptors like motion and shot tempo, to generate a more complete automatic semantic analysis of video media.
Strategic Issues in Trading Agent Competition: TAC-Classic
Authors: Francisco Oliveira, Yain-Whar Si
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

With the advancement of Internet Technology, increasing number of electronic marketplaces and online auction houses provides real-time transaction services for buyers and sellers regardless of their geographical locations. Easy access to these services has also fueled the need for automating the overall negotiation and decision making process. The annual Trading Agent Competition (TAC) is an international forum designed to provide a competitive benchmark for the trading agents developers around the world. The first TAC was held in July, 2000. In the TAC-Classic category, travel agents compete against each other to assemble travel packages on behalf of several clients. The objective of the travel agent is to maximize the total satisfaction of its clients. During the competition, travel agents may perform three crucial tasks: allocation of resources according to the clients' preferences, prediction of air ticket and hotel prices, and bidding in respective auctions for the required goods. In this paper, we review the algorithms, techniques and heuristics used by the agents which have the highest scores in the last six years of tournaments.
SOON: a tool for software performance validation
Authors: A. D'Ambrogio
Venue: International Journal of Modelling and Simulation (2006)

The automatic derivation of software performance models from CASE documents is a necessary step towards lifecycle validation of the performance of software products (or prediction of the product ability to satisfy the user performance requirements). This paper illustrates Soon (SOftware performance validatiON), a tool that implements a method for the automatic derivation of a software performance model from UML-based documents. The tool also includes a model evaluation facility. SOON allows software designers to overcome the difficulties often encountered when they lack specific knowledge of performance theory, allowing one to predict at design time the performance of various product alternatives. SOON is designed and implemented by use of standards for document exchange (XMI) and for application interoperability (CORBA), so that it can be easily linked or embedded into UML-based CASE environments.
Descendants of a recognizable tree language for sets of linear monadic term rewrite rules
Authors: Sándor Vágvölgyi
Venue: Information Processing Letters (2006)

For a tree language L and a set S of term rewrite rules over Σ, the descendant of L for S is the set S* (L) of trees reachable from a tree in L by rewriting in S. For a recognizable tree language L, we study the set D(L) of descendants of L for all sets of linear monadic term rewrite rules over Σ. We show that D(L) is finite. For each tree automaton A over Σ, we can effectively construct a set {R1,...,Rk} of linear monadic term rewrite systems over Σ such that D(L(A)) = {R1*(L(A)),...,RK*(L(A))} and for any 1 ≤ i < j ≤ k, Ri*(L(A)) ≠ Rj*(L(A)).
Semantic Kernels for Text Classification Based on Topological Measures of Feature Similarity
Authors: Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

In this paper we propose a new approach to the design of semantic smoothing kernels for text classification. These kernels implicitly encode a superconcept expansion in a semantic network using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations of little training data and data sparseness.
PID-filtered Negotiation for Decision Making in Dynamic Coverage Problem
Authors: Satoshi Kataoka, Fuyuki Ishikawa, Shinichi Honiden
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

Negotiation between robots is often needed in wireless ad-hoc robot network. In dynamic coverage problem, wireless ad-hoc robots move in the field negotiating with other robots frequently. Robots are tied with P2P network and determine their actions autonomously by negotiations with other robots. The topic of this paper is a negotiation for Multi-Agent-System (MAS) in the problem of robots activity in dynamic coverage, which is the problem of robots to cover all the areas of a free space by their sensors in the shortest possible time. This paper introduce a novel algorithm for robots' negotiation for MAS in the dynamic coverage problem by using PID-filter. This algorithm reduced useless motion of robots and decreased the time concerning the convergence of a robot's action for dynamic coverage problem. The claim made in this paper for new negotiation algorithm for MAS are supported by comprehensive experimental results and discusses future research directions.
Employing Object-Oriented Design Principles in the Design of Learning Objects in a Software Engineering Course
Authors: Permanand Mohan, Sandra Bucarey A., Ben Daniel
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

This paper describes a study aimed at exploring the design of learning objects based on object-oriented design principles. We present and discuss experiences gained in applying object-oriented software engineering principles to the development of learning objects. While key results indicate that the approach leads to the production of high-quality learning objects, results also reveal that the application of software engineering principles is not sufficient by itself. There is a need to integrate both instructional design and software design principles during the learning object development process.
A performance-oriented energy efficient file system
Authors: Dong Li, Jun Wang
Venue: Proceedings of the international workshop on Storage network architecture and parallel I/Os (2004)

Current general-purpose file systems emphasize the consistency of standard file system semantics and performance issues rather than energy-efficiency. In this paper we present a novel energy efficient file system called EEFS to effectively both reduce energy consumption and improve performance by separately managing those small-sized files with a good group access locality. To keep compatibility, EEFS consists of two working modules: a normal Unix-like File System (UFS) and a group-structured file system (GFS) that are transparent to user applications. EEFS contributes a new grouping policy that can construct files groups with group access locality and be used to migrate files between UFS and GFS. Comprehensive trace-driven simulation experiments show that EEFS achieves a great energy savings by up to 50% compared to that of the general-purpose UNIX file system, and simultaneously delivers a better file I/O performance by up to 21%.
Opening the Black Box of Feature Extraction: Incorporating Visualization into High-Dimensional Data Mining Processes
Authors: Jianting Zhang, Le Gruenwald
Venue: Proceedings of the Sixth International Conference on Data Mining (2006)

Feature extraction techniques have been used to handle high-dimensional data and experimental studies often show improved classification accuracies. Unfortunately very few studies provide concrete evidences on the effectiveness of these feature extraction techniques and they largely remain to be black boxes. In this study, we design and implement a visualization prototype system that allows users to look into the classification processes, explore the links among the original and extracted features in different classifiers, examine why and how an instance is correctly or incorrectly classified. We demonstrate the prototype's capabilities by combining a feature extraction method based on hierarchical feature space clustering with J48 decision tree classifiers and perform experiments on a real hyperspectral remote sensing image dataset.
An Algorithm for Predicting Customer Churn via BP Neural Network Based on Rough Set
Authors: E Xu, Liangshan Shao, Xuedong Gao, Baofeng Zhai
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

To solve the prediction of customer churn, the paper proposed a new algorithm. Based on rough set theory, the algorithm used the consistency of condition attributes and decision attributes in information table, and the conception of super-cube and scan vector to discretize the continuous attributes, reduce the redundant attributes. And furthermore, it took BP neural network as the calculating tool to predict customer churn. The experimental results showed the refined data by rough set was more concise and more convenient to be applied in BP neural network, whose prediction result was more accurate. So, the algorithm via BP neural network based on rough set theory is efficient and effective.
Automated Physical Storage Provision Using a Peer-to-Peer Distributed File System
Authors: Simon Chong, Paul A. Watters, Michael Hitchens
Venue: Proceedings of the 21st International Conference on Data Engineering Workshops (2005)

The allocation and management of physical storage structures in relational database systems is a timeconsuming manual process undertaken by database administrators. If datafiles, redo logs and control files exceed the available disk space on a volume, then new space will need to physically allocated on a new device. This reduces availability due to downtime, and arbitrary assumptions are often made about sizing requirements in the allocation of new storage media. What is required is an infinitely extensible, logically striped volume that can request and access storage on demand. In this paper, we present a Peer-to-Peer distributed file system that creates a single, virtual striped volume that can be mounted as a normal logical file system through the Common Internet File System (CIFS). New peers contribute unused space as required by the dynamic and growing physical storage requirements of relational databases.
Comparing market and token-based coordination
Authors: Yang Xu, Paul Scerri, Katia Sycara, Michael Lewis
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Many coordination algorithms claim to be general, implying that they can be used to coordinate agents in a variety of domains. However, little work has been done to quantitatively compare distinctly different approaches to coordination across a range of domains. In this paper, we present a detailed comparison of two published coordination algorithms, performed in an abstract coordination simulation environment that allows extensive, quantitative experimentation. The simulator is to used to compare two distinct approaches to coordination, token-based coordination and market based coordination. The results largely show the generality of different approaches, but performance and performance tradeoffs varies greatly across domains.
Exploring learner conceptions of programming
Authors: Errol Thompson, Lynn Hunt, Kinshuk Kinshuk
Venue: Proceedings of the 8th Austalian conference on Computing education - Volume 52 (2006)

The objective of this paper is to argue for an approach to the scholarship of teaching based on conceptions of learning and task representation. A case study based on learning to implement a data structure using test-driven development is presented to illustrate the issues. Documented phenomenographic investigations into concepts of programming are referenced to lay a foundation for future research into improving learning and teaching of programming themes.
From emergency departments to cottage cheese to fire departments: learning simulation experimentation through WebGPSS
Authors: Richard G. Born
Venue: Proceedings of the 36th conference on Winter simulation (2004)

An important component of any simulation course is the discussion of experimental design. WebGPSS has been used by the author for two years to discuss experimentation in an introductory course in discrete-event simulation. This paper discusses how to set up simulation experiments using WebGPSS by presenting three business problems whose solutions require careful attention to experimental design. The first problem looks at staffing an emergency department of a hospital with physicians. The second problem involves the optimal method for stocking a perishable food product such as cottage cheese on a supermarket shelf, showing that under realistic conditions, spoilage can be minimized by placing newer containers in the front. The third problem involves analysis of a proposal for two neighboring communities currently operating completely separate fire departments to integrate their two systems in a way that could reduce the amount of time that fires are unattended.
Computational approach to damped Beck's column
Authors: Y. Sugiyama, Si-Ung Ryu, M. Hamatani, T. Iwama
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 4 (2003)

The paper discusses on the basis of computational energy approach the effect of damping on stability of damped Beck's column. By making the present computational approach to the damped Beck's problem, it has been demonstrated that though the internal damping dissipates energy, the damping at the same time help the non-conservative force to do more work on the column. This paper aims at explaining the mechanism of the effect of damping on stability of non-conservative systems.
Defending against Distributed Denial of Service (DDoS) Attacks with Queue Traffic Differentiation over Micro-MPLS-based Wireless Networks
Authors: Scott Fowler, Sherali Zeadally
Venue: Proceedings of the International Conference on Systems and Networks Communication (2006)

Traditional security research has been on privacy or authentication. Unfortunately, attacks on wireless media cannot be simply addressed using traditional security methods [1]. An example of a security threat which is difficult to address using traditional network security techniques is Denial of Service (DoS) attacks. Under DoS attacks it is difficult to provide legitimate users their fair shares of bandwidth. However, it is important to allocate a fair share of bandwidth for multimedia traffic requiring Quality of Service. In this paper we address the issue of DoS attacks between mobile nodes and MPLS domains by proposing a queue differentiation policy at the entry point of the MPLS domain. We also investigated DoS attacks of the entry point (Foreign Domain Agent) into Micro-MPLS-based networks (otherwise know a Hierarchical Mobile MPLS (HMPLS)) [2]. Our simulation results show that our proposed technique provides maximum resource utilization by increasing the throughput of multimedia traffic while the network is under DoS attacks.
Benchmark lessons and the world wide web: tools for teaching statistics
Authors: Andrew Schaffner, Edith A. Graf, David Madigan, Jim Minstrell, Earl Hunt, Martha Nason
Venue: Proceedings of the 1996 international conference on Learning sciences (1996)

With the advances in computing technology over the past 15 years, much of applied statistics has changed. Researchers and students can now easily fit a wide variety of complex models and generate elaborate graphical displays of data with the press of a few buttons on a keyboard. Statistical software with graphical user interfaces does not require a conceptual understanding of the driving ideas behind the applications: design of experiments, data collection, descriptive statistics, and statistical inference. While traditional undergraduate applied statistics courses may provide some background and useful algorithms, they frequently leave a student with a cook-book of algorithmic recipes rather than any true understanding [Hawkins, et al. 1992], [Garfield & Ahlgren 1988]. In addition, while students may be able to memorize some algorithmic details to solve a familiar well-defined problem, they often fail to grasp the underpinnings of the methods and are therefore unable to apply their knowledge to new situations.
Resource Management for Control Tasks Based on the Transient Dynamics of Closed-Loop Systems
Authors: Rosa Castane, Pau Marti, Manel Velasco, Anton Cervin
Venue: Proceedings of the 18th Euromicro Conference on Real-Time Systems (2006)

This paper presents a resource management strategy for control tasks that maximizes control performance within the available resources by readjusting the task periods at runtime. A feedback scheduler is used to determine on-line the optimal task periods considering the response over a finite time horizon of the plants controlled by arbitrary linear control laws. We show how this problem can be expressed as an optimization problem, where the objective function relates the sampling periods to the transient responses of the controlled plants, and where restrictions are based on EDF schedulability constraints. For the general case, the solution of the optimization problem is computationally expensive, and thus, an approximate procedure to be executed on-line has been developed. We present simulation results that validate the presented approach.
Accessing and Distributing Streaming Events on DHT-Based Systems
Authors: M. Milanesio, G. Ruffo, F. Ricchiuti, D. Albertin
Venue: Proceedings of the Second International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (2006)

In this paper we present a time sensitive content distribution system under implementation with the following innovative features: (a) events can be accessed and distributed using a fully decentralized DHT-based platform, (b) the bootstrap problem is solved by means of a permanent core network made of always alive nodes, (c) the streams are served through a forest of collaborating nodes interested in the events, by means of a topology independent from the one of the underlying DHT structure.
COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses
Authors: Thomas King, Stephan Kopf, Thomas Haenselmann, Christian Lubberger, Wolfgang Effelsberg
Venue: Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation characterization (2006)

Positioning systems are one of the key elements required by location-based services. This paper presents the design, implementation and analysis of a positioning system called COMPASS which is based on 802.11-compliant network infrastructure and digital compasses. On the mobile device, COMPASS samples the signal strength values of different access points in its communication range and utilizes the orientation of the user to preselect a subset of the training data. The remaining training data is used by a probabilistic positioning algorithm to determine the position of the user. While prior systems show limited accuracy due to blocking effects caused by the human body, we apply digital compasses to detect the orientations of the users so that we can deal with these blocking effects. After a short period of training our COMPASS system achieves an average error distance of less than 1.65 meters in our experimental environment of 312 square meters.
Large Scale Neurocognitive Networks Underlying Episodic Memory
Authors: Lars Nyberg, Jonas Persson, Reza Habib, Endel Tulving, Anthony R. McIntosh, Roberto Cabeza, Sylvain Houle
Venue: Journal of Cognitive Neuroscience (2000)

Large-scale networks of brain regions are believed to mediate cognitive processes, including episodic memory. Analyses of regional differences in brain activity, measured by functional neuroimaging, have begun to identify putative components of these networks. To more fully characterize neurocognitive networks, however, it is necessary to use analytical methods that quantify neural network interactions. Here, we used positron emission tomography (PET) to measure brain activity during initial encoding and subsequent recognition of sentences and pictures. For each type of material, three recognition conditions were included which varied with respect to target density (0%, 50%, 100%). Analysis of large-scale activity patterns identified a collection of foci whose activity distinguished the processing of sentences vs. pictures. A second pattern, which showed strong prefrontal cortex involvement, distinguished the type of cognitive process (encoding or retrieval). For both pictures and sentences, the manipulation of target density was associated with minor activation changes. Instead, it was found to relate to systematic changes of functional connections between material-specific regions and several other brain regions, including medial temporal, right prefrontal and parietal regions. These findings provide evidence for large-scale neural interactions between material-specific and process-specific neural substrates of episodic encoding and retrieval.