ppxscal/embedding_network huggingface dataset:
A Mapping Scheme of XML Documents into Relational Databases using Schema-based Path Identi.ers
Authors: Kei Fujimoto, Dao Dinh Kha, Masatoshi Yoshikawa, Toshiyuki Amagasa
Venue: Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration (2005)

In this paper, we propose a mapping scheme of XML documents into relational databases. The scheme enables us to store, retrieve and update XML documents efficiently. When storing XML documents in relational databases, XML tree structures must be preserved explicitly. To this end, a label is assigned to nodes in the XML tree. In general, document retrieval and update performance is affected by node labeling schemes. We use SPIDER (Schema based Path IDenti- fier), a labeling scheme for XML documents utilizing DTDs that makes retrieval and update more efficient. SPIDER only identi?es paths from root node to a node. Thus, multiple nodes appearing in the same path cannot be distinguished by only using SPIDER. We introduced Sibling Dewey Order to identify such nodes. Generally, when a new node is inserted into XML documents, some other nodes need to be relabeled to preserve the order of nodes. In our method, only Sibling Dewey Order is relabeled; SPIDER is not affected. Since the range of relabeling is small, it is possible to update documents efficiently. We stored documents utilizing SPIDER in a relational database and then translated various XPath expressions into SQL using SPIDER. We perform experiments and demonstrate that the proposed scheme outpeforms conventional methods both in retrieval and update.
Give your bootstrap the boot: using the operating system to boot the operating system
Authors: R. G. Minnich
Venue: Proceedings of the 2004 IEEE International Conference on Cluster Computing (2004)

One of the slowest and most annoying aspects of system management is the simple act of rebooting the system. The sysadmin starts from a known state $the OS is running - and hands the computer over to an untrustworthy piece of software. With enough nodes involved, there is a certain chance that the process will fail on one of them. Bootstrapping is well named - it takes the system down to a low level, from which return is uncertain. It would be much better if we could use the known, trusted OS software to manage the boot process. The OS can apply all its power to the problem of locating, verifying, and loading a new OS image. Error checking and feedback can be far more robust. We discuss five systems for Linux and Plan 9 that allow the OS to boot the OS. These systems allow for the complete elimination of old-fashioned bootstrap.
(CBR-3) Intelligent knowledge-based systems: expert and agent systems
Authors: Cornelius T. Leondes
Venue:  (1995)

Expert and Agent Systems, considers such application areas as image databases, business process monitoring, e-commerce, and production planning and scheduling, offering a wide range of perspectives and business-function concentrations to stimulate readers' innovative thought.
Harmless advice
Authors: Daniel S. Dantas, David Walker
Venue: Conference record of the 33rd ACM SIGPLAN-SIGACT symposium on Principles of programming languages (2006)

This paper defines an object-oriented language with harmless aspect-oriented advice. A piece of harmless advice is a computation that, like ordinary aspect-oriented advice, executes when control reaches a designated control-flow point. However, unlike ordinary advice, harmless advice is designed to obey a weak non-interference property. Harmless advice may change the termination behavior of computations and use I/O, but it does not otherwise influence the final result of the mainline code. The benefit of harmless advice is that it facilitates local reasoning about program behavior. More specifically, programmers may ignore harmless advice when reasoning about the partial correctness properties of their programs. In addition, programmers may add new pieces of harmless advice to pre-existing programs in typical "after-the-fact" aspect-oriented style without fear they will break important data invariants used by the mainline code.In order to detect and enforce harmlessness, the paper defines a novel type and effect system related to information-flow type systems. The central technical result is that well-typed harmless advice does not interfere with the mainline computation. The paper also presents an implementation of the language and a case study using harmless advice to implement security policies.
Security Standards for the RFID Market
Authors: Ted Phillips, Tom Karygiannis, Rick Huhn
Venue: IEEE Security and Privacy (2005)

With the potential to dramatically decrease costs and improve efficiency in diverse application areas ranging from asset tracking and supply chain automation to healthcare patient administration and animal tracking, RFID has become one of the fastest-growing new IT technologies. In order for businesses to recover costs, RFID tags must be inexpensive, making security infrastructure difficult to install. The public has reacted with alarm to the possibility that anyone with an RFID reader could obtain information on their purchases, finances, and travel habits, and possibly even more sensitive information such as medical records, and thus, in response, manufacturers are introducing more privacy and security features in new RFID standards.In this column we survey current and future developments in RFID security standards.
Multi-agent and market based dynamic optimization and its extensions to distributed supply chain procurement planning problems
Authors: Soundar Kumara
Venue:  (2005)

This thesis discusses distributed computational methodologies used for solving supply chain procurement planning problems with information systems naturally distributed at different locations. The planning problem is heavily influenced by transportation costs, whose calculation is based on a stochastic environment with random service and travel times. The main contributions of this thesis are the computational methodologies that solve the problem under different conditions. All these methodologies are developed in the context of supply chain procurement planning, but they are not necessarily restricted to this context. They can be modified and adapted to other distributed planning and scheduling problems. The Double Auction Market Mechanism (DAMM) is an innovative distributed negotiation mechanism for supply chain procurement planning. It is developed to achieve a common objective among multiple individual decision makers by modeling their respective interests in a virtual market for trading transportation tasks. The trading mechanism emulates the double auction institutions of a real market. For trading, double auction institutions enable exchange of transportation tasks among a transportation company and its trucks, which are modeled as software agents. An agent's decision making among market interactions is based on a set of stochastic rules, which consider its self-interest with the support of transportation cost evaluation. Iterated task exchange among agents drives the task allocation to Pareto optimality, which is derived from the concept of market equilibrium. DAMM is an any-time negotiation protocol, so it is useful for dynamic optimization and contingency planning. A Coordinated Reinforcement Learning (CRL) method is developed to refine the distributed transportation plans generated by distributed heuristics based on DAMM. Through analyzing the drawbacks of the distributed double auction heuristic due to its strict market rules, we enable the task agents in the virtual market to return to their previous stages and reconsider their decisions for market trading interactions. This is realized in the framework of reinforcement learning (RL). To be adaptive in the framework of RL, the task allocation derived from the transportation plans is defined as a state; the collective proposals of the task agents are defined as an action ; the cost saving during one round of market interaction is defined as a reward; the update of transportation plans resulting from updating task allocation is defined as a state transition. CRL with tabular representations effectively refines the distributed transportation plans generated by DAMM. (Abstract shortened by UMI.)
Logistic regression with an auxiliary data source
Authors: Xuejun Liao, Ya Xue, Lawrence Carin
Venue: Proceedings of the 22nd international conference on Machine learning (2005)

To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper we propose a method to relax this requirement in the context of logistic regression. Assuming Dp and Da are two sets of examples drawn from two mismatched distributions, where Da are fully labeled and Dp partially labeled, our objective is to complete the labels of Dp. We introduce an auxiliary variable &mu; for each example in Da to reflect its mismatch with Dp. Under an appropriate constraint the &mu;'s are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples in Dp. The proposed algorithm, called "Migratory-Logit" or M-Logit, is demonstrated successfully on simulated as well as real data sets.
Multidimensional Exponential Timestepping with Boundary Test
Authors: Kalvis M. Jansons, G. D. Lythe
Venue: SIAM Journal on Scientific Computing (2005)

Exponential timestepping algorithms are efficient for exit-time problems because a boundary test can be performed at the end of each timestep, giving high-order convergence in numerical evaluation of mean exit times. Successive time increments are independent random variables with an exponential distribution. We show how to perform exact timestepping for Brownian motion in more than one dimension and consider hitting times of curved surfaces. Taking as examples the exit times from a circle, from a ball, from an ellipse, and from the region bounded by the two lines of a hyperbola, we report the results of numerical experiments performed with the algorithms developed in this work.
Genetically Evolving Higher Order Neural Networks by Direct Encoding Method
Authors: Abdul Ahad Siddiqi
Venue: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications (2005)

There are two major ways of encoding a neural network into a chromosome, as required in design of a Genetic Algorithm (GA). These are Explicit (direct) and Implicit (Indirect) encoding methods. The proposed direct encoding method to design Higher Order Neural Networks (HONN) does not use any known learning algorithm - rather it uses a gradient descent method to minimize the mean output error. The simple feed-forward network only uses one pass, called forward pass contrary to the standard learning algorithm which does the training in two passes. This saves an enormous amount of training time and the network converges to an optimum value as compared to other learning strategies.
Brain-Based Devices for the Study of Nervous Systems and the Development of Intelligent Machines
Authors: Jeffrey L. Krichmar, Gerald M. Edelman
Venue: Artificial Life (2005)

The simultaneous study of brain function at all levels of organization is difficult to undertake with current experimental tools. Present day electrophysiology only allows the recording of at most hundreds of neurons while an animal is performing a behavioral task. Because of this limitation and the sheer complexity of the nervous system, computational modeling has become essential in developing theories of brain function. Accordingly, our group has constructed a series of brain-based devices (BBDs), that is, physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for testing theories of brain function. Unlike animal models, BBDs permit analysis of activity at all levels of the nervous system as the device behaves in its environment. Although the principal focus of developing BBDs has been to test theories of brain function, this type of modeling may also provide a basis for robotic design and practical applications.
Evaluation of statistical program packages
Authors: Paul Velleman
Venue: ACM SIGSOC Bulletin (1974)

I enjoyed and agree with the article on peer review of computing materials by Roberta Ash, Francis Sim, and Ronald Anderson in the last issue of the SIGSOC Bulletin. I am writing to report that a project to sponsor, collect, and publish reviews of statistical Computing software has recently begun under the auspices of the Statistical Computing Section of the American Statistical Association. The committee doing this work is chaired by Ivor Francis (358 Ives Hall, Cornell University, Ithaca, New York 14850) and Richard Heiberger (Department of Statistics and Operations Research, Wharton School, University of Pennsylvania, Philadelphia, Penn. 19104). They have issued a preliminary proposal of specific criteria for use by reviewers which I have summarized below. I will be chairing a liaison committee to facilitate communication between this group and SIGSOC. Readers with ideas and suggestions are urged to send them to me or to either of the co-chairmen.
Canonical representations of discrete curves
Authors: Fabien Feschet
Venue: Pattern Analysis Applications (2005)

A new representation of digital curves is introduced. It has the property of being unique and canonical when computed on closed curves. The representation is based on the discrete notion of tangents and is complete in the sense that it contains all discrete segments and all polygonalizations which can be constructed with connected subsets of the original curve. This representation is extended for dealing with noisy curves and we also propose a multi-scale extension. An application is given to curve decomposition into concave&#x2013;convex parts and with application in syntactical based methods.
Thermal Management of On-Chip Caches Through Power Density Minimization
Authors: Ja Chun Ku, Serkan Ozdemir, Gokhan Memik, Yehea Ismail
Venue: Proceedings of the 38th annual IEEE/ACM International Symposium on Microarchitecture (2005)

Various architectural power reduction techniques have been proposed for on-chip caches in the last decade. However, these techniques mostly ignore the effects of temperature on the power consumption. In this paper, first we show that these power reduction techniques can be suboptimal when thermal effects are considered. Particularly, we propose a thermal-aware cache powerdown technique that minimizes the power density of the active parts by turning off alternating rows of memory cells instead of entire banks. The decrease in the power density lowers the temperature, which in return, reduces the leakage of the active parts. Simulations based on SPEC2000 benchmarks in a 70nm technology show that the proposed thermal-aware architecture can reduce the total energy consumption by 53% compared to a conventional cache, and 14% compared to a cache architecture with thermal-unaware power reduction scheme. Second, we show a block permutation scheme that can be used during the design of caches to maximize the distance between blocks with consecutive addresses. By maximizing the distance between consecutively accessed blocks, we minimize the power density of the hot spots in the cache, and hence reduce the peak temperature. This, in return, results in an average leakage power reduction of 8.7% compared to a conventional cache without affecting the dynamic power and the latency. Overall, both of our architectures add no extra run-time penalty compared to the thermalunaware power reduction schemes, yet they reduce the total energy consumption of a conventional cache by 53% and 5.6% on average, respectively.
An Ontology Learning Method Enhanced by Frame Semantics
Authors: Enhong CHEN, Gaofeng WU
Venue: Proceedings of the Seventh IEEE International Symposium on Multimedia (2005)

Ontology Learning is a method that can be used by ontology engineers to construct ontology more easily. With the rapid development of Semantic Web and the ever increasing need for ontology, Ontology Learning has been regarded as one of the most important fields in the Semantic Web related research work. In recent years, a lot of work has been done to design appropriate methods for ontology learning all over the world. But all these methods have some common shortcomings which limit their abilities. In this paper, we first analyze the characteristics of these shortcomings and then propose our own ontology learning method based on the theory of frame semantic which can overcome the shortcomings mentioned above and facilitate the ontology learning task. The experiment results show that this method could improve the performance of the ontology learning system.
Natural Language Processing
Authors: Alexander Gelbukh
Venue: Proceedings of the Fifth International Conference on Hybrid Intelligent Systems (2005)

Natural Language Processing (NLP) is a major area of artificial intelligence research, which in its turn serves as a field of application and interaction of a number of other traditional AI areas. Until recently, the focus in AI applications in NLP was on knowledge representation, logical reasoning, and constraint satisfaction-first applied to semantics and later to the grammar. In the last decade, a dramatic shift in the NLP research has led to the prevalence of very large-scale applications of statistical methods, such as machine learning and data mining. Naturally, this also opened the way to the learning and optimization methods that constitute the core of modern AI, most notably genetic algorithms and neural networks. In this talk I will give an overview of the current trends in NLP and discuss the possible applications of traditional AI techniques and their combination in this fascinating area.
Debugging Sequential Circuits Using Boolean Satisfiability
Authors: Moayad Fahim Ali, Andreas Veneris, Sean Safarpour, Magdy Abadir, Rolf Drechsler, Alexander Smith
Venue: Proceedings of the Fifth International Workshop on Microprocessor Test and Verification (2004)

Logic debugging of today's complex sequential circuits is an important problem. In this paper, a logic debugging methodology for multiple errors in sequential circuits with no state equivalence is developed. The proposed approach reduces the problem of debugging to an instance of Boolean Satisfiability. This formulation takes advantage of modern Boolean Satisfiability solvers that handle large circuits in a computationally efficient manner. An extensive suite of experiments with large sequential circuits confirm the robustness and efficiencyof the proposed approach. The results further suggest that Boolean Satisfiability provides an effective platform for sequential logic debugging.
Understanding intent to use the internet by group meeting planners
Authors: Joseph Levesque
Venue:  (2005)

The research presented in this study will examine the relationship between planned use of the Internet and actual use by group meeting planners in the hospitality industry. Understanding the factors that influence meeting planners to choose one hotel over another when it comes to planning and booking a meeting is beneficial to the hotel industry. The methods of hotel distribution have changed over the last decade with the introduction of the Internet, which has changed the way business must be conducted. Understanding the factors that affect planners' intentions can improve the efficiency of a hotels' marketing communications. This study was conducted among a population of active meeting planners and shows the impact of attitude, beliefs, and ease of use on intent to use the Internet to plan a group meeting.
Effect of the dielectric thickness and the metal deposition technique on the mobility for HfO2/TaN NMOS devices
Authors: L. Trojman, L.-Å Ragnarsson, L. Pantisano, G. S. Lujan, M. Houssa, T. Schram, F. Cubaynes, M. Schaekers, A. Van Ammel, G. Groeseneken, S. De Gendt, M. Heyns
Venue: Microelectronic Engineering (2005)

In this paper the effects from the high-k dielectric thickness and the metal gate deposition technique on the mobility of n-channel MOS transistors are investigated. The results reveal mobility degradation due to an increase of charge density in the dielectric and / or at the material interfaces, not efficiently compensated by the screening effect from the gate. We correlate this mobility degradation to the reduction observed in a comparison between metal and poly-Si gated MOSFETs. It is shown that the mobility can be improved by using different metals deposition technique, which indicates that the mobility reduction is related to the deposition technique.
A Conversation with Ray Ozzie
Authors: Charlene O'Hanlon
Venue: Queue (2005)

There are not many names bigger than Ray Ozzie's in computer programming. An industry visionary and pioneer in computer-supported cooperative work, he began his career as an electrical engineer but fairly quickly got into computer science and programming. He is the creator of IBM's Lotus Notes and is now chief technical officer of Microsoft, reporting to chief software architect Bill Gates. Recently, Ozzie's role as chief technical officer expanded as he assumed responsibility for the company's software-based services strategy across its three major divisions.
Semi-supervised learning with graphs
Authors: Xiaojin Zhu
Venue:  (2005)

In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice. We present a series of novel semi-supervised learning approaches arising from a graph representation, where labeled and unlabeled instances are represented as vertices, and edges encode the similarity between instances. They address the following questions: How to use unlabeled data? (label propagation); What is the probabilistic interpretation? (Gaussian fields and harmonic functions); What if we can choose labeled data? (active learning); How to construct good graphs? (hyperparameter learning); How to work with kernel machines like SVM? (graph kernels); How to handle complex data like sequences? (kernel conditional random fields); How to handle scalability and induction? (harmonic mixtures). An extensive literature review is included at the end.
Data relaying with optimal resource management in wireless sensor networks (Extended Abstract)
Authors: Robert Benkoczi, Hossam Hassanein, Selim Akl, Sylvia Tai
Venue: Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary (2005)

A typical application of wireless sensor networks (WSNet) [1] is the following. Given certain regions of interest from the environment, the problem is to design and deploy a WSNet whose sensors monitor the given regions, acquire information, and transmit this information via radio signals to a central location called the base station. There are several critical resources that need to be considered by the designer. (a) Cost: the cost of a WSNet is generally the actual cost of purchasing the sensors. It depends on the number and type of sensor nodes used in the design. (b) Lifetime: the lifetime of a WSNet is the duration of normal operation for the WSNet. Frequently, lifetime is defined as the time until a node in the system becomes inoperative because of an exhausted power supply. (c) Quality of service: additional application specific requirements exists, such as limiting the delay with which data arrive at the base station.