ppxscal/embedding_network huggingface dataset:
Solving variational inequalities and related problems using recurrent neural networks
Authors: Jun Wang
Venue:  (2007)

Variational inequality (VI) can be viewed as a natural framework for unifying the treatment of equilibrium problems, and hence has applications across many disciplines. In addition, many typical problems are closely related to VI, including general VI (GVI), complementarity problem (CP), generalized CP (GCP) and optimization problem (OP). During the past two decades, numerous recurrent neural networks (RNNs) have been proposed for solving VIs and related problems. However, first, the theories of many emerging RNNs have not been well founded yet; and their capabilities have been underestimated. Second, these RNNs have limitations in handling some types of problems. Third, it is certainly not true that these RNNs are best choices for solving all problems, and new network models with more favorable characteristics could be devised for solving specific problems. In the research, the above issues are extensively explored from dynamic system perspective, which leads to the following major contributions. On one hand, many new capabilities of some existing RNNs have been revealed for solving VIs and related problems. On the other hand, several new RNNs have been invented for solving some types of these problems. The contributions are established on the following facts. First, two existing RNNs, called TLPNN and PNN, are found to be capable of solving pseudomonotone VIs and related problems with simple bound constraints. Second, many more stability results are revealed for an existing RNN, called GPNN, for solving GVIs with simple bound constraints, and it is then extended to solve linear VIs (LVIs) and generalized linear VIs (GLVIs) with polyhedron constraints. Third, a new RNN, called IDNN, is proposed for solving a special class of quadratic programming problems which features lower structural complexity compared with existing RNNs. Fourth, some local convergence results of an existing RNN, called EPNN, for nonconvex optimization are obtained, and two variants of the network by incorporating two augmented Lagrangian function techniques are proposed for seeking Karush-Kuhn-Tucker (KKT) points, especially local optima, of the problems.
Embedding and Confluence of Graph Transformations with Negative Application Conditions
Authors: Leen Lambers, Hartmut Ehrig, Ulrike Prange, Fernando Orejas
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

The goal of this paper is the generalization of embedding and confluence results for graph transformation systems to transformation systems with negative application conditions (NACs). These conditions restrict the application of a rule by expressing that a specific structure must not be present before or after applying the rule to a certain context. Such a condition influences each rule application and transformation and therefore changes significantly the properties of the transformation system. This behavior modification is reflected by the generalization of the Embedding Theorem and the Critical Pair Lemma or Local Confluence Theorem, formulated already for graph transformation systems without negative application conditions. The results hold for adhesive high-level replacement systems with NACs and are formulated in this paper for the instantiation to double-pushout graph transformation systems with NACs. All constructions and results are explained on a running example.
Unobtrusive Dynamic Modelling of TV Program Preferences in a Household
Authors: Elena Vildjiounaite, Vesa Kyllönen, Tero Hannula, Petteri Alahuhta
Venue: Proceedings of the 6th European conference on Changing Television Environments (2008)

Majority of recommender systems require explicit user interaction (ranking of movies and TV programs and/or their metadata, such as genres, actors etc), which requires user time and effort. Furthermore, often such ranking is done separately by each person, while merging these manually acquired preferences in multi-user environments remains largely unsolved problem. This work presents a method to learn a model of multi-user environment in intelligent home from implicit interactions: the choices which family members make together and separately. In tests on TV viewing histories of twenty families, acquired during two months, the method has achieved prediction accuracy comparable with the accuracy of systems which require explicit user ratings: a set of TV programs, actually viewed during each test session (average set size was 2.2 programs per viewing session), was recommended among five top choices in 60% of cases on average, despite training on small data sets.
A New Approach to the Functional Design of a Digital Computer
Authors: R. S. Barton
Venue: IEEE Annals of the History of Computing (1987)

The present methods of determining the functional design of computers are critically reviewed and a new approach proposed. This is illustrated by explaining, in abstracted form, part of the control organization of a new and different machine based, in part, on the ALGOL 60 language. The concepts of expression and procedure lead directly to use of a Polish string program. A new arrangement of control registers results, which provides for automatic allocation of temporary storage within expressions and procedures, and a generalized subroutine linkage. The simplicity and power of these notions suggests that there is much room for improvement in present machines and that more attention should be given to control functions in new designs.(1961).
Reducing false reads in RFID-embedded supply chains
Authors: Yu-Ju Tu, Selwyn Piramuthu
Venue: Journal of Theoretical and Applied Electronic Commerce Research (2008)

RFID tags are increasingly being used in supply chain applications due to their potential in engendering supply chain visibility and reducing tracking errors through instantaneous location of objects as well as the tagged object's immediate ambient conditions. During its life-time, an RFID tag is highly likely to physically pass through different organizations that have disparate information systems with varying information needs. We consider management of data in these applications with respect to volume, data and query characteristics, privacy and security, among others, and propose means to reduce false positive and false negative reads. The beneficial properties of RFID tags can be realized only when they are readable with a high degree of accuracy. The read rate accuracy in turn depends on several factors including the medium and distance between tag and reader, presence of objects that are impenetrable by RF signal, orientation of tag with respect to the reader, among others. We propose three algorithms that address read rate accuracy, and illustrate their relative performance using an example scenario.
Computing Argumentation for Decision Making in Legal Disputes
Authors: Maxime Morge
Venue: Computable Models of the Law: Languages, Dialogues, Games, Ontologies (2008)

In this paper, we present a decision support system for lawyers. This system is built upon an argumentation framework for decision making. A logic language is used as a concrete data structure for holding the statements like knowledge, goals, and decisions. Different priorities are attached to these items corresponding to the uncertainty of the knowledge about the circumstances, the lawyer's preferences, and the expected utilities of sentences. These concrete data structures consist of information providing the backbone of arguments. Due to the abductive nature of practical reasoning, arguments are built by reasoning backwards, and possibly by making suppositions over missing information. Moreover, arguments are defined as tree-like structures. In this way, our computer system, implemented in Prolog, suggests some actions and provides an interactive and intelligible explanation of this solution.
Just how risky is online voting?
Authors: Stephen Coleman
Venue: Information Polity (2005)

The UK Government is committed to experimentation with online voting and has promoted Internet voting in local elections in 2002 and 2003. This article examines the risks of online voting and suggests that political risks should not be conceived in terms of an elusive scientific objectivity, but should be understood as arising from reflexive contestations involving a range of actors and contexts. After examining the risk of online voting from several perspectives, the article concludes by arguing for a more politicised and nuanced conception of risk which takes into account non-functional aspects of voting, such as the symbolic.
Efficient Location Management Scheme for Inter-MAP Movement Using M/G/1 Multi-class Queues in Hierarchical MIPv6
Authors: Jonghyoun Choi, Teail Shin, Youngsong Mun
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

Hierarchical Mobile IPv6 (HMIPv6) has been proposed to accommodate frequent mobility of an MN and reduce the signaling load in the Internet. In this paper, we propose hierarchical management scheme for Mobile IPv6 where MAP (Mobility Anchor Point) has priority queues for faster processing location update procedure in HMIPv6 system. The performance analysis and the numerical results presented in this paper show that our proposal has superior performance to the hierarchical Mobile IPv6.
A Constrained Dynamic Evolutionary Algorithm with Adaptive Penalty Coefficient
Authors: Bo Xiao, Danpin Yu, Lei Zhang, Xin Tian, Song Gao, Sanyou Zeng
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

This paper proposes a new evolutionary algorithm with adaptive penalty coefficient. Firstly, the crossover operator of the new algorithm searches a lower-dimensional neighbor of the parent points, so that the algorithm converges fast, especially for high-dimensional problems. Secondly, the violation values of all constraint functions and the value of the objective function are normalized, and therefore, only one penalty coefficient is needed in the scheme. The penalty coefficient is selected adaptively. It is not too big, so as the algorithm can converge fast, and it is not too small so as the algorithm can avoid local optimal as much as possible. Thirdly, the standard deviation of violation values of the constraint functions is added to the violation item in the penalty function, and therefore, the individuals in the population can evenly approach the feasible region from the infeasible space. We have used the 24 constrained benchmark problems to test the new algorithm. The experimental results show it works better than or competitive to a known effective algorithm [7]
Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System
Authors: Roger Nkambou, Engelbert Mephu Nguifo, Olivier Couturier, Philippe Fournier-Viger
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

In an intelligent tutoring system (its), the domain expert should provide relevant domain knowledge to the tutor so that it will be able to guide the learner during problem solving. However, in several domains, this knowledge is not predetermined and should be captured or learned from expert users as well as intermediate and novice users. Our hypothesis is that, knowledge discovery (kd) techniques can help to build this domain intelligence in ITS. This paper proposes a framework to capture problem-solving knowledge using a promising approach of data and knowledge discovery based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta knowledge and rules in a given domain which then extend domain knowledge and serve as problem space allowing the intelligent tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.
Study on Framework of Scalable High Performance Multi-layer and Multi-pattern Information Systems
Authors: Jie Zhao, Nanfeng Xiao
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

To avoid repetitive work of building information systems inefficiently, a multi-layer framework aiming at high performance is designed and implemented for common information system based on .NET. Many design patterns are applied so the framework is scalable, which provides various core services. Practices have proved that multi-pattern systems can be developed efficiently based on the framework. Various methods including cache service are adopted to optimize system performance. Test data have shown that the integrated cache service provided by the framework can improve system performance by decreasing TTFB(Time to First Byte) by 39.9% and decreasing ATCEH(average time cost of effective hits) by 76.1%.
Secure covert channels in multiplayer games
Authors: Malte Diehl
Venue: Proceedings of the 10th ACM workshop on Multimedia and security (2008)

Steganography is the art of hidden communication. In recent years, some publications presented approaches to transmit hidden information for different purposes via covert channels provided by games. These approaches used rather ad-hoc techniques to reach their objectives without establishing notions of steganographic security in their domain and applying them to review their solution. Therefore, this paper both theoretically and practically tackles the question how to implement secure steganography for games. First, we recall the necessary game-theoretic basics from which we derive a notion of steganographic security for game-based methods that depends on strategy imitation. This is followed by a general information-theoretic analysis of games to calculate the total amount of information that is sent during a game and the share that can be used securely for game-theoretic steganography. Second, based on our game-theoretic framework, we present a secure steganographic system for a simplified version of poker that serves as a proof of concept. Its features are evaluated and compared to the theoretical results. Despite its security, the system can replace over 50% of the cover data with steganographic information. Further advantages of our method are that no large cover messages are needed and that it can be used for steganographic streaming.
Simple Algorithms for Predicate Suggestions Using Similarity and Co-occurrence
Authors: Eyal Oren, Sebastian Gerke, Stefan Decker
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments, suggestion mechanisms help terminology convergence without forcing users. We introduce two domain-independent algorithms for recommending predicates (RDF statements) about resources, based on statistical dataset analysis. The first algorithm is based on similarity between resources, the second one is based on co-occurrence of predicates. Experimental evaluation shows very promising results: a high precision with relatively high recall in linear runtime performance.
Classifying Alarms in Intensive Care - Analogy to Hypothesis Testing
Authors: Wiebke Sieben, Ursula Gather
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

Monitoring devices in intensive care units observe a patient's health status and trigger an alarm in critical situations. The alarm rules in commercially available monitoring systems are usually based on simple thresholds set by the clinical staff. Though there are some more advanced alarm rules integrated in modern monitoring devices, even for those, the false alarm rate is very high. Decision trees have proven suitable for alarm classification and false alarm reduction. Random forests which are ensembles of trees can improve the accuracy compared to single trees in many situations. In intensive care, the probability of misclassifying a situation in which an alarm is needed has to be controlled. Subject to this constraint the probability of misclassifying a situation in which no alarm should be given has to be minimized - an analogy to a hypothesis test for testing "situation is alarm relevant" vs. "situation is non alarm relevant" based on an ensemble of trees. This yields a classification rule for any given significance level, which is the probability of misclassifying alarm relevant situations. We apply this procedure to annotated physiological data recorded at an intensive care unit and generate rules for false alarm reduction.
2008 Special Issue: Event detection and localization for small mobile robots using reservoir computing
Authors: E. A. Antonelo, B. Schrauwen, D. Stroobandt
Venue: Neural Networks (2008)

Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.
Hard Java
Authors: Paul Frenger
Venue: ACM SIGPLAN Notices (2008)

Sun Microsystems has been a fantastic repository of talent for years, somewhat reminiscent of Apple Computer and the Xerox PARC (Palo Alto Research Center). Sun developed the SPARC line of CPUs for their Solaris Operating System (a flavor of Unix). This group devised the only-ever hardware- and software-independent computer boot-up system (OpenBoot / Open Firmware / IEEE 1275). They also brought forth Java, a programming language which today seems ubiquitous and indispensable.
An LSI Modular Direct-Execution Computer Organization
Authors: Yaohan Chu
Venue: Computer (1978)

A potentially low-cost architecture offers both device and linguistic modularity and executes high-level code without compilation.
User-centered computing and design in esp: a driving interface for musical expression synthesis
Authors: Elaine Chew, Alexandre R. Francois
Venue:  (2007)

This dissertation is in the area of user-centered computing and design, which is concerned with the usability of human-computer systems. User-centered computing is computing that takes into account human response and reaction. User-centered design accounts for users' needs in the design process. We focus on these aspects of system design and implementation in this dissertation on the Expression Synthesis Project (ESP). ESP provides a driving (pedals, wheel and display) interface for generating expressive performances from expressionless music files. We design the system using the Software Architecture for Immersipresence framework, and implement it using the Modular Flow Scheduling Middleware. The ESP system processes multiple data streams in real-time, including graphics data, Music Instrument Digital Interface, music information, and driving control parameters. In the area of user-centered computing, we design the system to ensure tempo smoothness through road segment boundaries and when hitting road edges. To allow inexperienced users to form smooth tempo trajectories, we employ a virtual radius mapping strategy. Our experiments show that the proposed strategy reduces jerk under aggressive, erratic, and normal driving. To verify that the road in ESP can influence driving performance in desired ways, we consider performances generated from different roads for the same piece. We study two performances each of Brahms' Hungarian Dance Nos. 2 3, and design two roads each based on the different grouping strategies. We show that performances from the same roads are more similar to each other than those from different roads. Next, we focus on user-centered interface design by customizing road design for individual users. We first model how users control the wheel and pedals in response to the car dynamics and road conditions using an adaptive network based on a fuzzy inference system and a decision tree respectively. Then, we solve an optimization problem, using genetic algorithms, to design a customized road for the driver. We validate the approach by testing the process on ten participants. The experiment shows that, using the tailor-designed roads, seven out of ten users create performances that are more similar to the target than when they drove on other roads.
Computational Modeling of Collective Human Behavior: The Example of Financial Markets
Authors: Andy Kirou, Błażej Ruszczycki, Markus Walser, Neil F. Johnson
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

As a result of the increased availability of higher precision spatiotemporal datasets, coupled with the realization that most real-world human systems are complex, a new field of computational modeling is emerging in which the goal is to develop minimal models of collective human behavior which are consistent with the observed real-world dynamics in a wide range of systems. For example, in the field of finance, the fluctuations across a wide range of markets are known to exhibit certain generic stylized facts such as a non-Gaussian `fat-tailed' distribution of price returns. In this paper, we illustrate how such minimal models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the traders form a dynamically evolving social network but lack any decision-making based on global information. We show that a combination of these two models --- in other words, a population of virtual traders with access to both global and local information --- produces results for the price return distribution which are closer to the reported stylized facts. Going further, we believe that this type of model can be applied across a wide range of systems in which collective human activity is observed.
A Survey on Statistical Pattern Feature Extraction
Authors: Shifei Ding, Weikuan Jia, Chunyang Su, Fengxiang Jin, Zhongzhi Shi
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

The goal of statistical pattern feature extraction (SPFE) is `low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. Pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. This paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.